Proefschrift Visser

Registration and Analysis of Surgical C

omplications Bearing the Burden of Broken Butterflies

Annelies Visser

Uitnodiging

Voor het bijwonen van de openbare verdediging van

het proefschrift

Registration and Analysis of

Surgical Complications

Bearing the burden of broken butterflies

vanAnnelies Visser

Op vrijdag 17 april 2015 om 11.00 uur in de aula van

de Universiteit van Amsterdam, Oude Lutherse kerk,

Singel 411 (hoek Spui) te Amsterdam.

Receptie na afloop van de promotie in café Luxembourg,

Spui 24 te Amsterdam.

Annelies VisserWarmondstraat 128 hs

1058KZ Amsterdam06 41319850

ParanimfenMiranda Ekkel

[email protected] Marie van der [email protected]

Registration and Analysis of Surgical Complications

Bearing the Burden of Broken Butterflies

Annelies Visser



Annelies Visser

Registration and Analysis of Surgical Complications Bearing the Burden of Broken ButterfliesPhD Thesis, University of Amsterdam, The Netherlands

Cover “Broken butterflies” 2011, Anne ten Donkelaar, www.aneten.nlLay out Gildeprint – www.gildeprint.nlPrinted by Gildeprint – www.gildeprint.nlISBN 978-94-6108-931-1

Part of the research described in this thesis was financially supported by an unrestricted grant from the AGIS Health Innovation project.Printing of this thesis was kindly supported by: de Afdeling Chirurgie AMC, Chipsoft B.V. and Stichting IVZ.

@2015, A Visser, Amsterdam, The NetherlandsAll rights reserved. No part of this publication may be reproduced or transmitted in any form by any means, electronic or mechanical, including photocopy, recording or any information storage and retrieval system, without written permission of the author.



ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad van doctoraan de Universiteit van Amsterdamop gezag van de Rector Magnificus

prof. dr. D.C. van den Boomten overstaan van een door het College voor Promoties ingestelde commissie,

in het openbaar te verdedigen in de Aula der Universiteitop vrijdag 17 april 2015, te 11.00 uur

door Annelies Vissergeboren te Beverwijk

PROMOTIECOMMISSIE

Promotores Prof. Dr. J.C. Goslings Universiteit van Amsterdam Prof. Dr. D.J. Gouma Universiteit van Amsterdam

Copromotor Dr. D.T. Ubbink Universiteit van Amsterdam

Overige leden Prof. Dr. D.A. Legemate Universiteit van Amsterdam Prof. Dr. M. de Visser Universiteit van Amsterdam Prof. Dr. O.M. van Delden Universiteit van Amsterdam Prof. Dr. J.D. Blankensteijn Vrije Universiteit van Amsterdam Prof. Dr. J.F. Lange Erasmus Universiteit Rotterdam Dr. P.M.N.Y.H. Go St. Antonius Ziekenhuis

Faculteit der Geneeskunde

TABLE OF CONTENTS

Chapter 1 General introduction and outline of the thesis 9

Part I Registration of Surgical ComplicationsChapter 2 Quality of care and analysis of surgical complications 21 Digestive Surgery 2012;29:391-9

Chapter 3 Registration of surgical adverse outcome: a reliability study in a 41 university hospital BMJ Open 2012;2:e000891

Chapter 4 Questionnaire versus telephone follow-up to detect post-discharge 55 complications in surgical patients: Randomized clinical trial World Journal of Surgery 2012;36:2576-83

Chapter 5 Surgeons are overlooking post-discharge complications: 69 A prospective cohort study World Journal of Surgery 2014;38:1019-25

Part II Improvement of Surgical Complication RegistrationChapter 6 Which clinical scenarios do surgeons record as complications? 85 A benchmarking study of seven hospitals Submitted

Chapter 7 Predictors of surgical complications: A systematic review 101 Surgery 2015 Feb 27. pii: S0039-6060(15)00028-8

Chapter 8 Trigger tool versus standardized clinical registry to identify 137 surgical complications Submitted

Chapter 9 Hospital costs of complications after pancreatoduodenectomy 159 HPB 2015; Provisionally accepted

Chapter 10 Summary and future perspectives 177

Chapter 11 Samenvatting en toekomstperspectieven 189

Appendices PhD Portfolio 205 Publications 207 Acknowledgements (Dankwoord) 209 Curriculum vitae 213

‘Thread tensioner’ is missing two wings. With thread and pins the veins of his wings were constructed. Anne ten Donkelaar.

Chapter 1

General Introduction and

Outline of the Thesis

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GENERAL INTRODUCTION

During the history of mankind, medicine attempts to alleviate human suffering. For centuries, doctors pledge loyalty to Hippocrates’ oath, part of which pleads to ‘primum non nocere’ or ‘first do no harm’.1 However, any medical intervention that is intended to cure or alleviate sickness, may also cause harm. Particularly surgical interventions are inadvertently but inherently associated with harm. This starts with the skin incision, which may already lead to various unintended sequelae like wound infection, pain or hematoma. Furthermore in surgical patients harm may be due to for example bleeding, infection, disturbance of the normal healing process, (surgical) damage to anatomical structures, etc. Any harm should preferably be avoided, but some harm may be justified as a calculated risk if it is outweighed by the anticipated positive effect(s) of the intervention. For example, a bowel (colon) resection for cancer may require a re-anastomosis with a certain risk of leakage. Surgeons are obliged to compare the potential harm versus the intended positive results of their interventions and to communicate both aspects with their patients before an invasive diagnostic procedure or a treatment choice is made.

DefinitionsThe definition of harm in surgery still lacks uniformity, thereby confounding the interpretation of surgical performance and quality assessment.2 When observing harm, multiple terms, definitions and interpretations are used (see figure 1). The terms mostly refer to an unwanted event, process or outcome. A ‘near miss’ is an unwanted event without negative effects on the patient’s health (incorrect process). The term ‘event’ or ‘incident’ is used when the unwanted event did affect the patients’ health (incorrect process). An ‘adverse event’ is an unwanted outcome that occurs due to (the neglect of) a medical intervention, that negatively affects the patient’s health as such this requires their medical treatment to be adapted, or that irreparable damage is caused. An ‘adverse event’ suggests that there is a causal relationship between the unwanted outcome and the medical intervention but not whether the intervention was performed correctly. The term ‘complication’ is often interpreted erroneously, in terms of ‘incident’, ‘adverse event’, or ‘calculated risk’.3 Complications contain all unwanted outcomes that occur during or after a medical or surgical intervention, regardless of causal connections with factors, such as medical treatment, surgery, or comorbidities, causing irreparable damage or requiring adaptation of the medical treatment to be adapted.4 Complications, as opposed to adverse events, can be related to the primary disease, comorbidities, patient characteristics. If, in retrospect, certain actions could have prevented the harm, any of these types of harm will be marked as preventable.

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Application of this broad definition of a ‘complication’, combined with data on the consequences for treatment, allows analysis of a wide variety of complications and provides valuable tools for quality improvement.5 Complications refer to unintended and unwanted outcomes and may, after analysis, be used to prevent unintended events.

Figure 1: Global relationship between different terms.

Based on figure from ‘Praktijkboek Patientveiligheid’, Bohn Stafleu van Loghum. 2006; chapter 1, p 10.


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Registration of ComplicationsAfter the landmark publication of ‘To err is human’ of the Institute of Medicine (IOM) in 1999, patient safety became a highly discussed subject.6 This publication underlined that individual care providers should not solely bear the burden of harm inflicted. Complications are rather shortcomings in the healthcare systems. This shifted the subject from a legal perspective to a quality of care perspective. As a consequence, complication registration and reporting systems were considered as a pivotal step to improve the awareness of complications, to reduce their incidence, and to improve quality of care. Several registration and reporting systems have been developed since. The American College of Surgeons uses the National Surgical Quality Improvement Program (ACS-NSQIP) to monitor clinical morbidity and mortality.7 The Society of Thoracic Surgeons in the United States uses a complication registration system since 1989 for cardiothoracic surgery in adults (www.sts.org). In the Netherlands, the National Intensive Care Evaluation (NICE) registry has been developed especially for ICU departments, while surgical complications on general surgical wards are registered in the national surgical complication registration system (LHCR), developed by the Association of Surgeons of the Netherlands (NVvH). Also at the Department of Surgery of the Academic Medical Centre in Amsterdam complications have been documented routinely of all admitted patients from 1993 on, and using the department’s complication registration according to the LHCR system since 2002.4

Improvement InitiativesComplications can lead to unfavourable health outcomes for the patient, requiring a change in therapy or even causing irreversible damage.8 This might result in a prolonged hospital stay and increased costs for the patient, hospital, and society.9-11 Hence, improving the quality of care has become a priority for hospitals, which includes the registration and minimisation of complications.12 Comprehensive and accurate registration of complications and analysis of these data are essential to help surgeons properly inform their patients as part of the shared decision-making process and to provide surgical departments with adequate process information for internal quality control.This thesis addresses how the completeness and efficiency of the current registration system can be improved by investigating the different approaches to improve the registration of complications in surgical patients.

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OUTLINE OF THE THESIS

This thesis contains two parts. The first part focuses on studies regarding the quality and quantity of the complication registration in surgery. The introduction and optimisation of a complication registry may lead to an apparently increased complication rate. This increase does not necessarily mean that the performance of medical care is insufficient, but could be interpreted as a stimulus for further scrutiny of the quality of care, or merely as a sign that these complications are gradually being better reported, as part of a learning curve (also a certain awareness phase).13,14 In Chapter 2 an analysis of the changes in complication rates and types, and their possible causes, in an academic hospital over a six-year period is described. In Chapter 3 a study is performed to assess the completeness of the departments’ complication database as used in our hospital. The completeness is assessed by comparing the database to relevant information from other available resources on complications, such as the medical and nursing files, the discharge letters relevant to that admission period, the complications documented during morning hand-offs, and the complication database.Furthermore, previous studies have suggested that surgeons only record certain complications after discharge.15,16 The extent and impact of this potential under-recording of post-discharge complications is unknown. This is another aspect that underestimates the overall complication rate. If more information were available on all complications occurring after discharge, this would provide a more reliable representation of all surgery-related complications. In Chapter 4 a study is performed to determine which method, a telephone interview or a questionnaire by mail, is the best way to collect post-discharge complications as reported by patients. In Chapter 5 the extent and impact of the potential under-recording of post-discharge complications are determined. For this purpose the patient-reported complications are compared with surgeon-reported complications by the frequency, type, and grade of post-discharge complications.

Part 2 focuses on several initiatives for improvement of the quality of the complication data and the need for reducing complications. The trend to develop national benchmarking data, including those regarding complications in hospitalised surgical patients is growing. The reliability of benchmarking depends on the quality control of these data. Uniform interpretation and registration by the participating surgical departments is required to assemble high-quality data. The study described in Chapter 6 addresses the amount of agreement and potential differences in the application and interpretation of the definition of a complication among the surgeons and the surgical departments of seven Dutch hospitals. Surgical complications occur more frequently, are more often preventable, and their consequences can be more severe than other types of complications. A risk analysis is


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essential to identify those patients at risk of developing complications. In Chapter 7 a systematic review is performed to summarise available factors that may predict surgical complications. These factors are used in Chapter 8 to develop a new trigger tool. A ‘trigger’ can be defined as a specific factor that is derived from the patient’s medical record and is associated with an increased risk of complications, like bodyweight or complexity of the procedure. A ‘trigger tool’ is a set of triggers that identifies patients who are likely to have suffered a complication and thereby indicates which patient records should be checked for complications. The accuracy of the trigger tool is compared with the current standardised clinical registry method during morning handovers.Reducing complications has become an important goal for quality improvement initiatives to optimise patient outcomes and to reduce hospital costs. However, little is known about the cost consequences of complications. In Chapter 9 the possible financial consequences of specific complications occurring after pancreatoduodenectomy, in particular anastomotic leakage, haemorrhage, infection, as well as the severity of the complication are explored.The results of the studies presented in this thesis and consequences for changes in registration and management of complications in the future are summarised and discussed in Chapter 10.

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REFERENCES

1. Sokol DK. ‘First do no harm’ revisited. Following the dictum means balancing moral principles. BMJ. 2013;347;f6426.

2. Dindo D, Clavien PA. What is a surgical complication? World J Surg. 2008;32(6):939-41.

3. Wagner C, Wal van der G. Voor een goed begrip. Medisch Contact. 2005;60:1888-91.

4. Kievit J, Jeekel J, Sanders FBM. Complicaties registreren. Landelijke database voor beter inzicht. Medisch Contact. 1999;54:1363-5.

5. Goslings JC, Gouma DJ. What is a surgical complication? World J Surg. 2008;32:952.

6. Committee on Quality of Health Care in America, Institute of Medicine. To err is human: Building a safer health system. Washington, DC: National Academy Press. 2000.

7. Khuri SF, Henderson WG, Daley J, et al. Successful implementation of the Department of Veterans Affairs’ National Surgical Quality Improvement Program in the private sector: the Patient Safety in Surgery study. Ann Surg. 2008;248:329–36.

8. Kievit J, Krukerink M, Marang-van de Mheen PJ. Surgical adverse outcome reporting as part of the routine clinical care. Qual Saf Health Care. 2010;19:e20.

9. Brennan TA, Leape LL, Laird NM et al. Incidence of adverse events and negligence in hospitalized patients: results of the Harvard Medical Practice Study I. N Engl J Med. 1991;324:370–6.

10. Thomas EJ, Studdert DM, David ML et al. Incidence and types of adverse events and negligent care in Utah and Colorado. Med Care. 2000;38:261–71.

11. Thomas EJ, Studdert DM, Burstin HR et al. Costs of medical injuries in Utah and Colorado. Inquiry 1999;36:255–65.

12. Fan E, Laupacis A, Pronovost PJ, Guyatt GH, et al. How to use an article about quality improvement. JAMA 2010;304:2279–87.

13. Gouma DJ, Obertop H: The registration of complications of medical treatment (in Dutch). Ned Tijdschr Geneeskd 2003;147:1252–5.

14. Veen EJ, Janssen-Heijnen MLG, Leenen et al. The registration of complications in surgery: a learning curve. World J Surg 2005;29:402–9.

15. Kazaure HS, Roman SA, Sosa JA. Association of postdischarge complications with reoperation and mortality in general surgery. Arch Surg 2012;147(11):1000-7.

16. Kaasschieter EG, van Olden GJ. Complicatieregistratie en fractuurbehandeling. Ned Tijdschr Trauma 2007;6:182-5.


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Part 1

REGISTRATION OF SURGICAL COMPLICATIONS

‘24 pieces butterfly’ is built up from different butterfly wings, all the pieces together creating a new butterfly. Anne ten Donkelaar.

Chapter 2

Quality of Care and Analysis of Surgical Complications

Annelies VisserDirk T Ubbink

Anne KS van WijngaardenDirk J Gouma

J Carel Goslings

Digestive Surgery 2012; 29:391-9

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ABSTRACT

Background: During the past years evaluation of quality of care has become an important aspect of transparency of care, and complications is one of these parameters. Therefore, we analysed the complication rate in an academic hospital over a 6-year period.

Methods: During the period 2004–2009, all adult surgical patients admitted to and discharged from the Department of Surgery were selected for this time trend study. The Dutch national surgical complication registry was used in the analysis, which registers according to a three-tiered matrix-like classification system. Yearly changes in complication rates were analysed statistically using the x2 for trend test. Subsequently, multivariable regression analysis was used to find significant independent predictors for sustaining a complication.

Results: The mean complication rate per admission rose significantly from 0.18 in 2004 to 0.30 in 2009 (p < 0.001). The largest increase was observed by the following variables: less severe complications, complex surgical procedures, and ASA classification. Delirium, gastoparesis, and ileus were complications showing the largest increase. Age, male gender, ASA, and surgical complexity were found as independent predictors.

Conclusions: This study showed a significant increase of complications. The increase was mainly due to less severe complications, in particular delirium, ileus, and gastro paresis.


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INTRODUCTION

Improving the quality of care has become a priority for hospitals.1 Quality improvements, through evidence based interventions and changes in clinical behaviour, can lead to improved patient outcome.2 To improve transparency, the Dutch government defined indicators to measure the quality of care hospitals are expected to deliver. In addition, transparency within a specific hospital and department gives insight into the quality of medical care and potential areas for improvement. Recording of complication rates is one of the parameters of quality of care. The number and grade of occurring complications have become increasingly important as a quality indicator of hospital medical services.3

Patients suffer from a substantial number of complications due to medical interventions, and too many of these complications are the result of substandard care.4 Information about complications can be used to increase the quality of health care and to optimize the performance of medical staff. Complications can lead to reduced health outcomes for the patient, causing irreversible damage or requiring a change in therapeutic policy, which can result in prolonged hospital stay and increased cost.5-8 On the other hand, complications can be considered as an inherent risk of any medical, but particularly surgical, intervention.4 Nevertheless, we have a professional, moral, and ethical duty to constantly minimize the number of complications.For this purpose, it is important to register and analyse the incidence, type, and grade of complications.9,10 A reliable and complete registration is the first requirement. In 2004, the Dutch national surgical complication registry (LHCR) was introduced to foster the uniform registration of surgical complications. The LHCR is based on a specific classification system designed to match existing (inter)national classification systems as closely as possible.9-11

A high complication rate does not necessarily mean that the performance of medical care is insufficient.10 Yet, an increase in complication rates could be taken as a signal for further scrutiny of the quality of care or that these complications are just being better reported in time as part of a learning curve.11,12 During the past decade, using the LHCR, our surgeons received the impression that the yearly complication rate in our hospital was increasing, relating specifically to gastrointestinal complications.Hence, the aim of this study was to analyse the (changes in) complication rates during the past years, and determine which factors and types of complications are contributing the most to this change in complication rates.

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PATIENTS AND METHODS

Patients and SettingOver a 6-year period (2004–2009), all adult surgical patients admitted to and discharged from an academic tertiary referral centre of the Department of Surgery, as well as all patients operated by a surgeon from the Department of Surgery were selected for this trend study. The Department of Surgery performs gastrointestinal, vascular, and trauma operations. The complications were registered after each admission rather than per surgical procedure. The Department of Surgery has 20 full-time attending surgeons and 18 surgical residents covering the areas of general, vascular, gastrointestinal, and trauma surgery. The number of attending surgeons did not change during the study period.

National Complication RegistrationThe definition of a complication as used in our hospital was ‘an unintended and unwanted outcome or state occurring during or following medical care that is so harmful to the patients’ health that it requires (adjustment of) treatment or leads to permanent damage.13 This definition, its interpretation, and the method of registration did not change during the study period.The LHCR is a Dutch national surgical complication registration system that has been developed by the Dutch Association of Medical Specialists, based on national and international standards.14 Variables registered in the LHCR comprises patient characteristics, admission characteristics, and complications. Reported complications were automatically encoded in the LHCR according to a three-tiered matrix-like classification system based on: (1) type of pathology (e.g. infection, bleeding); (2) location, divided in region (e.g. thorax, abdomen), organ (e.g. lung, liver), or tissue; and (3) determinants and other information (e.g. medication).11,12

In addition, the LHCR categorizes each complication into four grades: grade 1, temporary health disadvantage recovering without (re)operation; grade 2, recovery after (re)operation; grade 3, (probably) permanent damage or function loss; and grade 4, death. Complications requiring an interventional treatment outside the operating theatre (e.g. percutaneous drainage, angiography) were categorized as non-operative treatment (grade 1) and not as a reoperation (grade 2).The LHCR registers the complexity of the types of surgery performed in seven levels, as provided by the Dutch Surgical Association. Operative procedures were classified by the technical complexity, on a scale from 1 for low complexity to 7 for high complexity procedures.11 For each admission with a surgical intervention, we marked the operation with the highest surgical complexity as the main operation and included all registered complications.


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Local Procedure for Recording ComplicationsThe presence or absence of complications of every discharged patient was recorded on paper during the morning handovers. Complications registered by a data manager were those reported by the surgical residents or attending surgeons. Of all discharged patients who had one or more complications during admission or within 30 days after discharge, the discharge letters were checked. Any missing data were added to the registry. Surgical procedures performed in day-care and minor complications after discharge that did not lead to readmission (e.g. urinary tract infection after discharge treated by the general practitioner) were not included in this registration system.There is a wide variation in the definition of complications, which influence the outcome of studies and the comparison of different institutions. The definition we used is broad.15 The doctor registers every unintended and unwanted outcome during or following medical care without judgment. Minor complications like cancellation of an operation, are also registered as a complication. Current quality of care standards in the Netherlands imply that any cancellation of an operation should be recorded as a (minor) complication.16 A change of the procedure due to metastatic and advance disease was not considered as a complication, but cancellation due to management problems such as logistic problems was considered as a minor complication. Interpretation of the registered complication data is performed later, for example during the complication meetings every 2 months.

Data AnalysisData were transferred from the LHCR into PASW Statistics v. 18 (SPSS Inc., Chicago, Ill., USA) for further analysis. Complication rates were calculated and expressed as means per month as well as per year. Complication percentages were expressed as percentage of admissions per patient with one or more complications and percentage admissions per patient with more than one complication. Patient characteristics were expressed as means or as medians, depending on the normality of their distribution.First, possible differences in complication rates due to gender, age, ASA-classification (defined as the first assessment of the ASA classification during the admission), or surgical intervention were analysed by means of univariable regression analysis.In an attempt to explain the observed gradual increase in complications in time, we made a list of potential predictors of complications: age, gender, type of surgery, and anatomic and systematic classification of operations.17 All of these variables are registered in our reporting system.Chronological changes in complication rates for the various variables were presented graphically using scatter plots. Yearly changes in complication rates were analysed statistically using the x2 for trend test. To narrow down the number of possible variables that could explain the increase in complication rate, we investigated whether and which

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patient characteristics or types of complications showed any changes in complication rates over time. Variables associated with an increase in complication rates of more than 1% in 6 years were analysed with univariable linear regression analysis. Variables associated with a significant increase in complication rates in time were subsequently entered into a multivariable logistic regression model to assess which variables are independent predictors. A p-value <0.05 was considered significant.

RESULTS

Patient CharacteristicsIn the 6-year period of this study, 26126 clinical admissions were registered. The mean age of the admitted patient was 54 years and 56% (14545) of all admitted patients were male. Of the admissions patients, 65.1% (17017) underwent one or more surgical procedures. Gastrointestinal surgery was performed in almost 50% (8054) of all surgery, followed by trauma surgery (21%, 3537) and vascular surgery (12%, 1981). Most of the gastrointestinal procedures were colorectal, gallbladder, pancreas, and oesophagus surgery.The number of admissions with at least one complication as registered in the LHCR was 4174 (16%), i.e. 5 of every 6 admissions passed without complications. The total number of complications recorded during this period was 6900.In 2004, the mean complication rate per admission was 0.18, which rose significantly to 0.30 in 2009 (p<0.001). This means that in 1 of 6 admitted patients, one or more complications will occur. Figure 1 shows the number of complications per admission over time. The ostensible decline in complication rates during the last quarter of 2009 was negated by the early data in 2010, which showed no decline (data not shown). Figure 2 confirms the suspected increase in percentage admissions with one or more complications, rising significantly from 13% in 2004 to 18% in 2009 (p<0.001). The percentage admissions with more than one complication also showed a significant rise from 3% in 2004 to 7% (p<0.001) in 2009 (data not shown).


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Figure 1. Number (mean) of complications per admissions.

Circles indicate the mean number of complications for each month. The drawn line represents the linear regression

Complication Rates: Changes in TimeThe mean age of the admitted patients showed a small but significant increase from almost 53 years in 2004 to 54.2 years in 2009 (p<0.030). In addition, the gender distribution changed slightly, but significantly (p=0.032), over the years (table 1). The percentage of patients undergoing surgery (mean 62%) did not increase over time. Similarly, the mortali-ty rate remained unchanged in the study period (2004: 1.7%; 2009: 1.8%).

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Table 1. Patient characteristics and ASA classifications of patients undergoing surgery during 2004–2009

2004 2009 p-value

Patient characteristics during 2004–2009Admitted patients, n 4435 4052 –

Age (mean), years 52.7 54.2 0.030

Male gender 2440 (55) 2268 (56) 0.032

With surgery 2728 (62) 2516 (62) 0.619

Readmissions 4 (0.1) 166 (4.1) <0.001

Mortality 75 (1.7) 73 (1.8) 0.270

ASA classifications of patients undergoing surgery during 2004– 2009

Available ASA, n 2672 1453 –

ASA 1 1098 (41) 402 (28) <0.001

ASA 2 1012 (38) 724 (50) <0.001

ASA 3 464 (17) 281 (19) 0.988

ASA 4 87 (3.3) 39 (2.7) 0.807

ASA 5 12 (0.4) 6 (0.4) <0.001

Values are given as n (%) unless otherwise indicated

In 2004 most of the patients undergoing surgery were classified as ASA 1 (41%), as opposed to 28% in 2009. Over the study period, the number of patients classified as ASA 2 increased significantly (p<0.001) from 38% to almost 50% in 2009.The complexity of surgical procedures also showed clear changes (table 2). In 2004, almost 50% of the operations belonged to complexity grade 1, 2, or 3, but these decreased in time to 31%. Furthermore, the higher complexity levels 6 and 7 showed a significant increase from 14 to 17% and 14 to 24%, respectively (p<0.001). The changes in type of surgery underscore the increase in complexity (table 2). The more complex gastrointestinal procedures like colorectal, liver, and oesophagus surgery showed a significant increase, while common procedures like hernia, varicosis, and gallbladder surgery showed a significant decrease over time.


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Table 2. Levels of surgical complexity and types of surgery in 2004 and 2009

Surgical complexity 2004 2009 p-value

N 2737 2551

1 531 (19) 255 (10) <0.001

2 444 (16) 254 (10) <0.001

3 319 (12) 252 (10) 0.052

4 455 (17) 464 (19) 0.011

5 237 (8.8) 229 (9.2) 0.832

6 368 (14) 433 (17) <0.001

7 368 (14) 604 (24) <0.001

Missing 16 (0.6) 21 (0.8)

Type1

Colon/rectum 249 (9.2) 299 (12) <0.001

Anal/perianal 202 (7.3) 67 (2.7) <0.001

Gallbladder 194 (7.1) 137 (5.5) 0.001

Hernia 187 (6.8) 93 (3.7) <0.001

Varicosis 102 (3.6) 2 (0.1) <0.001

Oesophagus 73 (2.7) 93 (3.7) <0.001

Kidney/urinary 37 (1.4) 164 (6.6) <0.001

Liver 28 (1.1) 75 (3.0) <0.001

Values are given as n (%). Listed are the types of surgery with the highest surgical complexity per admission.1 Shows only significant changes in type of surgery.

Table 3 shows the number of patients undergoing gastrointestinal surgery, especially the type of surgery that has shown a significant increase during the 6-year period: colon/rectum, oesophagus, and liver surgery.

Table 3. Patients with types of gastrointestinal surgery that show significant changes in 2004–2009

Colon/rectum Anal/perianal Oesophagus Liver

2004 2009 2004 2009 2004 2009 2004 2009

N 249 299 202 97 73 93 28 75

Patients with complication(s), n (%) 75 (30) 94 (31) 2 (1.0) 5 (7.5) 45 (60) 62 (66) 10 (39) 30 (40)

The complication rate (percentage of patients with one or more complications) in these groups ranged between 30 and 66% versus 24% of all patients with a surgical intervention (fig. 2; 2009).

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Figure 2. Percentages of admissions, with or without surgical intervention, in which one or more complications occurred

Types of Complications: Changes in TimeComplications were also analysed in each of the three main classification dimensions. In the dimension ‘pathology’ (type of complication), psychological disturbance was the only complication type that showed a significant increase over time (p<0.001). No important increases in complications in relation to specific locations or determinants were found, except for the locations ‘nervous tract’ and ‘head’. These increases were mainly due to an increase in the incidence of delirium as a specific complication. However, this complication was not registered during the first 2 years of the LHCR registration, while the number of patients with delirium did not increase significantly when analysed over the subsequent 4-year period (2004: 0.07%, 2006: 0.72%, 2009: 0.94% of all admissions complicated by delirium).A significant increase in complication rates was found in the lowest complication grade, rising from a mean of 0.11 complications per admission in 2004 to 0.26 complications per admission in 2009 as opposed to the other grades that hardly showed any changes over time (fig. 3). Also, the percentage of complications with grade 1 showed a significant increase from 2004 (61.7%) to 2009 (72.6%; p<0.001). The percentage of grade 2 showed a significant decrease from 2004 (16.0%) to 2009 (11.2%; p=0.002). Grade 3 also showed a significant decrease from 2004 (7.1%) to 2009 (2.6%; p=0.036).


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Figure 3. Linear regression lines of (changes in) the number (mean) of complications for complication grade levels 1–4 per monthly admissions, 2004–2009

Additional AnalysesBy its nature, the LHCR complication registration could not provide sufficient explanation for the total increase of complications in the investigated time period. The analyses of the database, especially on the pathology (type) and location of complications, showed an increase in the number of deliria, but this increase was not sufficient to explain the total increase. Therefore, we further analysed the complications classified as having the lowest grade level (grade 1), which showed the most substantial increase over time. We regrouped them by clinical signs or symptoms because distinct (subgroups of) complications might have remained unnoticed in the LHCR classification groups (table 4). This revealed that gastrointestinal dysfunction, specifically those related to an ‘ileus’ or ‘gastro paresis’, increased significantly over time. In 2004, less than 2% of the lowest level complications were gastrointestinal dysfunction, compared to almost 14% in 2009.

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Table 4. Classification, based on signs and symptoms, of the most common complications graded as grade 1

Group Specification 2004 2009 p-value1

Complications Grade 1 524 883 –

Respiratory problems pneumonia, respiratory insufficient, aspiration pneumonia, dyspnea, pleura empyema (postoperative), exacerbation COPD, chylothorax (postoperative), hemothorax, cardiogenic asthma, ARDS, aspiration, atelectasis, bronchial infection, pneumothorax

98 (19) 122 (14) 0.062

Wound infections superficial, arm, leg, abdomen, etc. 49 (9.7) 95 (11) 0.612

Gastrointestinal morbidity ileus, gastro paresis, paralytic ileus, ileus of small intestine, stomach retention

8 (1.4) 79 (8.9) 0.021

Urinary tract infection 35 (6.3) 33 (3.6) 0.009

Abscess intra-abdominal, wound abscess 34 (7.5) 50 (5.7) 0.120

Delirium 3 (0.4) 38 (4.3) <0.001

Atrial fibrillation1 23 (4.1) 32 (3.6) –

Anastomotic leakage oesophagus, small intestine, colon, rectum

20 (3.6) 23 (2.6) 0.164

Bleeding/hematoma 21 (3.9) 21 (2.3) 0.285

Cardiac morbidity cardiac failure, myocardial infarction, angina pectoris, tachycardia, resuscitation

17 (3.3) 19 (2.2) 0.170

Values are given as n (%).1 p-values calculated only in case of an increase or decrease of more than 1% during the study period.

Predictors of ComplicationsMultivariable analysis indicated that age, gender, ASA classification, and complexity of surgery were significant independent predictors for sustaining a complication (table 5). Male patients had 22% more chance of contracting a complication than female patients. In addition, the complication risk rose 2% with every year of age. If the ASA classification at admission was increasing over time with every grade or the surgical procedure was more complex (with every level), the patient had a 43 and 38% higher risk of contracting a complication, respectively.


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Table 5. Independent predictors of the occurrence of complications

Variable OR (95% CI) p-value

Age 1.020 (1.017–1.023) <0.001

Female gender 0.779 (0.713–0.852) <0.001

ASA (at admission) 1.429 (1.353–1.510) <0.001

Surgical complexity 1.379 (1.347–1.412) <0.001

If the variable increased 1 point, the patient had a higher risk of having a complication.

DISCUSSION

The present study showed a significant increase of complications during our study period (2006–2009). The increase was shown in the mean complication rate per admission of patients, the percentage admissions of patients with one or more complications and the percentage admissions with more than one complication. Thus, not only the risk of having a complication rose, but also the risk of developing more than one complication.The actual increase in complication rates was partly due to better registration of specific complications like ileus and delirium as well as a true increase due to a change in case-mix (based on increase of the first ASA and complexity of surgery). Regarding the improved registration, others also reported a dramatic increase in the total numbers of registered complications due to a new registration method.12 In our study, the increase in number of complications appeared to be caused by relatively minor complications requiring non-operative treatment, especially delirium and gastrointestinal dysfunction. This finding is in accordance with the literature, which indicates that the implementation of a new registration system is often combined with an increase of relatively mild complications.11

The availability of classification systems, involvement of information technology, and a low threshold for registering complications are likely to be responsible for the increase in complication rate found during the first 2 years in the present study. This can be explained by the fact that two of the three complications with a significant increase in the study period from 2004 did not show a significant increase when the first 2 years were excluded. A further stimulus for better awareness, identification, and recording of complications are regular clinical discussions of surgical patients and their complications. In our hospital, every 2 months the actual number of complications is presented and discussed with the attending surgeons and surgical residents of the Department of Surgery. The most important aspect of this meeting is to define quality improvement initiatives.The increase of particular complications might be related to the increased complexity of the patients treated, which is in accordance with the focus on secondary and tertiary surgical care of our university hospital. In recent years, we performed more complex surgical procedures, and more patients had a higher ASA at admission. This is illustrated

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by the increased number of patients with an ASA 2 classification, which was also shown to be an independent predictor of the risk of postoperative complications. Also, previous studies showed an increased risk of complications is mostly associated with ASA 3 and 4 patients.18

The types of surgery with a high complication rate showed a significant rise during our study period. The complication percentages of these types of surgery did not show a change, but we operated on more patients with a high risk of developing a complication (table 4). Hence, the shift in types of surgery is most likely related to the complication rate. These findings represent the increasing top-referral function of our university hospital over the years. Nevertheless, the quality of care did not appear to be at stake as we did not find an increased frequency of more severe complications, nor did mortality increase.

DeliriumThe underreporting of delirium as a complication is likely to be due to initially missed diagnoses. To overcome this underreporting, awareness and knowledge of sometimes difficult ‘nonsurgical’ diagnoses have to be improved. Several preoperative variables are associated with an increased risk of delirium, like age, history of depression, prolonged intubation, length of intensive care/hospital stay, and complexity of the operation.19,20 The academic patient population has become older and more complex, and may therefore have contributed to the increase of delirium over the years. Another aspect might be that delirium, which could have been expected in this group of patients, was not always registered as a complication. This omission has led to a dedicated education program for both doctors and nurses in the Department of Surgery, in close collaboration with the Department of Geriatrics, about the early diagnosis and treatment of delirium.19 Thus, the protocolized recognition, management, and, most importantly, prevention of delirium have become more familiar.

Gastrointestinal DysfunctionThe increase in relatively minor gastrointestinal complications is predominantly caused by the increase in the number of patients with ileus or gastro paresis, requiring non-operative treatment.The risk of developing an ileus and its duration likely correlates with type of anaesthesia, surgery time, blood loss, and use and dosage of opiates.21 The assumption that patients undergoing minimal invasive surgery and laparoscopic surgery would have less risk of developing postoperative ileus than after open procedures has not been substantiated by earlier studies.22,23 The recent use of Fast Track programs and clear definitions of GDE (gastric delayed emptying) in HPB surgery might also be partly responsible for the increased reporting of such complications.24


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In order to decrease and prevent these complications, further research is necessary on a possible change in patient population, the type and complexity of operations performed, and the use of narcotic analgesics.

LimitationsBased on the results of the study, it is difficult to prove whether the increase in complications was a true increase or the result of better reporting. Firstly, we could not study all potentially causative factors we conceived initially such as BMI, alcohol, diabetes mellitus, smoking, hypertension, comorbidity, acute versus elective care, first versus second opinion, and admission diagnosis.17 This is due to the complex linkage or integration of databases, as well as the incomplete registration of data. These other factors may play an important, but still unknown, role in the occurrence or increase in complications. Hence, this needs further research.Second, the regrouping of complications with grade 1 (non-operative treatment) by clinical signs or symptoms was not based on the master classification, but on the description of the most common complications.Third, we assumed that the definitions of the complications did not change over time, but we did not check this assumption. This especially holds for the decision whether and when ileus and gastro paresis must be reported as a complication. This might have contributed to the increase over the years.

Quality of CareThe ultimate goal of good complication registration is to improve quality of care. Interpretation of the registered complication data is performed, for example, during our complication meetings every 2 months. These complication meetings have led to various improvement actions, logistic changes, or changes in the medical protocols.Minor complications, like cancellation of an operation, are also registered as a complication. Current quality of care standards in the Netherlands imply that any cancellation of an operation should be recorded as a (minor) complication.16 We defined a new policy that should result in less cancelation and if a cancelation is inevitable the new policy should lead to a patient-friendly approach and solution. Examples of changes in medical policy are an adjustment to the protocol of bleeding after pancreas surgery. Now, an early bleeding will be treated differently (relaparotomy) than a late bleeding (angiography). Another example is a change in the protocol regarding the treatment of body packers. To avoid missing packets, these patients will always undergo CT scanning.Sometimes a complication meeting can lead to further research, for example ‘Morbidity related to defunctioning ileostomy closure after ileal pouch-anal anastomosis and low colonic anastomosis’.25 These actions were taken in 2010-2012. A change in policy may take several years before the effects become clear.

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CONCLUSION

The complication registration based on the LHCR has enabled us to detect specific complications that have contributed to the increase in complication rate over the 6-year period, specifically gastrointestinal complications like gastro paresis and ileus. This study shows that registration of complications offers a valuable source of information to detect significant changes in complication rates that foster subsequent surgical quality improvement actions, which may ultimately lead to improved quality of patient care.


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REFERENCES

1 Fan E, Laupacis A, Pronovost PJ, et al. How to use an article about quality improvement. JAMA 2010;304:2279– 87.

2 Nicolay CR, Purkayastha S, Greenhalg A, et al. Systematic review of the application of quality improvement methodologies from the manufacturing industry to surgical healthcare. Br J Surg 2012;99:335–42.

3 Roukema JA, van der Werken CH, Leenen LP. Registration of postoperative complications to improve the results of surgery. Ned Tijdschr Geneeskd 1996;140:781–4.

4 Brennan TA, Leape LL, Laird NM, et al. Incidence of adverse events and negligence in hospitalized patients. Results of the Harvard Medical Practice Study I. N Engl J Med 1991;324:370–6.

5 Thomas EJ, Studdert DM, Burstin HR, et al. Incidence and types of adverse events and negligent care in Utah and Colorado. Med Care 2000;38:261–71.

6 Thomas EJ, Studdert DM, Newhouse PJ, et al. Costs of medical injuries in Utah and Colorado. Inquiry 1999;36:255–65.

7 Olsen MA, Chu-Ongsakul S, Brandt KE, et al. Hospital-associated costs due to surgical site infection after breast surgery. Arch Surg 2008;143: 53–60, discussion 61.

8 Koperna T. Cost-effectiveness of defunctioning stomas in low anterior resections for rectal cancer: a call for benchmarking. Arch Surg 2003;138:1334–8, discussion 1339.

9 Marang-van de Mheen PJ, Kievit J. Automated registration of adverse events in surgical patients in the Netherlands the current status (in Dutch). Ned Tijdschr Geneeskd 2003;147:1273–7.

10 Gouma DJ, Obertop H. The registration of complications of medical treatment (in Dutch). Ned Tijdschr Geneeskd 2003;147:1252–5.

11 Kievit J, Krukerink M, Marang-van de Mheen PJ. Report surgical adverse

outcomes alongside routine clinical care. Qual Saf Health Care 2010;19:1–5.

12 Veen EJ, Janssen-Heijnen MLG, Leenen LPH, et al. The registration of complications in surgery: a learning curve. World J Surg 2005;29:402–9.

13 Kievit J, Jeekel J, Sanders FBM. Adverse outcome registration and quality improvement. Med Contact 1999;54:363–1365.

14 Marang-van de Mheen PJ, van Hanegem N, et al. Effectiveness of routine reporting to identify minor and serious adverse outcomes in surgical patients. Qual Saf Health Care 2005;14:378–82.

15 Goslings JC, Gouma DJ. What is a surgical complication? World J Surg 2008;32:952.

16 Clavien PA, Sanabria JR, Strasberg SM. Proposed classifications of complications of surgery with examples of utility in cholecystectomy. Surgery 1992;11:518–26.

17 Veltkamp DC, Kemmeren LM, van der Graaf Y, et al. Prediction of serious complications in patients admitted to a surgical ward. Br J Surg 2002;89: 94–102.

18 Wolters U, Wolf T, Stutzer H, et al. ASA classification and perioperative variables as predictors of postoperative outcome. Brit J Anaesthesia 1996;77:217–22.

19 Robinson TN, Raeburn CD, Tran ZV, et al. Postoperative delirium in the elderly: risk factors and outcomes. Ann Surg 2009;249:173–8.

20 Kazmierski J, Kowman M, Banach M, et al. Incidence and predictors of delirium after cardiac surgery: results from the IPDACS study. J Psychosom Res 2010;69:179–85.

21 Johnson MD, Walsh RW. Current therapies to shorten postoperative ileus. Cleveland Clin J Med 2009;276:641–8.

22 Holte K, Kehlet H. Prevention of postoperative ileus. Minerva Anesthesiol 2002;68:152–6.

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23 Story SK, Chamberlain RS. A comprehensive review of evidence-based strategies to prevent and treat postoperative ileus. Dig Surg 2009;26:265–75.

24 Vlug MS, Wind J, van der Zaag E, et al. Systematic review of laparoscopic vs open colonic surgery within an enhanced recovery programme. Colorectal Dis 2009;11:335–43.

25 van Westreenen HL, Visser A, Tanis PJ, et al. Morbidity related to defunctioning ileostomy closure after ileal pouchanal anastomosis and low colonic anastomosis. Int J Colorectal Dis 2012;27:49–54.


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‘Black spot butterfly’ was missing two top wings, the new wings were made by embroidering the black spots patterns on fabric. Anne ten Donkelaar.

Chapter 3

Registration of Surgical Adverse outcome:

a Reliability Study in a University Hospital

Dirk T UbbinkAnnelies VisserDirk J Gouma

J Carel Goslings

BMJ Open 2012;2:e000891

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ABSTRACT

Background: Accurate registration of adverse surgical outcome is essential to detect areas for improvement of surgical care quality. One reason for inaccurate adverse outcome registration may be the method to collect these outcomes. The authors compared the completeness of the national complication registry database (LHCR) as used in our hospital with relevant information from other available resources.

Methods: From the 3252 patients admitted to the surgical wards in 2010, the authors randomly selected a cohort of 180 cases, oversampling those with adverse outcomes. The LHCR contains adverse outcomes as reported during morning hand-offs or in discharge letters. The authors checked if the number and severity grade of adverse outcomes recorded in the LHCR agreed with those reported in morning hand offs, discharge letters and medical and nursing files.

Results: In 135 of 180 patients, all resources could be retrieved completely. Fourteen per cent of the patients with complications were not recorded in the LHCR. Missing adverse outcomes were all reversible without the need for (re)operation, for example, postoperative pain, delirium or urinary tract complications. Only 38% of these complications were reported in the morning hand-offs and discharge letters but were best reported in the medical and nursing files.

Conclusions: Registration of surgical adverse outcomes appears largely depending on the reliability of the underlying sources. For a more complete complication registration, the authors advocate a better hand-off and additional consultation of the patient’s dossier. This extra effort allows for improvement actions to eventually avoid ‘mild’ complications patients perceive as important and undesirable.


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INTRODUCTION

Of the patients admitted to a clinical department of surgery, approximately 10% is at risk of having a treatment-related complication and for some extensive gastrointestinal procedures even up to 50%.1 A substantial part of these complications is preventable and thus epitomises suboptimal care.2 Accurate and routine registration of these adverse outcomes is an important starting point from which to take action,3-5 in order to reduce or even prevent these events and lower hospital mortality due to diminishing flaws in the care system.6 Hence, professional societies and governmental institutions have urged to accurately record postoperative complications and to use this as a quality indicator. In the Netherlands, the Dutch Society of Surgeons already introduced a national surgical complication registry (LHCR) for this purpose in 2003.7,8 However, information is needed on the performance of hospitals’ adverse outcome reporting systems.9 Inaccurate registration and thus under-reporting of adverse outcomes, as shown in previous studies,10,11 seems to be rewarded with an erroneously high score for quality of care. A reason for an inaccurate registration of adverse outcomes could be the method chosen to collect and record these outcomes. The events entered into the registration database may be as complete as the resources from which these events are drawn. These resources can be daily verbal hand-offs, regular (multidisciplinary) meetings, medical and nursing dossiers or the discharge letter. A previous comparison between daily reported adverse outcomes with those documented in medical dossiers showed considerable discrepancy.11 Hence, even a uniform structural complication registration may have flaws to be improved. However, the effort to achieve a (nearly) complete registration should be weighed against its surplus value.The aim of this study was to assess the accuracy of the surgical complication registration database we are using routinely and a comparison with the source documents in order to detect areas for improvement of the adverse outcome registration in clinical surgical care.


PatientsThis survey was undertaken in the Department of Surgery of a tertiary referral university hospital in Amsterdam. From the admissions to any of the surgical wards during the year 2010, we randomly selected a sample of 180 patients (5.5%) from the LHCR database by means of a random number generator, while ensuring that at least half of the patients had suffered at least one adverse outcome according to the LHCR information. This was achieved by sampling half of the patients from the LHCR after selecting those in whom at least one adverse outcome had been recorded. Thus, we ensured a sufficient number of admissions with adverse outcomes to analyse. Patients admitted more than once during

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that year were included only once. We excluded patients whose resources could not be retrieved completely. The resulting sample was considered valid because we compared the various resources rather than the true incidence of adverse outcomes.The definition of a surgical adverse outcome used in this study was ‘an unintended and unwanted outcome or state occurring during or following medical care that is so harmful to the patients’ health that it requires (adjustment of) treatment or leads to permanent damage’, according to the Dutch Society of Surgery.12 These could include adverse outcomes due to medical management errors,13 as defined in the WHO reporting guidelines, but the recording of events took place before a conclusion regarding its causality (i.e., medical management error or disease complication) could be given. The definition, its interpretation and the method of registration did not change during the study period. Patients without adverse outcomes according to the LHCR were used to check whether the absence of events was in agreement with the other resources. The patient set with adverse outcomes was used to check whether the events as recorded in the LHCR were complete when compared with the other resources.

ResourcesFor each patient included, we retrieved and studied the medical and nursing files, the discharge letters relevant to that admission period, the documented morning hand-offs and the complication database (LHCR). Adverse outcomes entered into the LHCR were derived from the daily surgical morning hand-offs and the discharge letters. During these hand-offs, every discharged patient was reported. Adverse outcomes documented were those reported by the surgical residents or attending surgeons.12 The discharge letters were screened to find any additional events. The content of the discharge letters used during the study period was predefined in a local protocol, in which the reporting of adverse outcomes that had occurred during the patient’s admission was compulsory.As reference standard for the true number and type of adverse outcomes occurring during the hospital admission period of each patient, we used the combination of all resources consulted, that is, LHCR, morning handoff, discharge letter, medical file and nursing file. The discharge letter, medical and nursing files were judged separately within the patient’s dossier as they were being kept separately and produced by different caregivers. At the time of the study, the medical and nursing files were not yet digitalised but contained daily reports of the patient’s condition and well-being.

Study ProcedureFrom each of the resources, except the morning handoffs, two investigators independently extracted the documented adverse outcomes that had occurred in the selected patients and entered these in a database. In case of uncertainties interpreting the texts of the resources, the investigators consulted each other or their supervisors.


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The various types of adverse outcomes were first categorised based on the national classification as used by the Dutch Surgical Society. Because these categories in our sample were too fragmented, we regrouped the events by similarity (type) and number of appearance (figure 1). The grading of the severity of each event was based on the classification of Clavien et al14 and was divided into four classes: (1) temporary health disadvantage recovering without (re)operation, for example, wound infection; (2) recovery after (re)operation, for example, anastomotic leakage; (3) (probably) permanent damage or function loss, for example, stroke; and (4) death during admission. In retrospect, we also categorised recorded events that had no adverse health effects, for example, a cancelled operation, as ‘class 0’.

Data AnalysisData were transferred from the various resources into Excel 2003 (Microsoft Corp., Seattle, Washington, USA) for further analysis. Descriptive statistics were expressed as means including SDs or medians with IQRs, whenever appropriate. Agreements between the adverse outcomes recorded in the LHCR and in other resources were expressed as percentages. Similarly, we calculated the agreements for each event severity group.

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Which are the best resources for registration of surgical adverse outcomes?

Figure 1. Categories used to group the recorded adverse outcomes

1: Abscess 13: Fluid collections• Seroma

2: Surgical procedure cancelled 14: Pain• Correction of epidural analgesia

3: Cardiac complications• Atrial or ventricular tachycardia• Brady/tachycardia• Asthma of cardiac origin• Myocardial infarction• Heart failure• Arrhythmias

15: pulmonary complications• Pneumothorax• Respiratory Insufficiency• Atelectasis• Respiratory depression

4: Pneumonia 16: Over-infusion5: Bleeding

• Aneurysm• Hematoma• Dissection

17: Wound or fascia dehiscence

6: Shock• Hemodynamic instability

18: Thrombosis

7: Anastomotic dehiscence 19: (Wound) Infection• Sepsis• Poor wound healing• Wound infection

8: Miscellaneous leakages• Chylus• Gall• Wound

20: Bladder complications• Retention• Urinary tract infection• Urethritis

9: Pressure ulcer 21: Fistula

10: Delirium 22: Vascular complications• Phlebitis• Cellulitis

11: Electrolyte derailment• Anaemia• Hyperglycaemia• INR derailment

23: Cerebral complications• CVA• Infarction• Neuropraxia• Neural compression

12: Gut complications• Gastro paresis• Ileus• Derailed stoma output• Ischemia of sigmoid

24: Other complications• Kidney infarction• Allergy• Ascites• Contractures• Disturbed liver function• Paresis• Wrong K-wire• Secondary dislocation• Rhabdomyolysis• Addison’s crisis• Hernia• Temporary hoarseness• Small intestinal perforation• Shunt occlusion


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RESULTS

During 2010, a total of 4196 admissions (of 3252 patients) to the gastrointestinal, vascular or trauma surgery wards were recorded. In 705 (16.8%) of these, one or more adverse outcomes were documented in the LHCR. Of the 180 selected admissions, the resources of 135 different patients admitted could be analysed. Forty-five admissions were excluded because these concerned readmissions of the same patients (n=3) or the data from one of the resources could not be retrieved (n=42). These reasons for exclusion were not likely to be related to the completeness of the adverse outcomes as stated in the various resources. Hence, we considered the remaining set of 135 patients as valid for our purpose. Of the 135 patients included, 60.7% were men, with a mean age of 59.3 years. Median length of stay was 8 days. As shown in table 1, their characteristics did not differ significantly from the whole group of patients admitted in 2010, except for a significantly longer length of stay and higher number of American Society of Anaesthesiologists (ASA)-2 patients, obviously because we oversampled patients with adverse outcomes. In 70% of the 135 patients, one or more surgical procedures were performed, resulting in a total of 208 procedures in these patients. Based on the summary of all events from all resources, 275 adverse outcomes were recorded in total. A total of 98 of 135 patients had suffered one or more adverse outcomes.

Table 1. Characteristics of the 135 selected patients versus all patients admitted in 2010

Characteristic Included patients N=135

Patients admitted in 2010 N=3252

Male (%) 82 (60.7%) 1808 (55.6%)

Age (years):Mean (SD1)Median (IQR2)

59.3 (17.0)62.0 (47.5-71.8)

55.1 (18.0)57.6 (42.7-68.1)

Length of stay (days):Mean (SD1)Median (IQR2)

14.9 (27.6)8.0 (3.0–16.5)

7.9 (13.9)4.0 (1.0-9.0)

Underwent surgery, n (%):General (%)Oesophago-gastro-intestinal (%)Hepato-pancreato-biliary (%)Trauma (%)Vascular (%)

104 (70)22.434.514.311.317.5

2276 (70)26.230.4 8.920.613.9

ASA-classification*: (%)12345

6.872.713.6 6.8 0.0

24.245.626.0 3.3 1.0

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The proportions of patients with one or more adverse outcomes as recorded in the different resources as well as in the official LHCR are summarised in table 2. In 86% of the cases, the LHCR was in agreement with the reference standard as to the total number of patients with one or more events. In other words, 14% of admissions with adverse outcomes were not recorded in our official registry. Table 3 shows the severity grade categorisation of the events as recorded in the various resources. Virtually all events missing in the LHCR were mild (grade 1) events that could be treated with nonsurgical interventions, including pain, delirium and bladder complications. The six missing grade-2 events were categorised as haemodynamic instability (n=2), wound abscess (n=1), gastro paresis (n=1) and miscellaneous complications (n=2). The one grade-3 complication missed was a pressure ulcer.

Table 2. Percentages and absolute numbers of patients with one or more adverse outcomes as recorded in each resource compared with the reference standard

Resource Adverse outcome(s)

Reference standard 100% (98)

LHCR* 86% (84)

Morning hand-offs 80% (78)

Discharge letter 78% (76)

Medical file 78% (76)

Nursing file 77% (75)

*According to the Dutch national surgical complication registry.

Table 3. Percentages and absolute numbers of adverse outcome as recorded in each resource, categorised per severity gradeSeverity grade*

Reference standard

LHCR † Morning hand-offs

Discharge letter

Medical file Nursing file

0 100% (11) 73% (8) 9% (1) 18% (2) 55% (6) 64% (7)

1 100% (221) 44% (97) 32% (71) 38% (85) 60% (132) 67% (148)

2 100% (31) 81% (25) 66% (20) 77% (24) 68% (21) 58% (18)

3 100% (9) 89% (8) 56% (5) 22% (2) 0% (0) 0% (0)

4 100% (3) 100% (3) 100% (3) 67% (2) 67% (2) 33% (1)

Total 100% (275) 51% (141) 36% (100) 42% (115) 59% (161) 63% (174)

*Severity grade: 0, event without adverse effect on health; 1, temporary health disadvantage recovering without (re)operation; 2, recovery after (re)operation; 3, (probably) permanent damage or function loss; 4, death. †According to the Dutch national surgical complication registry.


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Adverse outcomes related to medical management errors (‘grade 0’) occurred rarely but were poorly registered, particularly in the morning hand-offs and discharge letters. This is mainly due to the fact that these events mainly concerned ‘cancelled operations’. Although these were recorded during the morning hand-offs, they were considered to be of limited information to include in the discharge lettersThe vast majority (80.4%) of the adverse outcomes in the reference standard was reversible and mild (grade 1). The morning hand-offs and discharge letters omitted most of these events. Only 38% of these mild events were registered in these resources. Also the LHCR missed most of the mild events, which were best reported in the nursing and medical files.Surgical complications requiring a re-intervention (grade 2) seemed to be under-recorded in most resources. However, this may be influenced by the fact that in some patients more than one grade-2 event had led to a single re-intervention, but only one of these events was recorded as reason for the re-intervention. Unfortunately, there was no consensus on how this should have been recorded.The more serious adverse outcomes (grades 3 and 4) occurred less frequently. The medical and nursing files did not state grade-3 events, probably because at that time the permanent effects of such events could not yet be assessed. Strikingly, even the discharge letter did not mention many of the events leading to permanent damage or function loss. A few deaths were not documented in the nursing and medical files because the patient died on the intensive care unit, which was documented in a separate discharge letter not included in this study.To discover which types of adverse outcomes in particular might be under-recorded, we investigated which events had a documentation rate of less than 50% compared with the reference standard. The events are listed as following: abscess, shock, pressure ulcers, delirium, fluid collections, pain, pulmonary complications, over infusion, urinary tract-related complications, fistulas and vascular complications (i.e., phlebitis or dialysis shunt occlusion).

DISCUSSION

Registration of surgical adverse outcomes appears valuable but is largely depending on the reliability of the underlying sources. In many hospitals, a complication registration system, such as the LHCR in the Netherlands, heavily depends on the accuracy of the reporting and documentation of adverse outcomes through various resources. The usage of the available resources might be different in the various Dutch hospitals using the LHCR and is not well defined.The present study showed that adverse outcomes are under-reported with the LHCR according to LHCR system and also during the morning report. The less severe events

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tended to be reported less frequently, except in the nursing file, which was not designed to serve as input for the LHCR. Nevertheless, all likely resources should be incorporated for an optimum registration of adverse outcomes. The medical rather than the nursing file seems the most appropriate additional resource for this purpose.One of the reasons for under-reporting might be a reluctance or negligence among doctors to report adverse outcomes. Particularly strong disincentives for reporting are shame, fear of liability, loss of reputation and peer disapproval.15 The awareness that medical errors, and also surgical complications, are frequently system errors rather than an individual liability has helped abandoning a shame-and-blame culture and has harnessed the medical professional to report errors and adverse outcomes.16 Furthermore, increasing societal demands as to safety and transparency in healthcare have created more awareness of the importance of, and willingness to contribute to, and a better quality of care.17 18 The completeness of the complication registration may also vary with the types of adverse outcomes a hospital decides to record. Should adverse outcomes with a low severity grade, for example, such as delay of surgery, be omitted, that is, a ‘light’ version of complication registration, a higher accuracy would be achievable. A drawback of this would be that other, but common, events, such as wound infections or pressure ulcers, are not monitored properly and cannot be acted upon.Moreover, particularly for relatively minor surgical interventions, patients will still perceive ‘mild’ adverse outcomes as important and undesirable.Conversely, registration of all possible adverse outcomes requires more effort to extract these from the various complementary resources. When pursuing this policy, the nursing file may be included as an important source of more ‘mild’ events, such as pressure ulcers, insufficiently controlled pain or urinary tract infections. Besides, recording the number of postponed or cancelled surgical interventions can be useful as indicator for a change in the organisation process of care and thereby an improvement of the quality of care. The low number of adverse outcomes included in the discharge letter may be due to selection of items considered relevant to the general practitioner or follow-up institution. However, any permanent damage or function loss acquired during admission surely needs more attention than it appears to receive, based on this study, in particular in the early phase after discharge and management of the adverse outcomes by the general practitioner. A predefined format and content of these letters, for example, a computer-generated summary, can improve quality and safety of hand-off communication and subsequent care.19 A limitation of this study could be that even the reference standard may have been an underestimation of the true number of adverse outcomes that had actually occurred. If so, the various sources leave even more events untracked. However, this does not seem likely, as all possible sources were studied in retrospect. We did not study the events that might have occurred (shortly) after discharge, which was beyond the scope


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of this research. Second, the random sample of admissions investigated may have been relatively small. Nevertheless, the trends we found are quite conspicuous and seem reliable since two investigators independently reviewed the resources for events. In conclusion, the registration, management and prevention of surgical adverse outcomes are not to be neglected in daily clinical practice. It may also impact the selection of patients to be treated and procedures to be performed. Therefore, hospitals and clinicians should be willing to put effort in a structural and reliable means to register not only the beneficial but also the harmful effects of their professional activities or clinical management to improve the quality of care for their patients.

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REFERENCES

1 Obertop H, Gouma DJ. Complications in surgery let’s face them. Dig Surg 2002;19:83-5.

2 Brennan TA, Leape LL, Laird NM, et al; Harvard Medical Practice Study I. Incidence of adverse events and negligence in hospitalized patients: results of the Harvard Medical Practice Study I. 1991. Qual Saf Health Care 2004;13:145-52.

3 Bruce J, Russell EM, Mollison J, et al. The measurement and monitoring of surgical adverse events. Health Technol Assess 2001;5:1-194.

4 Vrancken Peeters MP, Vrancken Peeters MJ, Corion LU, et al. Quality control of colorectal surgery with an extensive complication registration system. Dig Surg 2005;22:168-73.

5 Kievit J, Krukerink M, Marang-van de Mheen PJ. Surgical adverse outcome reporting as part of routine clinical care. Qual Saf Health Care 2010;19:e20.

6 Ghaferi AA, Birkmeyer JD, Dimick JB. Variation in hospital mortality associated with inpatient surgery. New Engl J Med 2009;361:1368-75.

7 Gouma DJ, Obertop H. The registration of complications of medical treatment. Ned Tijdschr Geneeskd 2003;147:1252-5.

8 Veen EJ, Janssen-Heijnen ML, Leenen LP, et al. The registration of complications in surgery: a learning curve. World J Surg 2005;29:402-9.

9 Farley DO, Haviland A, Haas A, et al. How event reporting by US hospitals has changed from 2005 to 2009. BMJ Qual Saf 2012;21:70-7.

10 O’Neil AC, Petersen LA, Cook EF, et al. Physician reporting compared with medical-record review to identify adverse medical events. Ann Intern Med 1993;119:370-6.

11 Marang-van de Mheen PJ, van Hanegem N, Kievit J. Effectiveness of routine reporting to identify minor and serious adverse outcomes in surgical patients. Qual Saf Health Care 2005;14:378-82.

12 Goslings JC, Gouma DJ. What is a surgical complication? World J Surg 2008;32:952.

13 Hiatt HH, Barnes BA, Brennan TA, et al. A study of medical injury and medical malpractice. An overview. New Engl J Med 1989;321:480-4.

14 Clavien PA, Barkun J, de Oliveira ML, et al. The Clavien-Dindo classification of surgical complications: five-year experience. Ann Surg 2009;250:187-96.

15 Leape LL. Reporting of adverse events. New Eng J Med 2002;347:1633-8.

16 Collins ME, Block SD, Arnold RM, et al. On the prospects for a blamefree medical culture. Soc Sci Med 2009;69:1287-90.

17 Khatri N, Brown GD, Hicks LL. From a blame culture to a just culture in health care. Health Care Manage Rev 2009;34:312-22.

18 Wolff AP, Boermeester M, Janssen I, et al. The national Dutch Institute for Healthcare Improvement guidelines ‘Preoperative trajectory’: the essentials (In Dutch). Ned Tijdschr Geneeskd 2010;154:A2184.

19 Kripalani S, LeFevre F, Phillips CO, et al. Deficits in communication and information transfer between hospital-based and primary care physicians: implications for patient safety and continuity of care. JAMA 2007;297:831-41.


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‘Twig-fly’ has a body made of a twig and wings made from leaves. Anne ten Donkelaar

Chapter 4

Questionnaire versus Telephone Follow-up to Detect

Post-discharge Complications in Surgical Patients:

Randomized Clinical Trial

Annelies VisserDirk T UbbinkDirk J Gouma

J Carel Goslings

World Journal of Surgery 2012; 36:2576-83

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ABSTRACT

Background: Post-discharge complications in surgical patients are usually recorded only when readmission is required, a method that likely underestimates the overall complication rate. Our aim was to determine which method, telephone interview or questionnaire by mail, collects the most post-discharge complications.

Methods: We performed a randomized clinical equivalence trial. From December 2008 until August 2009, all adult surgical patients admitted to a university hospital were randomized to be approached by mail or by phone 30 days after discharge to collect information about post-discharge complications. Primary outcome was the total number of reported complications after discharge. Secondary outcome was the severity of the complications.

Results: In all, 1595 patients were reached: 890 by means of a telephone interview and 705 through a questionnaire. Response rate was higher in the telephone group than in the questionnaire group (63.8% vs. 51.3%). The percentage of patients reporting one or more complications did not differ significantly between the groups: 43.3% in the telephone group versus 39.6% in the questionnaire group. Length of stay, American Society of Anaesthesiologist class, and type of surgery, but not the survey techniques compared here, significantly influenced the number of complications reported. The percentage of patient-reported complications requiring treatment did not differ significantly between the groups.

Conclusions: The two survey methods did not differ in their ability to appreciate post-discharge complications as reported by the patients. The decision to use either method may be determined by the institution, costs involved and labour requirement.


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INTRODUCTION

Postoperative complications can lead to unfavourable health outcomes for the patient, requiring a change in therapy or even causing irreversible damage.1 This in turn can result in prolonged hospital stay and increased costs.2–4 Nowadays, most hospitals record the occurrence of postoperative complications during hospitalization. Such information can be useful for anticipating and counteracting the occurrence of preventable complications, which in turn is helpful for optimizing performance of the medical staff, thereby increasing the quality of patient care. 5 The most common definition of a postoperative surgical complication comprises an adverse outcome that occurs within 30 days after discharge.6,7 Hence, post-discharge complications should be recorded as well. After discharge, a patient is generally monitored by surgeons in the outpatient clinic, where it is common practice (at least in our hospital) to record only severe complications in patients that result in readmission, which likely underestimates the overall complication rate. However, this may vary substantially depending on the surgeon, nursing staff, and even the method by which it is recorded (paper-based or electronic).A recent study showed that if all complications are taken into account 58% of the surgical complications occur after discharge.8 Another study showed that 25% of all patients from a general surgical practice suffer from a post-discharge complication.9

Information about post-discharge complications is important for comprehensive registration of postoperative complications because it can help improve the quality of surgical care. Also, it enhances the communication between surgeon and patient as well as between surgeon and general practitioners about what to expect and how to avoid or deal with post-discharge complications.Most studies have used telephone follow-up to detect post-discharge complications during the 30-day period after discharge, but there is no gold standard regarding how to acquire the data that can determine the complication rate after discharge.8–11 Because telephone interviews seem more time-consuming and labour-intensive, questionnaires are a possible alternative for reporting post-discharge complications, but only if this method is equally effective for determining the number of these complications.12 On the other hand, depending on the institution, a written questionnaire or survey could be more labour-intensive because of the preparation of the questionnaire and the materials, postage, and data entry required.The aim of this randomized clinical trial was to compare two survey methods, follow-up interview by telephone or a questionnaire by mail, to determine the number of post-discharge complications in surgical patients during the 30-day period after discharge.

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This randomized clinical equivalence trial was a Health Innovation Project performed at the Department of Surgery of the Academic Medical Centre, a university hospital in Amsterdam, The Netherlands. This trial is described according to the revised CONSORT statement.13 Because this trial was not regarded as a medical intervention, it was not registered in a trial register, and our institutional review board waived the need for their approval.

PatientsFrom December 2008 until August 2009 all adult surgical patients admitted to and discharged from six surgical wards (one general, one vascular, one trauma, two gastrointestinal) of a university hospital were eligible for this randomized clinical trial (RCT). Patients who died, had a foreign address, or had been readmitted after a previous hospitalization within 30 days were excluded from the trial. All patients were informed about the trial at discharge and received a dedicated information brochure from the ward nurse stating that they would be approached after 30 days by phone or by mail to collect information about any complications they may have sustained during that period. The patients approached were informed about the definition of a complication and the relevant period during which the complications should have occurred (between discharge and 30 days after discharge) before starting the interview. The definition of a complication as used in our hospital was an unintended and unwanted outcome or state occurring during or following medical care that is so harmful to the patients’ health that it requires (adjustment of) treatment or leads to permanent damage during the period from discharge to 30 days after discharge.

Sample Size CalculationThe trial sample size was based on an expected complication rate of 25% in the telephone group, which was based on the post-discharge complication rate as found by Marang et al.9 With a sample size in each group of 761 patients, a two-group large-sample normal approximation test of proportions with a one-sided significance level of 0.05 would have 90% power to reject the null hypothesis that the telephone and questionnaire interviews are not equivalent. This is defined as a difference in complication rates of a least 6% from zero, in favour of the alternative hypothesis that the complication rates in the two groups are equivalent. Thus, we needed a total sample size of 1522 patients. With an anticipated limited response rate of about 55%, we aimed to include at least 2750 patients.


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RandomizationOn each weekday of the study period, the list of discharged patients was reviewed for eligibility. Patient randomization was performed after discharge by using a random number sequence generator as provided by a computer program to ensure allocation concealment.

Telephone InterviewPatients randomized for the telephone interview received a written announcement 30 days after discharge that they could expect a call for an interview within a few days. The telephone interview was held using a standard questionnaire. This interview covered questions about the type, localization, and severity of the complications and if the patient had sought medical help. Furthermore, some questions addressed whether and how the complication was treated and if the complication had resulted in readmission or (re)operation. The questions were based on the classification system of the Dutch national surgical complication registration system (LHCR) as developed by the Dutch Bar of Medical Specialists. It is based on national and international standards.6

The questions were presented to the patient or to a close relative if the patient was incapable of answering. If the patient could not be reached, the research assistant tried to call the patient up to five times, at different times during the day, each time using all available numbers.

QuestionnairePatients randomized for the questionnaire were approached, in writing, 30 days after discharge. The patients were asked to complete the standard questionnaire (the same as the one used for the telephone interview) on paper. The questionnaire was sent once because of costs and labour. Also, we assumed that the patients who did not respond the first time did not want to participate. All completed questionnaires received within 8 weeks after dispatch were included.

Outcomes AssessmentThe researcher who performed the telephone calls and sent the questionnaires was unaware of the patient’s medical condition and of the treatment given. Post-discharge complications were recorded if they matched the following definition: an unintended and unwanted event or state occurring during or following medical care that is so harmful to the patient’s health that (adjustment of) treatment is required or that permanent damage results.9

The primary outcome was the complication rate in each study arm, which was expressed as the total number of complications reported and the mean number of complications per admitted patient. The secondary outcome was the severity of the complications reported.

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They were categorized using the four-level severity scale as developed and described by Clavien et al.; 0, temporary health disadvantage without treatment; 1, recovering without (re)operation; 2, recovery after (re)operation; 3, (probably) permanent damage or function loss; 4, death. Complications requiring an interventional radiologic treatment (e.g., percutaneous drainage) were categorized as non-operative treatment (severity 1) rather than a true reoperation in the operating room (severity 2).14 Although complications were compared by severity, this study did not aim to compare which types of complications were reported.In addition, relevant patient characteristics were retrieved from the medical dossiers and electronic hospital databases. They included age, sex, day-care clinic versus clinical admission, American Society of Anaesthesiologists (ASA) classification, in-hospital complications, length of hospital stay, type of surgery, complexity of the surgery— defined according to the Dutch Surgical Association on a scale from 1 (simple) to 7 (complex).9 For each admission with a surgical intervention, we marked the procedure with the highest surgical complexity as the main operation.

Data AnalysisData were entered in a Microsoft Access (2003) database (Microsoft, Seattle, WA, USA) and transferred into PASW Statistics version 18 (IBM, Armonk, NY, USA) for further analysis. A possible difference in complication rates between the two survey methods was analysed using an x2 test and expressed as the risk difference (RD) including the 95% confidence interval (CI). Differences between continuous variables were analysed using the unpaired Student’s t test or the Mann–Whitney U test, depending on the variables’ normal distribution.Because other factors apart from the survey technique might be unequally distributed between the two techniques, we applied regression analysis to adjust for it and to determine which factors were independently associated with the number of reported complications. Because this number was not normally distributed, we performed a log transformation of the number of reported complications as dependent-variable. Variables showing a substantial (p<0.20) difference in number of complications between the trial groups were subsequently entered into the multivariable linear regression model.

RESULTS

During the 8-month period of this trial, 2768 clinical admissions were registered. Among them, 1395 patients were randomized for a telephone interview and 1373 for the questionnaire (Fig. 1). In both groups, several patients were excluded from analysis, mostly because of an unknown address or phone number or readmission during the 30 days after discharge. Details on trial flow and reasons for exclusion are shown in Fig. 1.


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Eventually, 1595 patients responded and were analysed, 890 by means of a telephone interview and 705 through the questionnaire. Thus, the response rate in the telephone group was higher than in the questionnaire group (63.8% vs. 51.3%).

Figure 1. Flow diagram of patient inclusion and analysis

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Patient CharacteristicsTable 1 summarizes the characteristics of included patients in the two groups. Respondents in the groups were similar, except for their mean age, which was significantly lower in the telephone group (53 years) than in the questionnaire group (58 years) (p<0.001). The median length of hospital stay in patients in the telephone group was slightly but significantly shorter than in the questionnaire group (4 vs. 5 days, respectively; p=0.013). The distribution of the types of surgery was significantly different between the groups (p=0.023): Patients in the questionnaire group underwent more vascular surgical interventions.

Table 1. Patient characteristics of respondents in telephone interview vs. questionnaire groups

Telephone interview Questionnaire p-value

N=890 N=705

Age (mean) 53 58 <0.001

Male gender 54% 53% 0.786

Day care 17% 15% 0.202

Complications during hospital stay 14% 16% 0.369

Days of hospital stay

(median and IQR*) 4.0 (2.0-9.0) 5.0 (2.0-9.0) 0.013

Underwent surgery 651 (73.1%) 530 (75.2%) 0.213

Types of surgery performed

Gastro-intestinal 284 (43.6%) 221 (41.7%) 0.023

Vascular 76 (11.7%) 90 (17.0%)

Trauma 113 (17.4%) 101 (19.1%)

Other 178 (27.3%) 118 (22.2%)

Complexity >6 25% 29% 0.149

Missing (N) 69 50

ASA at first operation ≥3 15% 15% 0.990

Missing (N) 280 204

Bold values indicate significant p-values (p < 0.05)IQR: Inter-Quartile Range, ASA: American Society of Anaesthesiologists


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Complications After DischargeComplication rates did not differ between the groups: 43.3% of all respondents in the telephone group and 39.6% in the questionnaire group reported to have suffered from one or more complications after discharge (RD 3.7%, 9% CI 1.2–8.5%; p=0.138). Table 2 shows that significantly (p=0.003) more complications were reported by the patients who completed the questionnaire (mean rate 1.53 complications per patient) than by those who were questioned on the telephone (mean rate 1.26).

The severity of the complications was significantly different between patients who returned the questionnaire and those reached by telephone (Table 2). The latter reported significantly more severity 1 complications, which required only conservative treatment (RD 6.7%, 95% CI 1.1–12.2%). The percentage of patient-reported complications that needed any treatment (severity 1 or 2) was not significantly different between the telephone group and the questionnaire group.The complication type was classified according to the LHCR system (Table 3). In both groups, almost 40% of the reported complications were symptoms rather than diagnoses (e.g. pain, fever, nausea). Two categories showed a difference in percentage. Complications categorized as a ‘‘functional disorder’’ (e.g., ileus, gastro paresis, weight loss) were reported twice as much in the telephone group. In the questionnaire group, the most frequently (19%) reported complication belonged to the category ‘‘other’’ (e.g., mental disorder).

Table 2. Complication numbers and severity grade in patients reporting complications after discharge, by telephone interview and questionnaire groups

Parameter Telephone interview N=890

QuestionnaireN=705

p-value

Reporting at least one complication 385 (43.3%) 279 (39.6%) 0.138

In-hospital complications 123 (14%) 105 (15%) –

Complications reported (total) 485 426

Complications per patient reporting at least one complication (mean)

1.26 1.53 0.003

Severity grade of complications

0 160 (33%) 159 (37%) 0.067

1 311 (64%) 244 (57%) 0.004

2 14 (3%) 23 (6%) 0.605

Bold values indicate significant p-values p < 0.05. Results are the number of patients or complications

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Table 3. Complication types in patients reporting complications after discharge, by telephone interview and questionnaire groups

Parameter Telephone interviewN=890

Questionnaire N=705

No. of complications 485 426

Symptoms without diagnosis: pain, fever, nausea 179 (37%) 151 (36%)

Functional disorders: bowel problems, weight loss, numbness, insomnia, cardiac problems

106 (22%) 41 (10%)

Inflammation/infection: abscess, (wound) infection 90 (19%) 89 (21%)

Accumulation/leakage of body fluids: gallbladder/chyle leakage or accumulation

33 (7%) 34 (6%)

Bleeding/hematoma: abdominal, anal, limbs, groin 28 (6%) 27 (6%)

Other: depression, dehiscence, others 49 (10%) 84 (19%)

Regression AnalysesUnivariable analysis showed that, apart from the survey technique, sex, age, length of hospital stay, day care, ASA classification, whether an operation was performed, and type of surgery substantially differed between the telephone and questionnaire groups.Multivariable regression analysis showed that using the questionnaire instead of the telephone interview increased the number of reported complications by 4%, but the difference was not statistically significant (95% CI -0.1% to 2.6%). There was a significant increase in the number of reported complications of 0.4% per day of additional hospitalization. This means that if the patient’s hospital stay was prolonged 1 day the chance of developing complications would increase by 0.4% (95% CI 0.1–0.8%). Day care was associated with a reduction in the complication rate of 9.2% (95% CI –0.3% to 17.5%), whereas the ASA class increased the complication rate by 6.4% (95% CI 1.3–11.9%). Finally, the type of surgery was significantly associated with a change in complication rates.

DISCUSSION

This trial shows that about 40% of surgical patients report experiencing one or more complications during the 30-day period after discharge, most of which require additional non-operative or surgical treatment. The two survey methods, telephone interviews and mailed questionnaires, showed a significant difference in number, type, and severity grade of reported complications after discharge. However, the two groups also showed differences in patient characteristics: number of days in the hospital, sex, and type of surgery. Multivariable analyses showed that factors other than the survey method (e.g., hospital stay) are strongly associated with a larger number of post-discharge complications. Taking this into account, we assume that the two survey methods do not differ in their ability to identify post-discharge complications as reported by the patients, in particular


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the complications that require further treatment. The reporting of these complications was associated with ASA class, clinical admissions, length of hospital stay, and type of surgery. Because the percentages of in-hospital complications in the two groups were similar, we did not further analyse the influence of in-hospital complications (e.g., type and number) to complications as reported by the patients after discharge. The differences in types of complications may be related to the differences in patient characteristics in both groups. For example, the higher percentage of bowel-related complications might well be correlated with the number of gastrointestinal procedures in this group.15,16

To ensure correct reporting of complications by patients using any method, its definition must be clear and strict. Although the definition was explained to every patient interviewed, in a telephone follow-up the interviewer can ask if the definition is clear and explain it if necessary. Answers obtained through a questionnaire might be biased when given by person other than the patient. However, this also happened during some telephone interviews because of the health status of the patient or language barriers. Furthermore, telephone interviews may lead to more socially acceptable answers, whereas patients may answer more freely if approached by questionnaire.12 Also, patients may have difficulty determining whether their complaint fits the definition of a complication. Even among specialists, this can be ambiguous.7 Further research should compare the answers of patients during the follow-up with the information in the outpatient record as recorded by the specialist to establish if there is a difference between the interpretation of a complication by a patient or a specialist and whether patient-reported complications can be considered valid and useful information.The response rate of telephone follow-up is likely to be higher than that by questionnaire because a letter is easier to ignore. This was also apparent in our trial, which recorded a 15% higher response rate in the telephone group. The difference in this trial may also have been due to the fact that questionnaires were sent only once, whereas phone calls were repeated if unanswered. Nevertheless, the effectiveness and costs of telephone interviews seem much less profitable than the questionnaires. Clearly, most studies have used a telephone follow-up because of the better response rate.8–11 Several studies have presented methods to increase the response rate to questionnaires; for example, contacting people before sending a questionnaire, providing a stamped self-addressed envelope, keeping the questionnaire short, and making it more personal.17,18 In our study, we called the patient up to five times, but the questionnaire was sent only once. Had we used these methods we probably could have further increased the response rate for the questionnaires. Unfortunately, sending the questionnaire more than once means higher costs and more labour, which might make it as expensive and labour-intensive as a telephone follow-up. Finally, although patients appreciate follow-up by telephone, there is no evidence of its effectiveness in preventing complications or readmissions.10,11

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The considerable dropout rate, resulting in differences in patient numbers and characteristics between the two groups in our trial, was due to the fact that randomization of patients took place before making sure the patients had a valid address or telephone number and checking if they had died. This might have led to attrition bias, although it is unlikely that being unable to reach the patients was related to the number of complications they may have had. This could also explain the complication rate of almost 40%, whereas the expected complication rate based on previous research was only 25%.9 The dropout rate after randomization may have led to the differences found in patient characteristics in the two randomized groups. After correcting for these differences, the two groups showed no significant difference in the number of reported complications. These characteristics can also be useful for developing triggers to search for complications, which might imply for sending a questionnaire or to check the medical file or other sources.19 An alternative is to check only the patient records of the patients with a high risk of complications.Although post-discharge complications tend to be missed, the question remains whether hospitals are willing to invest time and money in registering all complications, including those that occur after discharge. It may require the use of more resources to detect such complications. Complications after discharge that lead to readmission, reoperation, or death are generally recorded with the current registration methods. On the other hand, missed complications are usually those that require conservative treatment (e.g., medical treatment from their general practitioner), which are nevertheless adverse outcomes patients perceive as important and undesirable. Moreover, the information registered is usually part of a quality measure that aims at preventing complications. To optimize this stratagem, as many complications as possible should be included because they may ultimately prevent the need for additional care.Wound infection is one of the national indicators for quality of care and patient safety [Dutch Inspectorate of Health Care, www.igz.nl]. Hospitals are obliged to register wound infections and report their numbers to national health care institutions. A reliable, complete complication registration can be useful not only to improve quality of care but also as a benchmark on a national level.

CONCLUSIONS

The two survey methods did not differ in their ability to identify post-discharge complications as reported by patients. The decision to use either method may be determined by the institution, costs involved and labour requirement. Information about complications after discharge is valuable for improving the quality of care and for informing the patient about the benefits and risks of a treatment.


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REFERENCES

1 Kievit J, Krukerink M, Marang-van de Mheen PJ. Surgical adverse outcome reporting as part of the routine clinical care. Qual Saf Health Care 2010;19:e20.

2 Brennan TA, Leape LL, Laird NM et al. Incidence of adverse events and negligence in hospitalized patients: results of the Harvard Medical Practice Study I. N Engl J Med 1991;324:370–6.

3 Thomas EJ, Studdert DM, David ML et al. Incidence and types of adverse events and negligent care in Utah and Colorado. Med Care 2000;38:261–71.

4 Thomas EJ, Studdert DM, Burstin HR et al. Costs of medical injuries in Utah and Colorado. Inquiry 1999;36:255–65.

5 Healy MA, Shackford SR, Osler TM et al. Complications in surgical patients. Arch Surg 2002; 137:611–8.

6 Marang van de Mheenen PJ, Kievit J. Automated registration of adverse events in surgical patients in The Netherlands: the current status. Ned Tijdschr Geneeskd 2003;47:1273–7.

7 Veen EJ, Janssen-Heijnen ML, Leenen LP et al. The registration of complications in surgery: a learning curve. World J Surg 2005;29:402–9. doi:10.1007/s00268-004-7358-8

8 Kaasschieter EG, van Olden GJ. Complicatieregistratie en fractuurbehandeling. Ned TijdschrTrauma 2007;6:182-5 (in Dutch)

9 Marang-van de Mheen PJ, van Duijn-Bakker N, Kievit J. Adverse outcome after discharge: occurrence, treatment and determinants. Qual Saf Health Care 2008;17:47–52.

10 Mistiaen P, Poot E. Telephone follow-up, initiated by a hospital-based health professional, for postdischarge problems in patients discharged from hospital to home. Cochrane Database Syst Rev 2006;18:CD004510

11 D’Amore J, Murray J, Powers H et al. Does telephone follow-up predict patient satisfaction and readmission? Popul Health Manag 2011;14:249–55.

12 Swanborn PG. De enquête. In: Methode van Sociaalwetenschappelijk Onderzoek. Boom, Meppel, The Netherlands 1981;265–313 (in Dutch)

13 Schulz KF, Altman DG, Moher D et al. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ 2010;340:c332

14 Clavien PA, Sanabria JR, Strasberg SM. Proposed classifications of complications of surgery with examples of utility in cholecystectomy. Surgery 1992;11:518–26.

15 Story SK, Chamberlain RS A comprehensive review of evidence-based strategies to prevent and treat postoperative ileus. Dig Surg 2009;26:265–75.

16 Vlug MS, Wind J, van der Zaag E et al. Systematic review of laparoscopic vs. open colonic surgery within an enhanced recovery program. Colorectal Dis 2009;11:335–43.

17 Hing CB, Smith TO, Hooper L et al. A review of how to conduct a surgical survey using a questionnaire. Knee 2011;8:209-13.

18 Edwards PJ, Roberts I, Clarke MJ et al. Methods to increase response to postal and electronic questionnaires. Cochrane Database Syst Rev 2009;8:MR000008.

19 Dambrink JH, Hermanides R, Beuving D et al. Op zoek naar late complicaties. Med Contact 2010;65:1536–1539 (in Dutch)

The ‘Medusozoa’ had lost her body and antennae and could no longer fly. She was given a new set of flying tools and a propeller. Anne ten Donkelaar.

Chapter 5

Surgeons Are Overlooking Post-Discharge Complications:

A Prospective Cohort Study


J Carel Goslings

World Journal of Surgery 2014; 38:1019-25

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ABSTRACT

Introduction: The registration of surgical complications is an important quality indicator of hospital medical care. Previous research has suggested that surgeons only record certain complications after discharge. The extent and impact of this potential under-recording of post-discharge complications is unknown. Therefore, we aimed to determine the frequency, type, and grade of post-discharge complications as reported by patients and their surgeons.

Methods: A prospective cohort study was performed in the Department of Surgery of a University Medical Centre. From December 2008 until August 2009, all adult surgical patients were interviewed by phone or questionnaire 1 month after their discharge to inquire about any new complications after discharge. These complications were compared with the surgeon-reported post-discharge complications and letters from the outpatient clinic as documented in the patients’ medical files.

Results: A total of 976 patients were included. Patients reported more complications (659) than did surgeons (465), especially psychological disturbances (4.2 vs. 0%). A medical consult was needed in 527 (80%) of the patient-reported complications. Of all patient-reported complications, 291 (44%) resulted in a visit to the outpatient clinic, 144 (22%) in a consultation with a general practitioner, and 92 (14%) led to referral to a hospital; 743 (76%) were treated non-operatively.

Conclusion: Surgeons are unaware of many of the complications their patients experience after discharge. These post-discharge complications are important to patients and are therefore relevant to be aware of and to act upon whenever necessary.


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INTRODUCTION

Complications are an inherent risk of surgical interventions. They generally lead to reduced patient health outcomes, can cause irreversible damage, or require a change in therapeutic planning, which can result in a prolonged hospital stay and increased costs.1,2 Registration of these complications is becoming increasingly important as a quality indicator of hospital care.3–5

While patient monitoring and registration of complications during hospitalization is a routine procedure, this is performed less consistently after discharge, either in the outpatient clinic by the surgeon or by general practitioners (GPs). Surgeons therefore tend to have an incomplete view of their patients’ complications after discharge. However, which and how many post-discharge complications surgeons miss or do not record is unknown.The generally accepted definition of a post-discharge surgical complication in the Netherlands is ‘‘an adverse outcome occurring between discharge and 30 days after discharge’’.6 Recent studies have shown high rates of post-discharge complications, but most studies do not include these as an outcome parameter. A previous study showed that 25% of all patients in a general surgical practice experienced post-discharge complications.7

Other studies have shown that between 41.5 and 58% of all complications occur after discharge.8,9 However, these studies did not report on the differences between surgeon-reported and patient-reported complications and their severity grade.More accurate information on complications after discharge may provide a more reliable estimate of all surgery related complications. Post-discharge complications may have an impact on patients and are therefore relevant to be aware of and to act upon when necessary. The aim of this study was therefore to compare the number, type, grade, and treatment of complications in surgical patients after discharge as reported by surgeons and patients.

METHODS

A prospective cohort study was performed at the Department of Surgery of the Academic Medical Centre, a university hospital in Amsterdam, the Netherlands. This study was reported in accordance with the STROBE (Strengthening the Reporting of Observational studies in Epidemiology) statement.10 The medical ethics board waived the need for approval, as the study did not interfere with the treatment of patients, and the interview would not cause a serious psychological burden.The definition of a post-discharge complication as used for this study was ‘‘An unintended and unwanted outcome or state that occurs in the 30-day period after discharge and is so harmful to the patients’ health that it requires (adjustment of) treatment or leads to permanent damage’’.6-11

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PatientsFrom December 2008 until August 2009, all adult surgical patients admitted to and discharged from six surgical wards (general, vascular, trauma, gastrointestinal surgery [two], and day surgery) were eligible for this study. Patients who had died, had no forwarding address, or had been readmitted after a previous hospitalization within 30 days were excluded from the study, because these patients were unable to report post-discharge complications.Only patients who completed the questionnaire were included, because data were needed from both sources (questionnaire vs. medical file and outpatient letter) to be able to compare the information from patients and surgeons about the complications they perceived.Furthermore, if the patient’s medical file and outpatient letter could not be retrieved 30 days after discharge, the patient was excluded from our analysis, because information was lacking about the complications the surgeon had recorded.

Collecting Patient-reported ComplicationsAt discharge, patients were informed that they would be contacted after 30 days by phone or by mail to collect information about any new complications they may have sustained during the period after discharge. The researcher who performed the telephone interviews and sent the questionnaires was blinded to both the patient’s medical condition and the treatment given. Patients were interviewed by trained interviewers who used a structured, predefined set of interview questions. Methodological details are described in a previous publication.12

During this interview, the patients were first informed about the definition of a complication and the period of concern (between discharge and 30 days thereafter) and were then asked to report any new complications occurring during that period.

Collecting Surgeon-reported ComplicationsMedical files and outpatient clinic letters of all respondents were checked for surgeon-reported post-discharge complications, and collected by a medical student who was familiar with the definitions used in this study, but blinded to the patient-reported complications and treatments the patients had undergone. Data were collected on frequency, type, grade, and treatments of complications reported.

Additional Data CollectedRelevant patient characteristics were also retrieved from medical dossiers and electronic hospital databases, including age, gender, day surgery or hospital admission, American Society of Anaesthesiologists classification, in-hospital complications, length of hospital stay, type of surgery, complexity of surgery. The latter was defined according to the surgery


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complexity scale used by the Dutch Surgical Association, ranging from 1 for ‘simple’ to 7 for ‘complex’ procedures.11 For admissions with more than one surgical intervention, the surgical procedure with the highest surgical complexity was defined as the main operation. ‘High complexity’ was defined as 6 or 7 on the surgical complexity scale.

Data AnalysisReported complications were classified according to the Dutch national surgical complication registration system (LHCR), as developed by the Dutch Surgical Association and based on national and international standards.11

Complication grade was categorized using the four-level grade scale as developed and described by Clavien et al.; (0) temporary health disadvantage without treatment; (1) recovering without (re)operation; (2) recovery after (re)operation; (3) (probably) permanent damage or function loss; and (4) death.13 Complications requiring an interventional radiological treatment (e.g. percutaneous drainage) were categorized as non-operative treatment (grade 1) rather than a true re-operation in the operating room (grade 2).Data were entered in Microsoft® Access version 2003 (Microsoft Inc., Seattle, WA, USA) and exported into IBM Statistics version 20 (IBM Inc., Armonk, NY, USA) for further analysis. Frequencies were expressed as means and standard deviations, or medians and inter-quartile ranges (IQR) if the distribution was skewed. Crude complication percentages were compared statistically, i.e., without matching the specific types of complications as mentioned by surgeons and patients, using the Chi-squared test. McNemar’s test was used to test the significance of the differences between paired proportions, i.e., to analyse statistically the amount of disagreement between patients and surgeons about whether or not a complication had occurred. The differences in frequency, type, and grade of reported complications were expressed as risk differences (RD), including their 95% confidence intervals (CI).

RESULTS

Over an 8-month study period, a total of 2768 clinical admissions were registered. These patients were approached for an interview or questionnaire; 6% of the patients could not be reached (N = 165). Reasons for exclusion are shown in figure 1. The final response rate was 58%. Data from 976 patients (61%) were obtained and analysed.Patient characteristics and the types of surgery they underwent are detailed in Table 1. One-fourth of the surgical procedures (N=187; 25.3%) were rated as ‘high complexity’ (Table 1). Of the 976 respondents, 16% (N=159) had at least one in-hospital complication, resulting in 300 registered in-hospital complications.

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Of all patients, 420 (43%) reported one or more complications after discharge, which was significantly (p<0.001) higher than the number of patients with post-discharge complications according to the surgeons (N=363; 37%; RD 5.8%, 95% CI 2.2–9.5). Moreover, patients and surgeons did not agree on whether a post-discharge complication had occurred (p<0.001; McNemar). Patients also reported more post-discharge complications than did surgeons (659 vs. 465, respectively; p<0.001). Surgeons rarely registered more than three complications, while some patients reported four to six complications (N=19; see figure 2).

Figure 1 Flow diagram of patient inclusion and analysis

All patientsN=2768

Reasons for exclusion:Re-admission 79Death 34Unknown address 30Other 22

Contacted patientsN=2603

RespondentsN=1595

InclusionN=976

Reasons for exclusion:No medical records 456No outpatient visit <30 days 165

No response N=1008

ComplicationsReported by patients

N= 659

Emergency Department14% (N=92)

Missing

4% (N=27)

Outpatient clinic

44% (N=291)

General practitioners22% (N=144)

No action

16% (N=105)

In-hospital complications

N=300


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Figure 2. Number of patients with 0–6 complications in the 30 days after discharge period: surgeon-reported complications versus patient-reported complications

Table 1. Characteristics of responding patients

Total N=976

Males: N (%) 515 (52.8)

Median age (IQR*) 56.6 (43.6-67.6)

Median hospital stay in days (IQR*) 5.0 (2.0-10.0)

In-hospital complication rate (%) 159 (16.3)

In-hospital complications: N 300

Surgery: N (%) 743 (76.1)

Day-surgery 116 (15.4)

Clinic 627 (84.6)

Type: N (%)

Gastro-intestinal 326 (33.4)

Vascular 60 (8.1)

Trauma 166 (22.5)

Other 187 (25.3)

Surgical procedure complexity level ≥ 6: N (%) 187 (25.3)

* IQR = Inter-Quartile Range

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The complication types as reported by surgeons and patients are summarized in Table 2. The most frequently reported complication types were symptoms without specific diagnoses (e.g. pain or fever), infections, and functional disorders (e.g. bowel or cardiac problems, weight loss). Patients reported significantly more complications related injury to the surgical site (RD 2.2%; 95% CI 0.8–3.5), and psychological disturbances (RD 4.3%; 95% CI 2.7–5.8), whereas surgeons did not register any such complications. Surgeons reported significantly more abnormal wound healing than patients (RD 5.9%; 95% CI 2.9–8.9).

Table 2. Types of complications of patients with one or more complications in the 30 day-period after discharge: surgeon- versus patient-reported complications

Surgeon-reported N=465

Patient-reported N=659

% Risk difference (95% CI)

Symptoms without diagnoses, e.g. fever, pain, nausea

156 (33.5%) 238 (36.1%) 2.6 (-3.1 to 8.2)

Inflammation/infection, e.g. abscess, (wound) infection

106 (22.8%) 139 (21.1%) -1.7 (-6.6 to 3.2)

Functional disorder, e.g. bowel problems, weight loss, numbness, insomnia, cardiac problems

63 (13.5%) 98 (14.9%) 1.3 (-2.8 to 5.5)

Abnormal wound healing, e.g. dehiscence

43 (9.3%) 22 (3.3%) -5.9 (-8.9 to -2.9)

Accumulation/leakage of body fluids, e.g. gall/chyle leakage or accumulation

29 (6.2%) 55 (8.4%) 2.1 (-0.9 to 5.2)

Bleeding/hematoma, e.g. abdominal, anal, limbs, groin

24 (5.2%) 36 (5.5%) 0.3 (-2.4 to 3.0)

Injury by mechanical, physical or chemical cause, e.g. loose suture or dislocated drain, re-rupture, pins piercing skin

2 (0.4%) 17 (2.6%) 2.2 (0.8–3.5)

Psychological disturbance, e.g. depression, delirium

0 28 (4.2%) 4.3 (2.7–5.8)

Other, e.g. allergy, pressure sores, fistulae, thrombosis

42 (9.0%) 26 (4.0%) -5.0 (-2.1 to -8.1)

Of the reported post-discharge complications 94% were treated non-invasively (Table 3). Grade 0 complications were reported more frequently by the surgeon than by the patient (RD 9.9%, 95% CI 4.2–15.6). Patients reported significantly more grade 2 complications (RD 2.7%, 95% CI 0.7–4.6) than the surgeons did.Patients sought medical help or advice for 84% (N=527) of the complications they reported. Of these, more than half (N=291) presented to the outpatient clinic (27 of which were in


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another hospital), while 92 complications were seen in the Emergency Department. For the remaining 144 (27%) complications, GPs were consulted (Fig. 1). The complications presented to GPs concerned pain (N=39), infection (N=32), and wound problems (N=11). These complications were all treated non-surgically.

Table 3: Number of complications of patients with one or more complications by grade in the 30-day period after discharge: Surgeon-reported vs. patient-reported

Surgeon-reported Patient-reported % Risk Difference (95% CI)

Total number of complications 465 659 p<0.001*

Grade 0 195 (42%) 211 (32%) -9.9 (-15.6 to -4.2)

Grade 1 258 (56%) 388 (59%) 3.4 (-2.5 to 9.3)

Grade 2 6 (1.3%) 26 (4.0%) 2.7 (0.7 to 4.6)

Not reported 6 34 3.9 (1.8 to 6.0)

* Pearson Chi-square

DISCUSSION

This prospective cohort study shows that surgical patients report more post-discharge complications than do their surgeons, and experience more, albeit mild, complications after discharge than during hospitalization. Apparently, surgeons tend to overlook post-discharge complications among their patients, some of which warrant additional surgical care. Hence, surgeons should look harder for post-discharge complications with which they should be dealing.Although some complications may require only non-operative treatment and may be taken care of by the patient’s GP, patients perceive these complications as important and undesirable, because they seek help for them by visiting their GP, an outpatient clinic, or an Emergency Department. Surgeons should therefore better inform their patients at discharge that complications may still occur and how they can detect such complications, especially wound infection, and discuss their role in preventing complications, especially regarding pain and wound infection.Although surgeons reported more complications requiring no additional treatments, they missed others, some of which required treatment by GPs. It is possible that surgeons do not document every complication they recognize in their patients. In particular, the higher-grade complications tended to be omitted, although these are more likely to be documented than those not requiring medical treatment. In our study, 40% of surgical patients reported post-discharge complications, which is higher than reported in previous studies.7–9 It is likely that this figure also included complications that did not require medical advice.

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The finding that patients reported more higher-grade complications than did surgeons may be because patients graded interventions such as re-stitching, opening the wound, and radiological investigations as grade 2, whereas surgeons rated them as grade 1. Another explanation could be the finding that patients did not report less severe post-discharge complications to their surgeon, but consulted their GPs for these and were treated there. Thus, the surgeon may not have been informed about these complications.Wound infections were the only post-discharge complications reported more frequently by surgeons than by patients. Wound infection is one of the nationally recognized indicators for quality of care and patient safety (The Health Care Inspectorate, www.igz.nl/english). Hospitals are obliged to register wound infections and to report their incidence to the Inspectorate. This obligatory reporting may have been exemplified by our findings.Despite the discrepancies found between complications perceived by surgeons and patients, the current definition of a complication remains valid, as it is an accepted and practical definition among surgeons when recording complications after surgery. Furthermore, screening for complications may well be carried out by trained surgical assistants as was found in our previous trial.12 The current method was found to be clear enough to be used by assistants, which may save time for the surgeon.Although post-discharge complications seen by GPs tend to be missed by surgeons, the question remains whether hospitals should be willing to invest time and money in registering all complications, including those occurring after discharge. This may require the use of more resources to detect and record such complications14, while complications after discharge leading to re-admission, reoperation, or death will usually be recorded in the current registration. What pleads in favour is that quality of care has become an important aspect of transparency of care, and complications is one of these parameters. Surgeons can also use these complication data to inform the patient about the risks of surgery. However, complications missed by the surgeon are mostly, and fortunately, the (less severe) complications that can be dealt with by a GP. Hence, it is most likely not cost effective to strive to collect and record all these complications.

Study LimitationsFirst, our medical files were digitalized during this study, sometimes resulting in restricted access to medical records. This partly explains why the records of 456 respondents were irretrievable at that time. Another reason is that the patient had to be excluded from our analysis if the medical file or outpatient letter could not be retrieved, because in that case we would not be fully informed about what the surgeon had recorded as complications. This might have led to selection bias, although it is unlikely that these drop-outs caused substantial bias, in terms of more or less complications than when the files could be retrieved completely.


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Second, it is debatable whether patients can reliably report complications and whether they might give socially desirable answers. Patients may have difficulty determining whether their complaint fits the definition of a complication and which intervention is to be counted as a re-operation. Even among medical specialists, such definitions can be ambiguous.15

Other studies have shown that patients are clearly in a position to report issues related to patient safety.5,16 However, none of these studies included vulnerable patients and included only the responders. Hence, it is not likely that all patients will be able (or are willing) to be involved. To ensure correct reporting of complications by the patients by any method, the definition of a complication must be clear and strict, so that patient-reported complications are reliable. To ensure correct reporting of complications by surgeons, the sources for recording should be reliable and complete.14 These are important prerequisites for the accurate recording and reporting of post-discharge complications.Third, in this study, the specific complication types mentioned by patients and surgeons could not be matched. Hence, crude complication rates were reported rather than on a patient level. Patients usually have no medical background and might give a different description of their complaint or complication. For example, a single complication could be described by the patient as ‘pain’ or ‘fever’, while the surgeon could document the same as ‘wound infection’.Fourth, patients who were readmitted or had died at the time of the interview, 30 days after discharge, were excluded. Readmission, reoperation, and death are registered in our complication registry and are considered to be the most severe complications. Due to this exclusion, the set of patients studied was less representative for those suffering in-hospital complications. However, this very readmission rendered them unable to complete the questionnaire. Only patients who completed the questionnaire were included, because data were needed from both sources (patient’s questionnaire vs. surgeon’s medical file and outpatient letter) to be able to compare the complications reported by patients and surgeons. In doing so, we missed the complications that might have been reported by readmitted patients. However, our current registration records in-hospital complications, while those occurring after discharge are not registered unless resulting in readmission or/ and reoperation.Finally, the primary aim of our study was to assess whether or not surgeons overlook any of the complications patients complain of after discharge, and how these are dealt with. Further research should address the impact of the complications missed after discharge, for example, in financial terms, methods by which post-discharge complications should be captured or managed, or how post-discharge complications can be prevented.

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CONCLUSION

One in four post-discharge complications in surgical patients are missed by the treating surgeon. Most of these patients with complications are seen and treated by GPs. Surgeons should anticipate common post-discharge complications and communicate with their patients about what to do, should this happen, to avoid unnecessary involvement of, or referral to, other healthcare professionals.


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REFERENCES

1. Kievit J, Krukerink M, Marang-van de Mheen PJ. Surgical adverse outcome reporting as part of routine clinical care. Qual Saf Health Care 2010;19:e20.

2. Brennan TA, Leape LL, Laird NM et al. Incidence of adverse events and negligence in hospitalized patients: results of the Harvard Medical Practice Study I. N Engl J Med 1991;324: 370–6.

3. de Bruijne MC, Zegers M, Hoornhout LH et al. Onbedoelde schade in Nederlandse Ziekenhuizen. EMGI Instituut en NIVEL, Utrecht. 2007

4. Cima RR, Lackore KA, Nehring AA et al. How best to measure surgical quality? Comparison of the Agency of Healthcare Research and Quality Patient Safety Indicators (AHRQ-PSI) and the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) postoperative adverse events at a single institution. Surgery 201;150(5):943–9.

5. Grosse Frie KJ, van der Meulen J, Black N. Relationship between patient’s report of complications and symptoms, disability and quality of life after surgery. Br J Surg 2012;99:1156–63.

6. Goslings JC, Gouma DJ. What is a surgical complication? World J Surg 2008;32(6):952. doi:10.1007/s00268-008-9563-3.

7. Kazaure HS, Roman SA, Sosa JA. Association of postdischarge complications with reoperation and mortality in general surgery. Arch Surg 2012;147(11):1000–7.

8. Kaasschieter EG, van Olden GJ. Complicatieregistratie en fractuurbehandeling. Ned Tijdschr Trauma 2007;6:182–85.

9. Marang-van de Mheen PJ, van Duijn-Bakker N, Kievit J. Adverse outcome after discharge: occurrence, treatment and determinants. Qual Saf Health Care 2008;17:47–52.

10. STROBE statement. http://www.strobe-statement.org. Accessed 30 Sep 2013.

11. Marang-van de Mheen PJ, van Duijn-Bakker N, Kievit J. Automated registration of adverse events in surgical patients

in the Netherlands: current status. Ned Tijdschr Geneeskd 2003;47: 1273–7.

12. Visser A, Ubbink DT, Gouma DJ et al. Questionnaire versus telephone follow-up to detect post-discharge complications in surgical patients: randomized clinical trial. World J Surg 2012;36:2576–2583. doi:10.1007/s00268-012-1740-8.

13. Clavien PA, Sanabria JR, Strasberg SM. Proposed classifications of complications of surgery with examples of utility in cholecystectomy. Surgery 1992;111(5):518–26.

14. Ubbink DT, Visser A, Gouma DJ et al. Registration of surgical adverse outcome: a reliability study in a university hospital. BMJ Open 2012;2(3):e000891.

15. Veen EJ, Janssen-Heijnen ML, Leenen LP et al. The registration of complications in surgery: a learning curve. World J Surg 2005;29:402–409. doi:10.1007/s00268-004-7358-8.

16. Ward JK, Armitage G. Can patients report patient safety incidents in a hospital setting? A systematic review. BMJ Qual Saf 2012;21(8):685–99.

Part 2

IMPROVEMENT OF SURGICAL

COMPLICATION REGISTRATION

‘Carnival’ is a grashopper that dressed up like a butterfly. He has got a pair of wings and high heels made from flower twigs. Anne ten Donkelaar.

Chapter 6

Which Clinical Scenarios do Surgeons Record

as Complications?

A Benchmarking Study of Seven Hospitals


J Carel Goslings

Submitted

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ABSTRACT

Background: The trend to develop national benchmarking data, including those regarding complications in hospitalized surgical patients is growing. To obtain high-quality benchmarking data a reliable and uniform registration by the participating surgical departments is required. Several studies show considerable variability regarding the definition of a complication and regarding the application of this definition. To investigate agreement and potential differences in the application and interpretation of the definition among surgical departments of various hospitals.

Design: Twenty-four cases were formulated including general, trauma, gastrointestinal and vascular surgery and based on points of discussion about the definition and ambiguities regarding complication registration as encountered in daily practice. The cases were presented to the surgical staff and residents in seven Dutch hospitals using an electronic response system.

Results: In total 134 participants responded. Interpretation differences were particularly found regarding: 1) complications considered as logical consequences of a surgical procedure; 2) complications occurring after radiological interventions; 3) severity grade criteria as when to consider a complication as a ‘(probably) permanent damage or function loss’ 4) registering a cancelled operation as a complication; and 5) patients with serial complications during hospital stay.

Conclusion: The definition of surgical complications as currently applied in the Netherlands does not ensure a uniform complication registration. Improvement of this registration system is mandatory before benchmarking of these findings in the public domain is appropriate. Modifications of the current definition of a surgical complication and improved consensus about specific clinical situations and training of surgeons might improve the quality of benchmarking.


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INTRODUCTION

The trend to develop national benchmarking data, including those regarding complications suffered by surgical patients during their hospital stay or shortly after discharge is ongoing. For example, the national benchmarking by the NSQIP institutions (the American College of Surgeons National Surgical Quality Improvement Program) appears to be improving morbidity and mortality over time.1 In order to obtain high quality benchmarking data, it is necessary to correct for underreporting of complications, and for differences in case mix as well as in the level of complexity of the interventions.2 The validity of benchmarking data also depends on the quality control of these data.3,4 High-quality data requires reliable and uniform registration by the participating surgical departments. This includes that at least, for identical situations, all hospitals should register the same complications with the same degree of severity. Santford et al. already showed that variations in definition and methods of retrieval greatly influence what is rated as a complication in patients undergoing a pacreatoduodenectomy.5 This is especially true for complications of a lower severity grade.Other studies have shown that there is still variability about the definition of a complication or regarding the interpretation of this definition.6,7,8 Should we define a complication as an undesirable event following surgical medical care? By this definition an operative scar would also be a complication.6 Or do we perhaps consider a complication to be an unexpected result? Is damage of an intra-abdominal organ – for example injury to the spleen during pancreatic surgery – a complication, or only if this negatively affects the patient outcome, for example when an accidental splenectomy is performed with the patient has to follow a vaccination programme? In the Netherlands, the currently used definition of a surgical complication consists of three essential components (specified by the Association of Surgeons of the Netherlands (NVvH) and the Dutch Association of Medical Specialists):9,10

A complication is an unintended and undesirable event or state that:1. Occurs during or following a medical specialist intervention that negatively affects the patient’s health such that this requires their medical treatment to be adapted, or such that irreparable damage is caused,2. Is established either during in-hospital treatment or during immediate follow-up, up to a period of 30 days after discharge3. Is the result of the actual medical specialist intervention, the chances of the complication occurring, and the presence or absence of culpability is not relevant

It is unclear whether this definition is interpreted and applied in the same way among different surgical departments and by all surgeons within a surgical department. Therefore

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we investigated the agreement in the registration of complications within and among the surgical departments of hospitals in The Netherlands.

METHODS

Example CasesAn inventory study was carried out in a convenience sample of seven hospitals. Two surgeons formulated 24 cases based on critical points of discussion, definitions and ambiguities regarding the registration of complications taken from their experience during complication registration from daily practice (Table 1).The questions were divided into the following six main categories:1. Definition. Whether this is a complication according to the definition of a ‘complication’ as defined

by the Association of Surgeons of the Netherlands.2. Other specialty. Whether complications of a patient admitted at the surgical department were included

in the registry if these occurred as a result of an other specialty but within the well-defined postoperative period of registration (during admission and a the 30-day period after discharge).

3. Severity. Determining the grade of severity of the complication, categorized using the four-level

grade scale based on Clavien and Dindo grading system;11 Severity 0) temporary health disadvantage without treatment 1) recovering without (re)operation; 2) recovery after (re)operation; 3) (probably) permanent damage or function loss; and 4) death.

4. Intra-operative damage. Whether complications that occurred intra-operatively were registered.5. Cancellation of operations. Whether physicians registered cancelled operations as a complication.6. Serial complications and transfers. The registration of complications of patients with severe and serial medical problems

and transferred from other hospitals.Each category was represented by at least three questions. Since some cases were relevant for several categories; these cases were also assigned to several categories and analysed as such.


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Procedure Medical professionals (surgeons, fellows and residents) working at the surgical departments of seven hospitals participated in the study. These hospitals included two university medical centres, four tertiary referral hospitals and one general teaching hospital. The 24 cases were presented at a random order in the format of a multiple-choice quiz to the members of the surgical staff and residents. The responses were registered using electronic voting devices (Turning Technologies LLC, Youngstown, OH, USA). The participants were first asked about their position (attending surgeon, fellow, resident) and specialty/subspecialty (gastrointestinal/oncology, vascular surgery, trauma surgery, or not applicable). The approved definition of a complication was not shown to the participants before the session and they were not allowed to ask any questions for clarification during the presentation of the cases. The potential responses to the 24 example cases were either dichotomous or categorical. Participants were given 10 seconds to respond to each case and the time available was shown on a screen. The number of participants that voted for each case was recorded.

Data AnalysisData was analysed for each hospital, per case and per category. The dichotomous answers were used to calculate the proportion of participants (in percent) who responded to the case with ‘yes, I register this as a complication’ and the total number of participants for that case. Proportions close to 100% were defined as unanimity in the interpretation of a particular case as a complication; the same applied for numbers close to 0% for cases not being considered as a complication. For each question, the average (with its range) percentage of ‘yes’ responses was calculated over all hospitals, weighted for the number of participants per hospital. The results of the example cases that had a categorical range of responses were analysed separately. Analysis of responses related to function (staff versus residents) were performed by Chi-squared test. A p<0.05 was considered statistically significant.

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Table 1. CasesCase Outcome measure N Category

1. After a left hemicolectomy, a patient suffers a postoperative anastomotic leak and an intra-abdominal abscess. How many complications would you enter into the complication registry?

0 (No) / 1 (Yes) / 2 (Yes) 132 16

2. A patient is admitted with bilateral multiple rib fractures and haemothorax. During clinical course patient develops new episode of respiratory insufficiency with pneumonia, requiring re-intubation. Also delirium and UTI, which are treated. Which complication(s) do you register?

None / Resp. & UTI / Resp. & UTI & delirium / Resp. & UTI & delirium & pneumonia

109 16

3. During surgery, the patient’s spleen is accidentally damaged, requiring splenectomy. This means that the patient must take part in a vaccination programme.

NoYes; temporary / Yes; re-op / Yes; permanent

127 134

4. A patient is operated for a perforated appendix. The wound is not left open but is aligned with 2 stitches. The wound becomes infected.

No / Yes 115 1

5. A patient underwent a right hemicolectomy. The patient developed a wound abscess which is treated conservatively.

No / Yes 126 1

6. After a high-energy trauma a patient is surgically treated for a crural fracture with step-off of the lower leg with a metal rod. 2 hours after the operation the patient develops a compartment syndrome, which is treated with fasciotomy.

No / Yes 130 1

7. Patient undergoes an aorta valve replacement. During hospitalisation he suffers abdominal pain for which the surgeon performs an ileocaecal resection. During the postoperative l course the patient suffers congestive heart failure due to AF which is treated with medication. The patient dies on the surgery ward.

No / Yes 128 1

8. A patient undergoes a total thyroidectomy. Hypocalcaemia develops postoperatively.

NoYes; Temporary / Yes; permanent

129 13

9. A patient underwent sigmoid resection in another hospital and is transferred with abdominal sepsis. During hospitalisation the patient develops an intra-abdominal abscess which is treated with percutaneous drainage. Which complication do you register?

None / Sepsis / Abd. abscess / Both

126 16

10. A patient undergoes a laparoscopic colectomy. 5 days after discharge from hospital, the patient presents to the emergency department with abdominal pain and is admitted for observation.

NoYes; Temporary

126 13

11. A patient with ‘body packer’ syndrome undergoes a laparotomy during which 13 packets are removed via enterotomy. During the postoperative clinical course the patient develops ileus and is discharged after 22 days.

No / Yes 128 1

12. A day after undergoing daycare laparoscopic cholecystectomy, a patient presents to the emergency department with continuous abdominal pain and vomiting. The patient is diagnosed with choledocholithiasis and admitted to hospital where an ERCP is performed.

No / Yes 126 1

13. After a high-energy trauma a patient is surgically treated for a crural fracture with step-off of the lower leg with a metal rod. During the operation the patient develops compartment syndrome which is treated with fasciotomy.

No / Yes 129 14

14. During an operation to repair an incisional hernia, the patient’s small intestine is accidentally damaged. The defect is repaired immediately during the same operation. The postoperative clinical course is uncomplicated.

No / Yes; temporary / Yes; re-operation

126 134

15. A patient has undergone surgery to remove an adrenal tumour by means of laparotomy. The patient is discharged after one week in good condition. 6 weeks later, the patient presents to the outpatient department with abdominal pain and is readmitted with bowel obstruction due to adhesions.

No / Yes 92 1

16. A patient is admitted and underwent a gastrectomy. It takes 6 days after the operation before gastric emptying occurs.

No / Yes 132 1


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17. Following surgery for acute appendicitis, the patient occupies a ‘borrowed’ bed at the orthopaedics department. During this period the patient suffers morphine toxicity.

No / Yes 129 2

18. During placement of a central venous catheter in a surgical patient in the ICU, the patient develops pneumothorax which requires placement of a thorax drain.

No / Yes 126 2

19. A patient undergoes a PTA (by the interventional radiologist). Following the intervention, a large haematoma develops in the groin at the puncture site.

No / Yes 126 2

20. In the operating theatre, the surgeon performs the ‘time out’. It appears that all equipment is not sterile. The patient is sent back to the ward and is operated on a day later.

No / Yes 130 5

21. A patient is admitted for elective surgery for a fractured ankle. A day before the operation, it is cancelled due to priority being given to more emergency patients.

No / Yes 127 5

22. A patient is admitted for elective surgery for a fractured ankle. A day before the operation, it is cancelled because the ankle is still too swollen.

No / Yes 126 5

23. The patient has undergone a left hemithyroidectomy. After the operation the patient is found to have vocal cord paralysis. What severity do you register for this complication?

Temporary / Permanent 124 3

24. A patient undergoes a hemicolectomy. After 5 days the patient suffers an anastomotic leak for which an ileostomy is constructed. What is the severity of this complication?

Re-op / Permanent 125 3

N.B. The questions are ordered from the highest to the lowest average percentage of ‘yes’ responses per category per case. The example cases were presented to participants in random order.

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RESULTS

General Characteristics of Participants and HospitalsThe number of participants in the seven hospitals was 134. The distribution over different functions and subspecialties is shown in table 2. More than 50% of participants practiced at a university medical centre, almost 40% in a tertiary referral hospital and around 10% in a general training hospital. About 40% of the participants were attending surgeons. The largest subspecialty was gastrointestinal oncology, represented by almost 35%, while 25% of participants indicated not having any specific subspecialty.

Table 2. Participants per hospital

Hospital 1 2 3 4 5 6 7 Total

Number 34 17 14 7 12 17 36 134

Function

Staff member 16 4 4 2 8 5 14 53

Surgical trainee/fellow 4 3 0 0 1 1 3 12

Residents 12 9 7 5 3 6 13 55

Missing data 2 1 3 0 0 2 6 14

Specialty

Gastrointestinal/oncology 14 4 2 0 4 6 16 46

Vascular surgery 8 3 1 0 2 1 3 18

Trauma surgery 5 3 1 3 4 3 3 22

None 5 5 7 4 2 3 10 36

Missing data 2 2 3 0 0 1 4 12

Results per CategoryCategory 1: DefinitionFigure 1 shows the percentage of ‘yes’ responses per hospital in the category ‘Definition’. For 6 out of 16 example cases (fig. 1; cases 1 through 6), the agreement between hospitals was more than 80% on average, whereas agreement in case 16 was below 20%. For some of the other cases, either the variation among hospitals was extremely high, ranging from 9-100% in case 15 (fig. 1), or there was no agreement within hospitals (range 18-58%), as shown for case 13. The highest agreement was found for complications such as post-operative wound infections or anastomotic leaks. The lowest agreement was found in cases with complications that might often directly be related to the surgical procedure, such as gastro paresis after a gastrectomy or ongoing bowel paralysis following adhesiolysis.


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Figure 1. Agreement within and between hospitals: ‘definition’ category.

The questions are ordered from the highest to the lowest average percentage of ‘yes’ responses per case per hospital

Category 2: Complication related to other specialty For 2 out of the 3 cases in this category, cases 17 and 18 (fig. 2) a ‘complication in the ICU’ and ‘complication on a non-surgical nursing ward’, 98% of participants agreed that both cases should be registered as a complication (ranges 70-100% and 83-100%, respectively). On the other hand, a groin haemorrhage following percutaneous intervention by a radiologist (fig, 2: case 19) was reported as a complication with a wide variation ranging from 50-82% of the participants (Figure 2).

Category 3: Severity We also found differences in responses with regard to the severity assigned to a complication (categorical variables not shown in figure 2, table 1: cases 3, 8, 10, 14, 23, 24). A complication that occurs during surgery but that is repaired during that same operation would generally not be registered as complication with severity grade: ‘recovery after (re)operation’ (case 14; average 0%, range 0-14.3%). In 2 cases (3, 14) participants were asked whether a complication would be registered with a severity grade ‘(probably) permanent damage or function loss’. The percentage of participants who judged this as correct varied widely per hospital (17-62% for case 14 and 67-100% for case 3).

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The construction of an intentionally temporary ileostomy performed during a surgical intervention after a complication (case 24) was considered to be registered with severity grade ‘(probably) permanent damage or function loss’ by only 0-41% of the participants.

Category 4: Intra-operative damage Damage to the spleen (requiring splenectomy; fig. 2, case 3), followed by a vaccination programme for the patient, was considered by an average of 95% of participants (range 86-100%) as a complication. However, only 32% of surgeons would register damage during a surgical procedure, such as an accidental intestinal perforation (fig. 2, cases 14) with subsequent closure of the defect, as a complication (range 0-50%).

Category 5: Cancelled operationsWhether or not a cancellation of an operation is registered as a complication varied widely between participants and hospitals. Cancellation for medical reasons (case 22) would be registered as a complication by 0 to 40% of the participants. If the reason for cancellation was identified during the ‘time out’ procedure this percentage was higher; 25-93% (case 20). Operations cancelled due to logistic reasons, for example due to the urgency of other emergency surgery patients (fig. 2, case 21), showed a large variation among hospitals, e.g. (range 8-80%).

Category 6: Serial complications and transfersOn average, more than 70% of the participants would register one or more complications (incl. during the further clinical course) if a patient with complications had been transferred from another hospital (fig. 2, case 9; range 55-86%). Of all participants, an average of 55% would not register existing complications upon admission, but would register any subsequent complications that occurred during hospitalisation in the receiving hospital (not shown). In the cases with serial complications, about half of the participants (range 25-73%) would register all complications during hospitalisation, while the other half (range 27-67%) would register only some of them (not shown).


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Figure 2. Agreement within and between hospitals: other categories.

The questions are ordered from the highest to the lowest average percentage of ‘yes’ responses per category per case per hospital. The cases can appear in more than one category.

Staff versus ResidentsResponses to 19 cases showed no significant differences between staff and residents, whereas three cases (1, 8, 10) did show significant differences in responses. Staff would register a hypocalcaemia after thyroidectomy significantly more often as a complication than residents (case 8; p=0.002), as well as post-discharge abdominal pain after a laparoscopic colectomy (case 10; p=0.015). Finally, residents would register more complications after hemicolectomy (case 1; p<0.001).

DISCUSSION

Despite a uniform definition for surgical complications, the present study showed there is limited consensus both among and within hospitals as to which event should be considered as a complication and should therefore be registered, which is a prerequisite for adequate hospital benchmarking. This is particularly important in the current era of reporting and comparing the quality of healthcare, for example using Hospital Mortality Ratios like the HSMR,12,13 or the national and international complication registrations for heart surgery in adults (LCRHV; www.nvtnet.nl), or the NSQIP.1,5

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The present study showed enormous differences in the use of the current definition of a complication. In order to improve uniform interpretation, three different aspects of the definition might require revision:First, surgeons could consider some results of care to be ‘calculated risks’.14 Based on the findings in this study, a result should be registered as a complication only if this result is undesirable for the patient and negatively affects the patient (e.g. vaccination following accidental splenectomy).6

Second, this study found no consensus as to registering complications related to other specialties. Despite this divergence, working in multidisciplinary teams has become increasingly more important in healthcare17. Some years ago, the report entitled ‘To err is human’ also argued in favour of teamwork, a concept that might be able to prevent a large number of avoidable complications.16 A more consistent registration of all complications is advocated, meaning that all complications developed under the responsibility of the surgical department should be registered, regardless of which specialty is responsible.Third, although complications might indicate something about the results of care, they do not inform about the process or any underlying, unintended incidents.15 Complication registration provides better awareness of the actions of individuals or departments and of trends in complications.17 The definition should therefore be applied as literally as possible, without interpretation or desire for self-protection. Only in retrospect we should consider whether or not the results were avoidable. For such complications we can refer back to the processes.18 Results of a previous study suggest that differences in interpretation of definitions might be more important than the differences in the definition itself.19 Even if the same way of reviewing medical records and definition of complication is used, important differences in complication rates may occur.20 This study describes several cases that call for agreement among surgeons. For example the impact of serial complications should be addressed.21,22 Several studies describe extensive training in the use of the complication registration, resulting in better patient outcomes over time.23,24,25 Educating and training surgeons to familiarise themselves with the definition, and encouraging them to acquire knowledge about national agreements with regard to specific situations may help achieve a more uniform registration.

Strengths and LimitationsThis study used real-life situations from daily clinical practice in one country to show that there are clear judgement differences between surgeons which demonstrate that there is room for improvement in complication registration. Because the choice of clinical problem situations for the cases was arbitrary, some problem situations may well have been left out. However, this would not have changed the main conclusion of the study. For some complications, the discussion remains regarding whether or not they should be


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considered as permanent (e.g. in the case of vocal cord paralysis or an ileostomy intended to be temporary), because it is not known beforehand. For intraoperative complications it is unclear whether these should be considered as a re-operation.26 Finally, one could argue whether the seven participating hospitals were representative of all hospitals in the Netherlands. Nevertheless, the participating hospitals did include a mix of the different hospital types: university medical centres, tertiary and general hospitals.

CONCLUSION

Given the considerable differences in interpretation of the current definition of a complication, it is unlikely that uniform registration of complications is actually possible. This uniformity may be increased by additions to the current definition, by more agreement about specific clinical situations, and by training of surgeons, thereby improving comparisons at both local and national levels. This seems a prerequisite before such data can be used at the public domain and function as one of the parameters for the quality of healthcare.

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REFERENCES

1. Hall BL, Hamilton BH, Richards K, et al. Does Surgical quality improve in the American College of Surgeons National Surgical Quality Improvement Program; An evaluation of all participating hospitals. Ann Surg. 2009;250:363-76.

2. Olden G van, Kaasschieter EG, Steller EJA, et al. Complicatieregistratiesysteem in de Heelkunde. Ned Tijdschr Geneeskd 2008;17:33-5.

3. Veen EJ, Janssen-Heijnen MLG, Bosma E, et al. The accuracy of Complications Documented in a Prospective Complication Registry. J Surg Res 2012;173:54-9.

4. Dishoeck A van, Lingsma HF, Markenbach JP, et al. Random variation and rankability of hospitals using outcome indicators. BMJ Qual Saf 2011;20:869-74.

5. Sanford DE, Woolsey CA, Hall BL, et al. Variations in definition and method of retrieval of complications influence outcome statistics after pancreatoduodenectomy: Comparison of NSQIP with non-NSQIP methods. J am Coll Surg 2014;219:407-15.

6. Sokol DK, Wilson J. What is a surgical complication? World J Surg 2008;32:942-4.

7. Remmelt Veen M, Lardenoye JHP, Kastelein GW, et al. Recording and Classification of Complications in a Surgical Practice. Eur J Surg 1999;165:421-4.

8. Gouma DJ, Obertop H. The registration of complications of medical treatment. [Article in Dutch] Ned Tijdschr Geneeskd 2003;147:1252-5.

9. Kievit J, Jeekel J, Sanders FBM. Complicaties registreren. Landelijke database voor beter inzicht. Medisch Contact 1999;54:1363-5.

10. Goslings JC, Gouma DJ. What is a surgical complication? World J Surg. 2008;32:952.

11. Clavien, PA Sanabria JR, Strasberg SM. Proposed classifications of complications of surgery with examples of utility in

cholecystectomy. Surgery 1992;11:518-526.

12. van den Bosch WF, Silberbusch J, Roozendaal KJ et al. Variations in patient data coding affect hospital standardized mortality ratio (HSMR) [Article in Dutch] Ned Tijdschr Geneeskd. 2010;154:A1189.

13. Mackenzie SJ, Goldmann DA, Perla RJ, Parry GJ. Measuring Hospital- Wide Mortality-Pitfalls and Potential. J Health Qual. 2014. doi: 10.1111/jhq.12080.

14. Wagner C, Wal van der G. Voor een goed begrip. Medisch Contact 2005;60:1888-91.

15. Lerner S, Magrane D, Friedman E. Teaching Teamwork in Medical Education. Mt Sinai J Med 2009;76:318-29.

16. Kohn L, Corrigan J., Donaldson MS. To err is human: Building a Safer Health Care System. Washington, DC: National Academic Press; 2000.

17. Visser A, Ubbink DT, Gouma DJ, et al. Quality of Care and Analyses of Surgical Complications. Dig Surg 2012;29:391-9.

18. Gouma DJ, Laméris HJ, Rauws EA, Busch OR. The centralisation of highly complex operations. [Article in Dutch] Ned Tijdschr Geneeskd 2012;156(32):A4887.

19. Marang-van de Mheen PJ, Hollander EJ, Kievit J. Effects of study methodology on adverse outcome occurence and mortality. Qual Saf Health Care 2007;19:6:399-406.

20. Thomas EJ, Studdert DM, Runiciman WB et al. A comparison of iatrogenic injury studies in Australia and the USA. I: Context, methods, casemix, population, patient and hospital characteristics. Int J Qual Health Care 2000;12:371-8.

21. Clavien PA, Barkun J, de Oliviera ML et al. The Clavien-Dindo classification of surgical complications: five year experience. Ann Surg 2009;250:187-96.

22. Ivanovic J, Seely AJE, Anstee C et al. Measuring surgical quality: Comparison of postoperative adverse events with American College of Surgeons NSQIP


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and the Thoracic Morbidity and Mortality Classification System. J Am Coll Surg. 2014:218;1024-31.

23. Howell AM, Panesar SS, Burns EM, Donaldson LJ, Darzi A. Reducing burden of surgical harm. Ann Surg 2014;259:630-41.

24. Sellers MM, Reinke CE, Kreider S et al. American College of Surgeons NSQIP: Quality In-Training Initiative pilot study. J Am Coll Surg 2013;217:827-32.

25. Zeeshan M, Dembe AE, Seiber EE, Lu B. Incidence of adverse events in an integrated US healthcare system: a retrospective observational study of 82,784 surgical hospitalizations. Patient Saf Surg 2014;8:23

26. Rosenthal R, Hoffmann H, Dwan K, Clavien PA, Bucher HC. Reporting adverse events in surgical trials: Critical appraisal of current practice. World J Surg 2014 DOI 10.1007/s00268-014-2776-8.

‘Golden root butterfly’ was missing its body and the plant root was added to recreate the butterfly’s torso. Anne ten Donkelaar.

Chapter 7

Predictors of Surgical Complications:

A Systematic Review

Annelies VisserBart GeboersDirk J Gouma

J Carel GoslingsDirk T Ubbink

Surgery 2015 Feb 27. pii: S0039-6060(15)00028-8

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ABSTRACT

Background: Surgical complications occur more frequently, are more often preventable, and their consequences can be more severe than other types of complications. Controversy exists how best to identify and predict surgical complications. Several studies on predictive factors for surgical complications focussed on a specific predictor for a specific outcome. In order to develop a reliable tool to identify patients with surgical complications, insight in predictive factors for surgical complications is required.

Methods: We searched all publications addressing predictive factors for the development of surgical complications in adult patients admitted to the gastrointestinal, vascular or general surgery departments. Data were extracted regarding study design, patient characteristics, surgical specialty, types of surgical procedures, types of complications, possible predictors, and associated complication risk increase (expressed as an odds ratio; OR).

Results: The final set of 30 articles yielded a total of 53 predictive factors studied in various settings, surgical specialties, and disorders. To focus our analysis we selected the 25 most robust and clinically applicable factors (i.e. appearing in 3 or more studies). These factors were then categorized into 4 different groups: Patient-related factors, Co-morbidities, Laboratory values, and Surgery-related factors. The most predictive factors for morbidity in these groups were BMI (ORs from 1.80 to 6.30), age (1.02-4.62), ASA classification (1.77-7.10), dyspnea (1.23-1.30), serum creatinine (1.39-2.14), emergency surgery (1.50-2.54) and functional status (1.36-4.07).

Conclusion: This review presents a set of factors predictive of surgical complications for general surgical departments. These easily retrievable factors can and should be validated in the specific patient populations of each hospital.


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INTRODUCTION

About 40% of in-hospital complications are related to surgical procedures, while the incidence of complications is 2 to 4.5 times higher in surgery than in general medicine.1-3 Surgical complications occur more often than other types of complications, are more often preventable, and their consequences are more severe.4 This knowledge charges us with the professional, moral, and ethical duty to minimize the incidence of these surgical complications.5 This task requires accurate information about the incidence of perioperative complications, based on a reliable and comprehensive documentation. Such a complication registry might also help determine which complications are predictable and preventable. Such registries have been introduced by various surgical specialties around the world and are considered essential for an optimum quality of care.6-10 However, controversy exists how best to identify these complications.11 Conventional approaches to identify complications, such as incident reports or voluntary complication reporting, had limited success in the identification of complications,11 while the use of a verbal inventory of complications during handover meetings was also found to be imperfect, identifying only 86% of all surgical complications during hospitalization.12 Besides, it takes costly time from clinicians. Hence, hospitals would benefit from a more effective way to identify complications and to complete their registration. For this purpose a trigger tool, consisting of a set of predictors, or thresholds, might be useful and more efficient to indicate patients at risk of a complication. Patient at risk in the context of a trigger tool should be defined as patients who may have suffered complications during hospitalization.13,14 Trigger tools, comprising a range of predictors, are currently used to check the charts of patients at risk for complications, in order to complete the complication registry database.15,16 As an example, the Institute for Healthcare Improvement (IHI) developed a global trigger tool (GTT).17 This trigger-based chart audit aims to provide a practical tool to identify patients at risk of complications after discharge. However, significant variations of GTT findings appeared to be common15. This may be due to the inter-observer variation among different reviewers when identifying complications or a low detection rate.11,13,18-20

Several studies on predictive factors for surgical complications focused on a specific predictor for a specific outcome, e.g., emergency admission as a predictor for death, or on surgical parameters (e.g. surgery time) as predictors of postoperative cardiac events.21,22 However, academic hospitals are highly sub-specialized but, at least in Europe, this represents only a small part of all surgical care, most of which is conducted in non-academic centres. In order to develop a reliable tool to identify patients at risk for surgical complications after discharge, an overview is needed of all predictive factors for surgical complications in general.

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Hence, the aim of this systematic review was to search the literature for factors that may identify patients who have sustained an in-hospital complication (postoperative morbidity or mortality) in surgical (general, gastro-intestinal and vascular) patients.

METHODS

This systematic review was conducted along the STROBE guidelines.23

Search StrategyThe search strategy was validated by a clinical librarian. We searched for publications addressing predictive factors for the development of surgical complications in adult patients admitted to the gastrointestinal, vascular or general surgery departments (see full search strategy in appendix 1). Surgical complications were defined as any medical adverse outcome occurring between admission and 30 days after surgery.Any retrospective or prospective study design was accepted that focused on prognostic factors for surgical complications. The study results should be presented as ratios for each predictive factor based on multivariable regression analysis. Case reports, abstracts, conference proceedings, and editorials were excluded from the search. Because of the wide search strategy and sensitive search terms we limited the study population to the three surgical specialties mentioned above. This limitation was also useful to find more specific predictive factors for each type of surgery. Depending on the number of predictive factors we would find, we also planned to select only the most robust and clinically applicable factors for further analysis.The Medline and Embase databases were searched from 2000 through March 2013. We did not use the Cochrane database because studies on harm are unlikely to be available in the form of RCTs. Eligible studies were selected and duplicates were removed (figure 1).

Study Selection and AppreciationTitles and abstracts of the studies were judged for eligibility by two reviewers independently. Suitable articles that matched the predefined selection criteria were then obtained in full. The methodological validity of the selected articles was critically appraised by two reviewers independently, using the relevant checklists from the Dutch Cochrane centre (http://dcc.cochrane.org/). In case of disagreement, consensus was reached through discussion among the reviewers.


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Data Extraction and AnalysisData were extracted regarding study design, patient characteristics, surgical specialty, types of surgical procedures, types of complications, possible predictive factors, and associated complication risk increase (odds ratio; OR).The ORs from multivariate regression analyses in the different studies i.e., corrected for other confounders, were used to make general statements per predictive factor. A meta-analysis will be conducted if the patient characteristics and outcomes are homogenous.

RESULTS

Study DescriptionsOur search rendered 648 hits, of which 39 matched our inclusion criteria (Figure 1). Eighteen of these described different predictive factors for overall mortality or morbidity in general surgery patients. General surgery includes all surgical areas; gastro-intestinal, trauma, endocrine, oncology and vascular surgery. No specific sub specialty was described. The remaining 21 articles addressed specific subspecialty, operations, complications or predictive factors. After reading the full-text articles, 9 of these were excluded because no multi-variable analysis was performed, no predictive factors were given, or the article tested an existing trigger tool and did not describe the contribution of individual predictive factors.

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Figure 1: Flow diagram of study inclusion

Records after duplicates removed n = 648

Iden

tific

atio

n Additional records identified through other sources searching in Embase

n = 501

Records identified through database searching

in Medline n = 617

Records excluded on title: not about Gastro-intestinal,

general, vascular, or trauma surgery. n = 568

Scre

enin

g

Records after screening on title and abstract

n = 39

Elig

ibili

ty

Full-text articles excluded: no odds-ratios or predictive

values described n = 9

Incl

uded

Studies included in synthesis

n =30

Records excluded on abstract: not suitable for the

review n = 41

Quantitative meta-analysis

n=0 (Outcome was to heterogeneous)

The final set included 30 articles (Figure 1), comprising 11 articles focusing on general surgery, 9 on gastrointestinal surgery, 5 on vascular surgery and 5 combining general and vascular surgery. Of these, 25 were retrospective and 5 were prospective cohort studies (table 1).


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Table 1. Quality of included studies

Checked item % of studies

1. Clear definition of study population? 100%

2. Exclusion of selection bias? 60%

3. Clear definition of exposure? 77%

4. Clear method to evaluate/assess exposure? 67%

5. Clear definition of outcome? 90%

6. Clear method to evaluate/assess outcome? 63%

7. Outcome determined blind from exposure? 10%

8. Affects this the evaluation of the outcome? 0%

9. Follow-up long enough? 93%

10. Selective lost to follow up excluded? 90%

11. Are confounders described? 93%

All articles were published between 2001 and 2013. Most studies were conducted in the USA (27 studies) and some in Europe (3 studies) or Canada (1 study). Study size varied widely between 226 and 964,263 patients. Most of these studies derived their data from a prospective national database (National Surgical Quality Improvement Program: NSQIP). These data contained preoperative patient characteristics, clinical risk factors, and post-operative outcomes.The 30 articles yielded a total of 53 predictive factors studied in various settings, specialties, and disorders. To focus our analysis we selected the 25 most robust and clinically applicable predictive factors, using two criteria: 1. Cited in three or more articles, 2. The factor is routinely recorded and readily available in hospital databases. The 25 predictive factors were then categorized into 4 different groups: Patient-related factors, Co-morbidities, Laboratory values, and Surgery (procedure)-related factors. Associated complications were described in terms of morbidity and mortality. Because of the expected heterogeneity of the patient characteristics and outcomes we refrained from conducting a meta-analysis.

Methodological QualityOverall, the included studies were judged as valid and of reasonably good quality (Table 1).24-53 In 10% of the studies the outcomes were determined while being unaware of the exposure. In 40% of all studies selection bias could not be excluded as they excluded patient records with missing laboratory values or patient characteristics from their study cohort. Three out of 4 studies described a clear method to evaluate exposure and outcome.

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Outcome VariablesThe 25 predictive factors we studied predicted a wide range of outcomes, e.g. mortality or various morbidities (Appendix 2). Because of the heterogeneity of these results we describe each predictive factor separately for the outcomes morbidity and mortality. The significant ORs found for each predictive factor are summarized in Table 2. Table 2 also shows the number of studies showing significant ORs for each predictive factor versus the total number of studies describing this factor.

Table 2. Predictors per category

Patient-related factors

Number of studies showing significant ORs/ vs number of studies showing non- significant ORs

Odds ratio rangesMortality (N)

Odds ratio rangesMorbidity (N)

Age 15/16 1.03-5.32 (8) 1.02-4.62 (11)

BMI: Obesity 7/7 0.74 (1) 1.80-6.30 (6)

Underweight 3/3 1.40-1.52 (2) 1.90 (1)

Gender: Male 6/6 1.19 (1) 1.15-2.34 (6)

Female 3/3 2.70 (1) 1.40-1.60 (2)

Functional status 7/7 1.43-3.78 (4) 1.36-4.07 (4)

Long term steroid use 6/6 1.30-1.98 (3) 1.31-1.67 (5)

DNR 4/4 1.55-3.09 (4) 1.30 (1)

Smoking 5/6 0.75 (1) 1.19-1.62 (4)

Alcohol abuse 4/4 - 1.15-2.80 (4)

Recent weight loss 4/6 1.65-2.40 (2) 1.61-1.80 (2)

Co-morbidities Number of studies showing significant ORs/ vs number of studies showing non- significant ORs



ASA classification 12/13 1.54-11.60 (4) 1.77-7.10 (8)

Dyspnea 7/8 1.22-6.25 (5) 1.22-1.30 (3)

Previous cardiac intervention/ failure

6/7 1.37-2.87 (3) 1.21-2.00 (5)

Pre-operative sepsis 5/5 2.10-2.97 (3) 1.32-1.99 (3)

COPD 5/5 1.31-1.97 (4) 1.22-1.67 (3)

Ascites 4/4 1.80-3.54 (3) 1.47-1.77 (3)

CVA 3/3 - 1.30-7.63 (3)

Diabetes 4/6 1.37-10.98 (3) 1.84-5.14 (2)

Dialysis 3/3 2.30-5.70 (3) -

Hypertension 3/3 - 1.21-1.70 (3)


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Laboratory values Number of studies showing significant ORs/ vs number of studies showing non- significant ORs

Odds ratio-ranges Mortality (N)


Elevated creatinine 6/6 1.70-1.71 (3) 1.39-2.14 (5)

Pre-operative albumin 5/5 1.33-2.20 (2) 1.13-1.67 (4)

Elevated white blood cell count 4/4 1.82 (1) 1.22-1.40 (3)

Hyponatremia 2/3 1.33 (1) 1.44-1.72 (1)

Surgery-related factors Number of studies showing significant ORs/ vs number of studies showing non- significant ORs



Emergency operation 10/11 1.90-3.33 (5) 1.50-2.54 (7)

Intra-operative transfusion 3/4 2.58 (1) 1.09-2.60 (2)

Increase in operation time 3/3 1.00-2.20 (3)

Numbers of studies showing significant ORs and ranges mortality and morbidity (95% CI)

1. Patient-related FactorsIn this category we found 9 significant factors (Table 2). Most studies described age and body mass index (BMI) as predictive factors (15 and 9 studies, respectively). Both predictive factors showed ORs ranging from 1.03 to 5.32 and from 0.74 to 6.30, respectively.Age Increasing age was found to be a predictive factor in 15 studies.25-37,40,42 High age (with different cut-off values) was related to a higher risk of post-surgical complications, resulting in higher morbidity and higher mortality. In 9 out of the 15 studies a risk increase was found using a cut-off value of higher than 65 years.26,28-30,33,34,36,37,42 BMIBMI as a significant predictive factor for complications was found in nine studies.26,29,32,

33,34,35, 36, 38, 39 Six of these studies found obesity (BMI >30 or >35) predictive of morbidity.26,29,33,34,38,39 On the other hand one study32 found that underweight patients (BMI <18.6) also had a higher risk of experiencing post-surgical morbidity as compared to patients with a normal BMI. Obesity was not a predictive factor for mortality after surgery, while underweight patients did show a higher mortality risk.35,36

2. Co-morbidities In this category we found 10 significant predictive factors (table 2). Most studies described the ASA (American Society of Anaesthesiologists) classification and dyspnea as predictive factors (12 and 7 studies, respectively). Both predictive factors showed ORs (ranging from 1.54 to 11.6 and from 1.22 to 6.25, respectively).

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ASA classification ASA classification was an independent predictive factor of post-surgical morbidity in eight studies.24,25,28,29,30,31,37,44 ASA Class 1 was used as reference in most studies. Morbidity risk increased with an increasing ASA classification. An increasing ASA classification was also an independent predictive factor for mortality in four studies.40-43 DyspneaDyspnea was a significant predictive factor for morbidity in two studies27,37 and even more predictive of post-surgical mortality in 5 studies 27,31,34,35,36 with ORs varying between 1.22 and 6.25, due to differences in type of surgery.

3. Laboratory Values In the category ‘laboratory values’ we found 3 significant predictive factors (Table 2). Six studies described an elevated serum creatinine as a predictive factor.25,27,29,30,33,42 Serum Creatinine ORs for this predictive factor ranged from 1.39 to 1.84, given a cut-off value of >1.5 mg/dl. This seems to predict a cardiac complication in particular, which was found in 4 out of 5 studies investigating serum creatinine as predictive factor for morbidity.25,27,29,30 Three studies found that a high serum creatinine level was also predictive of mortality (using various cut-off values).27,33,42

4. Surgery-related Factors In this category we found 3 significant predictive factors (Table 2). Most studies (n=11) described ’Emergency surgery’ as a predictive factor. Emergency surgeryEmergency surgery was predictive of morbidity in seven general and vascular surgery studies as compared to non-emergent operations, showing a range of ORs from 1.50 to 2.54).26,27,28,29,30,37,41 Patients who underwent emergency surgery also had a higher mortality risk (OR from 1.90-3.33)than non-emergent surgery patients (five studies).27,2934,35,42

DISCUSSION

This systematic review rendered 53 predictive factors predictive of the development of surgical complications (i.e., postoperative morbidity or mortality) in patients undergoing gastrointestinal, general, vascular surgery, in the period up to 30 days after discharge. Of these, 25 were highly associated with surgical complications and readily available from hospital databases. An additional advantage is that most of them indicate conditions already known before admission. This is in contrast with predictive factors associated with demand for care, which are also related to the occurrence of complications but only


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become manifest during hospitalization.54 Some of these 25 patient-related and surgery-

related co-morbidities and laboratory tests may seem obvious potential predictors, but the value of a systematic review is to summarize available evidence. Here, we provide general surgical departments with a complete overview of possible predictors from the literature.Other, non-patient-related factors may also influence the risk of surgical complications, apart from patient-related factors. Studer et al. reviewed surgery-related non-technical risk factors influencing the outcome of surgical care.55 They found working hours, surgeon skills, handoffs, and checklists to be important factors predicting surgical complications. However, these factors are less easy to monitor and incorporate in a trigger tool.Some limitations of our review deserve mentioning. Many of the factors found here were studied for their association with a specific complication rather than any complication. Some studies focused on factors predicting complications for certain specific disorders and may not be indicative for complications in other circumstances. Moreover, the studies in this review applied different cut-off points or slightly different definitions of the exposure or outcome. Hence, we attempted to categorize the different predictive factors to summarize our findings. The huge clinical heterogeneity among the included studies precludes a meta-analysis. However, our systematic review generated and supported the hypothesis that a set of these factors may predict the occurrence of a complication in surgical patients. We selected factors based on the number of publications in which they were used. This does not necessarily mean these are most relevant, but reflects their importance in clinical practice. It should also be noted that 27 out of the 30 studies we found were American, while 25 of these studies retrieved their data from the American College of Surgeons’ national surgical quality improvement program (ACS-NSQIP). This is an outcome-based, risk-adjusted program developed to improve the quality of surgical care in the United States. This validated database provides patient-level information for surgical procedures, is available for research, and provides a source of reliable clinical information.43 In the USA researchers have easy access to this source of information, which may explain why most of the articles we found were of American origin. This might be a potential source of bias. Other countries may have different definitions of health and sickness and preferences regarding diagnoses and treatment differ among both surgeons and patients. Also the facilities within the healthcare systems are likely to differ among countries. This is, however, no reason to assume their findings cannot be extrapolated to other Western countries. The set of factors as found in this systematic review may be useful for hospital care administration managers of general surgery departments to optimize their registration of complications. Despite the current era of analysing subspecialty-driven outcomes and predictive factors, the Dutch Government requires all general departments of surgery to use a general complication registry. The goal of complication registration consists of at

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least three aspects; first to inform the patient about the outcome of a specific surgical intervention in that particular centre, second to review and analyse the data to evaluate the current process of care and therefore the quality of care and third to use this data to develop and implement initiatives to improve the quality of care. Complication registration is an outcome-driven registration. It enables us to review trends in complication frequencies like increasing postoperative infections. These trends should be reviewed and analysed on the higher level of general surgery because the process or actions for improvement can transcend subspecialties. The risk of missing trends by analysing smaller subgroups makes complication registration relevant to the general surgery department. Traditional efforts to scrutinize for complication development comprise voluntary reporting or incident reports of all patients. These methods have often been poorly successful in the detection of complications. Various other risk scoring systems have been introduced to identify surgical complications like ASA (American Society of Anaesthesiology) classification,56 APACHE (Acute Physiology and Chronic Health Evaluation)57 and POSSUM (Psychological and Operative Severity Score for the Enumeration of mortality and morbidity).58 However, these systems also have their limitations,59 including inter-observer variation (ASA), complexity (APPACHE), and overestimation of mortality in lower risk groups (POSSUM). In this review we did not take into account the severity grade of the complications detected, which may also be useful when weighing the impact of predictive factors.60

In this review the odds provided for each factor were only used to select the set of 25 most predictive factors as these may vary depending on the patient mix in which they were assessed. Hence, this set of factors should be validated in a specific patient population of each hospital for significance and cut-off values. The most predictive factors can then be used for the development of a customized trigger tool applicable to the specific patient mix admitted and treated in that hospital. We believe that a trigger tool based on these predictive factors, this patient mix, and these types of surgery might be most effective in helping hospitals to identify patients at risk of surgical complications and to improve the current complication registry data. Further research should clarify the usefulness and external validity of trigger tool as compared to other means of identifying surgical complications in hospitalized patients.

CONCLUSION

This review extracted 25 predictive factors from the literature that are all independently associated with higher surgical complication risks. Although many of the described predictors may be rather obvious, the results give a valuable overview of the literature and


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could be useful in guiding general surgery departments to focus on the right aspects in their search for clinical quality improvement. Most of these factors are easily retrievable from hospital data and can be validated for the case mix of a specific hospital.

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Appendix 1. Search Strategy

Medline(((((“General Surgery”[Mesh]) OR (surgery[tiab] OR surgical[tiab])) AND (safe[tw] OR safety[tw] OR side-effect*[tw] OR undesirable effect*[tw] OR treatment emergent[tw] OR complicatio*[tw] OR adrs[tw] OR (adverse[tw] AND (effect[tw] OR effects[tw] OR reaction[tw] OR reactions[tw] OR event[tw] OR events[tw] OR outcome[tw] OR outcomes[tw]))) AND ((predictor[tw] OR predictive value[tw] OR prediction[tw]) OR (“precipitating factors”[MeSH Terms] OR “precipitating factors”[tw] OR (“trigger tool”[tw])))) AND (quality[tiab])) NOT (child OR children)) NOT (case reports[pt])Embase((((“General Surgery”/) OR (surgery.ti,ab. OR surgical.ti,ab.))AND (safe.tw. OR safety.tw. OR side-effect*.tw. OR undesirable effect*.tw. OR treatment emergent.tw. OR complicatio*.tw. OR adrs.tw. OR (adverse.tw. AND (effect.tw. OR effects.tw. OR reaction.tw. OR reactions.tw. OR event.tw. OR events.tw. OR outcome.tw. OR outcomes.tw.)))AND ((predictor.tw. OR predictive value.tw. OR prediction.tw.) OR ( “precipitating factors”.tw. OR (“trigger tool”.tw.))))AND (quality.ti,ab.) NOT (child/ or (child OR children).tw.) OR (case report [pt])

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Appendix 2. Odds Ratios per Predictor (significant predictors in bold#)Articles with one or more significant OR per predictor

White blood cell count Cut-off Mortality

27. O’Brien Normal (4-10) Mildly low (2-4)Severely low (<2)Mildly high (10-15)Severely high (>15)

Reference1.89 (0.81-1.76)1.97 (0.68-5.67)1.23 (1.03-1.47)1.82 (1.49-2.23)

30. Davenport Preop <2.5 vs 2.5-10Preop >10 vs 2.5-10

31. Dhungel Pre-op WBC (k/ml)

33. Greenblatt ≤8.68.61-10.4>10.4missing

Previous cardiac intervention / failure

Cut-off Mortality

26. Ketherpal

27. O’Brien CHF before surgery 1.40 (1.10-1.78)

30. Davenport CHF<30 days before surgery

34. Kneuertz Previous cardiac surgeryCHF

1.37 (1.03-1.83)1.80 (0.84-3.84)

35. Nafiu Active CHF 2.87 (2.29-3.58)

36. Nelson History of cardiac surgeryHistory of PCIAngina <1 month prior to surgeryMI <1 month prior to surgeryCHF

n/a1.30 (0.98-1.73)1.08 (0.61-1.92)1.15 (0.79-1.66)1.97 (1.47-2.63)

COPD Cut-off Mortality

27. O’Brien Severe COPD history 1.31 (1.10-1.55)

33. Greenblatt COPD 1.97 (1.12-3.45)

34. Kneuertz History of COPD 1.23 (0.82-1.86)

35. Nafiu COPD 1.39 (1.18-1.65)

36. Nelson COPD

CVA Cut-off Mortality

26. Kheterpal Cerebrovascular disease

30. Davenport CVA with neural deficit

31. Dhungel CVA


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Appendix 2. Odds Ratios per Predictor (significant predictors in bold#)Articles with one or more significant OR per predictor

White blood cell count Cut-off Mortality

27. O’Brien Normal (4-10) Mildly low (2-4)Severely low (<2)Mildly high (10-15)Severely high (>15)

Reference1.89 (0.81-1.76)1.97 (0.68-5.67)1.23 (1.03-1.47)1.82 (1.49-2.23)

30. Davenport Preop <2.5 vs 2.5-10Preop >10 vs 2.5-10

31. Dhungel Pre-op WBC (k/ml)

33. Greenblatt ≤8.68.61-10.4>10.4missing

Previous cardiac intervention / failure

Cut-off Mortality

26. Ketherpal

27. O’Brien CHF before surgery 1.40 (1.10-1.78)

30. Davenport CHF<30 days before surgery

34. Kneuertz Previous cardiac surgeryCHF

1.37 (1.03-1.83)1.80 (0.84-3.84)

35. Nafiu Active CHF 2.87 (2.29-3.58)

36. Nelson History of cardiac surgeryHistory of PCIAngina <1 month prior to surgeryMI <1 month prior to surgeryCHF

n/a1.30 (0.98-1.73)1.08 (0.61-1.92)1.15 (0.79-1.66)1.97 (1.47-2.63)

COPD Cut-off Mortality

27. O’Brien Severe COPD history 1.31 (1.10-1.55)

33. Greenblatt COPD 1.97 (1.12-3.45)

34. Kneuertz History of COPD 1.23 (0.82-1.86)

35. Nafiu COPD 1.39 (1.18-1.65)

36. Nelson COPD

CVA Cut-off Mortality

26. Kheterpal Cerebrovascular disease

30. Davenport CVA with neural deficit

31. Dhungel CVA

Morbidity Remarks

Reference1.19 (0.95-1.50)1.16 (0.53-2.54)1.07 (0.96-1.19)1.40 (1.23-1.59)

*1000/mm3

1.69 (0.87-3.30)1.38 (1.20-1.58)

CAE

1.12 (1.02-1.25) k/mlCardiac complications

Reference1.40 (1.12-1.62)1.31 (1.09-1.57)1.14 (0.73-1.76)

*1000/mm3

Morbidity Remarks

2.00 (1.20-3.50) HR Perioperative cardiovascular adverse event (CEA)

1.32 (1.11-1.57)

1.59 (1.30-1.95) CAE

1.21 (1.07-1.38)0.88 (0.52-1.50)

1.34 (1.04-1.72)

1.47 (0.77-2.81)1.38 (0.90-2.11)1.23 (0.86-1.76)Morbidity Remarks

1.22 (1.10-1.35)

1.67 (1.30-2.15)

1.46 (1.22-1.75)

1.18 (0.89-1.51)1.54 (1.12-2.13)

AKABKA

Morbidity Remarks

2.00 (1.30-3.90) Predictive value: Hazard RatioCAE

1.30 (1.08-1.57) CAE

7.63 (1.36-28.6) 1 OR for morbidity and mortality following esophagectomy.Specific complication: deep venous thrombosis.

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Long term steroid use Cut-off Mortality

27. O’Brien Corticosteroid use

32. Giles Long term steroid use 1.30 (1.20-1.60)

33. Greenblatt Steroids Yes

34. Kneuertz Steroid use 1.80 (1.09-2.96)

36. Nelson Steroid use AKASteroid use BKA

1.98 (1.39-2.83)1.54 (1.05-2.24)

37. Neumayer Steroid use

Alcohol Cut-off Mortality

48. Nath Pre-surgical alcohol use 1.10 (0.87-1.39)

27. O’Brien >2 per day for 3 weeks

31. Dhungel Alcohol

37. Neumayer >2 units/day <2 wks before surgery

Ascites Cut-off Mortality

42. Kazaure 1.80 (1.30-2.40)

27. O’Brien 3.54 (2.66-4.72)

30. Davenport

34. Kneuertz 2.05 (1.29-3.24)

Dyspnea Cut-off Mortality

27. O’Brien 1.22 (1.03-1.44)

30. Davenport

31. Dhungel 6.25 (1.85-20.5)

34. Kneuertz At rest 2.38 (1.38-4.11)

35. Nafiu 1.52 (1.25-1.75)

36. Nelson AKABKA

1.42 (1.11-1.83)1.11 (0.84-1.48)

37. Neumayer


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Long term steroid use Cut-off Mortality

27. O’Brien Corticosteroid use

32. Giles Long term steroid use 1.30 (1.20-1.60)

33. Greenblatt Steroids Yes

34. Kneuertz Steroid use 1.80 (1.09-2.96)

36. Nelson Steroid use AKASteroid use BKA

1.98 (1.39-2.83)1.54 (1.05-2.24)

37. Neumayer Steroid use

Alcohol Cut-off Mortality

48. Nath Pre-surgical alcohol use 1.10 (0.87-1.39)

27. O’Brien >2 per day for 3 weeks

31. Dhungel Alcohol

37. Neumayer >2 units/day <2 wks before surgery

Ascites Cut-off Mortality

42. Kazaure 1.80 (1.30-2.40)

27. O’Brien 3.54 (2.66-4.72)

30. Davenport

34. Kneuertz 2.05 (1.29-3.24)

Dyspnea Cut-off Mortality

27. O’Brien 1.22 (1.03-1.44)

30. Davenport

31. Dhungel 6.25 (1.85-20.5)

34. Kneuertz At rest 2.38 (1.38-4.11)

35. Nafiu 1.52 (1.25-1.75)

36. Nelson AKABKA

1.42 (1.11-1.83)1.11 (0.84-1.48)

37. Neumayer

Morbidity Remarks

1.31 (1.18-1.45)

1.80 (1.30-2.60) AAA repair open vs. endovascular

1.67 (1.15-2.43)

1.44 (1.11-1.87) serious0.33 (1.05-1.69) overall

AKABKA

1.39 (1.18-1.63) SSI

Morbidity Remarks

1.98 (1.84-2.13)1.19 (1.03-1.38)1.40 (1.17-1.68)1.15 (1.02-1.31)0.86 (0.69-1.08)1.21 (0.96-1.52)0.94 (0.77-1.16)1.41 (1.11-1.80)

Specific complication:PneumoniaSepsisSeptic shockSuperficial SSIOrgan space SSIDeep incision SSIUTIWound disruption

1.15 (1.02-1.30)

2.77 (1.17-6.05) 1 OR for morbidity and mortality following esophagectomy. Specific complication: respiratory insufficiency

1.17 (1.03-1.32) SSI

Morbidity Remarks

Do not resuscitate patients

1.47 (1.12-1.86)

1.50 (1.13-1.99) CAE

1.77 (1.40-2.34)

Morbidity Remarks

1.30 (1.17-1.44)

1.22 (1.06-1.39) Cardiac adverse events

1.36 (0.97-1.90)

1.23 (1.12-1.35) SSI

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BMI Cut-off Mortality

26. Ketherpal ≥30

29. Crawford ≥35

32. Giles Underweight (<18.6)Normal ReferenceObese 1 (30-35)Obese 2 (35-40)Obese 3 (≥40)

33. Greenblatt <18.518.5-24.9 normal25-29.930.-34.935-39.9≥40

38. Johnson Open: AAA repairBMI > 40BMI 30-35BMI >40Endovascular AAA repair:BMI 30-35

34. Kneuertz UnderweightExtreme Obese

1.03 (0.57-1.87)1.12 (0.79-1.59)

39. Mathur UnderweightNormalOverweightObese

0.52 (0.08-3.27)Reference 1.24 (0.65-2.37)1.83 (0.98-3.46)

35. Nafiu BMIUnderweightNormal weightOver weightObesity class 1 (BMI 30-35)Obesity class 2 (BMI 35-40)Obesity class 3 (BMI >40)

1.02 (1.01-1.04)1.40 (1.20-1.80)10.85 (0.73-1.00)0.76 (0.63-0.85)0.74 (0.63-0.84)1.02 (1.00-1.10)

36. Nelson AKA:UnderweightNormal weightOver weightObesity class 1 (BMI 30-35)Obesity class 2(BMI35-40)Obesity class 3 (BMI >40)BKA

1.52 (1.07-2.15)Reference0.96 (0.74-1.26)0.95 (0.67-1.35)0.91 (0.55-1.50)1.04 (0.6-1.81)n/a


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BMI Cut-off Mortality

26. Ketherpal ≥30

29. Crawford ≥35

32. Giles Underweight (<18.6)Normal ReferenceObese 1 (30-35)Obese 2 (35-40)Obese 3 (≥40)

33. Greenblatt <18.518.5-24.9 normal25-29.930.-34.935-39.9≥40

38. Johnson Open: AAA repairBMI > 40BMI 30-35BMI >40Endovascular AAA repair:BMI 30-35

34. Kneuertz UnderweightExtreme Obese

1.03 (0.57-1.87)1.12 (0.79-1.59)

39. Mathur UnderweightNormalOverweightObese

0.52 (0.08-3.27)Reference 1.24 (0.65-2.37)1.83 (0.98-3.46)

35. Nafiu BMIUnderweightNormal weightOver weightObesity class 1 (BMI 30-35)Obesity class 2 (BMI 35-40)Obesity class 3 (BMI >40)

1.02 (1.01-1.04)1.40 (1.20-1.80)10.85 (0.73-1.00)0.76 (0.63-0.85)0.74 (0.63-0.84)1.02 (1.00-1.10)

36. Nelson AKA:UnderweightNormal weightOver weightObesity class 1 (BMI 30-35)Obesity class 2(BMI35-40)Obesity class 3 (BMI >40)BKA

1.52 (1.07-2.15)Reference0.96 (0.74-1.26)0.95 (0.67-1.35)0.91 (0.55-1.50)1.04 (0.6-1.81)n/a

Morbidity Remarks

1.90 (1.20-3.10) HR CAE

1.80 (1.40-2.20)

1.90 (1.30-2.80)

1.10 (0.90-1.30)1.20 (0.90-1.60)1.20 (0.80-1.70)

AAA open repair vs. endovascular repair

0.69 (0.45-1.07)Reference1.27 (1.10-1.47)1.33 (1.11-1.59)1.40 (1.09-1.80)1.86 (1.36-2.56)

Pancreaticoduodenectomy

6.30 (2.20-18.0)2.40 (1.50-5.30)4.50 (1.10-23.0)

3.10 (1.10-8.10)

AAA repair surgeryRenal complicationsWound complicationsCardiac complications

Wound complication

0.97 (0.77-1.21)1.25 (1.11-1.42)

1.17 (0.67-2.06)Reference0.94 (0.75-1.17)1.24 (1.01-1.55)

AKA BKA

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Creatinine Cut-off Mortality

25. Gupta >1.5 mg/dl

42. Kazaure >1.2 mg/dL 1.70 (1.40-2.0)

27. O’Brien <1.5 mg/dl1.5-2.0 mg/dl2.0-2.5 mg/dl2.5-3.0 mg/dl>3.0 mg/dl

Hemorrhage1.34 (0.94-1.91)1.57 (0.97-2.54)1.18 (0.63-2.21)1.71 (1.20-2.45)

29. Crawford Major complicationsMajor systemic complications

30. Davenport Preoperative ≥1.5 mg/dl

33. Greenblatt ≤1.0 mg/dl (Reference)1.01-1.2 mg/dl>1.2mg/dl

1.42 (1.00-2.03)1.71 (1.12-2.63)

Intra operative transfusion

Cut-off Mortality

26. Ketherpal 1 unit

31. Dhungel DVTSepsisRe-surgery

35. Nafiu 1 unit 2.58 (2.21-3.00)

Diabetes Cut-off Mortality

45. Ata yes

31. Dhungel yes 1.98 (1.37-64.8)

35. Nafiu yes 1.37 (1.12-1.66)

37. Neumayer yes 1.33 (1.22-1.45)

ASA Cut-off Mortality

40. Farhat 12345

0.200.020.020.080.30

41. Glance 12345

Reference2.01 (0.86-3.16)3.83 (2.69-4.97)5.06 (3.92-6.21)6.37 (5.19-7.54)


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Creatinine Cut-off Mortality

25. Gupta >1.5 mg/dl

42. Kazaure >1.2 mg/dL 1.70 (1.40-2.0)

27. O’Brien <1.5 mg/dl1.5-2.0 mg/dl2.0-2.5 mg/dl2.5-3.0 mg/dl>3.0 mg/dl

Hemorrhage1.34 (0.94-1.91)1.57 (0.97-2.54)1.18 (0.63-2.21)1.71 (1.20-2.45)

29. Crawford Major complicationsMajor systemic complications

30. Davenport Preoperative ≥1.5 mg/dl

33. Greenblatt ≤1.0 mg/dl (Reference)1.01-1.2 mg/dl>1.2mg/dl

1.42 (1.00-2.03)1.71 (1.12-2.63)

Intra operative transfusion

Cut-off Mortality

26. Ketherpal 1 unit

31. Dhungel DVTSepsisRe-surgery

35. Nafiu 1 unit 2.58 (2.21-3.00)

Diabetes Cut-off Mortality

45. Ata yes

31. Dhungel yes 1.98 (1.37-64.8)

35. Nafiu yes 1.37 (1.12-1.66)

37. Neumayer yes 1.33 (1.22-1.45)

ASA Cut-off Mortality

40. Farhat 12345

0.200.020.020.080.30

41. Glance 12345

Reference2.01 (0.86-3.16)3.83 (2.69-4.97)5.06 (3.92-6.21)6.37 (5.19-7.54)

Morbidity Remarks

1.84 (1.63-2.09) Myocardial infarction

DNR patients

Reference Sepsis1.14 (0.90-1.44)1.62 (1.18-2.22)2.14 (1.53-2.99)1.83 (1.43-2.33)

Respiratory complications: Cardiac arrest:1.42 (1.22-1.64) 1.00 (0.76-1.32)1.39 (1.11-1.75) 1.28 (0.87-1.88)1.60 (1.23-2.07) 1.52 (1.01-2.29)1.58 (1.31-1.89) 1.56 (1.16-2.08)

1.50 (1.20-1.70)2.00 (1.50-2.70)

After revascularizations Creatinine ≥1.8

1.74 (1.52-1.98) CAE

1.39 (1.22-1.59)1.49 (1.24-1.79)

Pancreas

Morbidity Remarks

2.60 (1.40-4.70) HR CAE

1.14 (1.04-1.25)1.09 (1.02-1.19)1.10 (1.03-1.19)

Morbidity Remarks

1.84 (1.20-2.82) SSI

1.12 (1.09-3.57)1.86 (1.03-3.29)5.14 (1.93-13.2)

SepsisRespiratoryCardiac

SSI

Morbidity Remarks

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24. Gupta 12345

25. Gupta 12345

42. Kazaure 12345

ReferenceReference3.40 (1.60-7.10)7.20 (3.40-15.3)11.6 (4.20-32.2)

28. Veltkamp Serious complications12345Mild complications12345

43. Breitenstein 1 and 23 and 4

Reference1.54

29. Crawford Major complications (ASA 4/5)Major systemic complications (ASA 4/5)

30. Davenport 4-5 vs. 1-2 3 vs. 1-2

31. Dhungel 3

37. Neumayer 1234/5

44. Obeid 45

Hyponatremia Cut-off Mortality

47. Leung Normal: 135-144 mEq/LAny: <135 mEq/LMild: 130-134 mEq/LSevere: < 130 mEq/L

27. O’Brien Normal: >=135Mildley abnormal 130-135Severely abnormal: <135

Ref1.33 (1.10-1.59)1.40 (0.98-2.00)


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24. Gupta 12345

25. Gupta 12345

42. Kazaure 12345

ReferenceReference3.40 (1.60-7.10)7.20 (3.40-15.3)11.6 (4.20-32.2)

28. Veltkamp Serious complications12345Mild complications12345

43. Breitenstein 1 and 23 and 4

Reference1.54

29. Crawford Major complications (ASA 4/5)Major systemic complications (ASA 4/5)

30. Davenport 4-5 vs. 1-2 3 vs. 1-2

31. Dhungel 3

37. Neumayer 1234/5

44. Obeid 45

Hyponatremia Cut-off Mortality

47. Leung Normal: 135-144 mEq/LAny: <135 mEq/LMild: 130-134 mEq/LSevere: < 130 mEq/L

27. O’Brien Normal: >=135Mildley abnormal 130-135Severely abnormal: <135

Ref1.33 (1.10-1.59)1.40 (0.98-2.00)

0.03 (0.02-0.05)0.14 (0.11-0.17)0.54 (0.44-0.67)1.28 (1.04-1.57)Reference

Specifically predictive for respiratory failure

1.01 (0.01-0.02)0.04 (0.03-0.05)0.15 (0.11-0.19)0.39 (0.30-0.49)Reference

Reference1.30 (1.00-1.58)2.10 (1.40-3.10)2.20 (1.00-4.50)3.60 (1.20-11.2)

Reference-2.10 (1.30-3.10)-1.40 (1.10-2.50)

1.40 (1.20-1.70)2.01 (1.60-2.70)

After revascularization

5.80 (4.26-7.90)3.33 (2.49-4.40)

Specific for CAE

1.91 (1.04-3.78) Sepsis/Septic shock

Reference1.56 (1.22-2.01)1.97 (1.52-2.54)1.77 (1.34-2.32)

SSI

3.20 (1.34-11.7)7.10 (3.51-30.3)

Morbidity Remarks

Major coronary event Wound infection Pneumonia

Reference1.44 (1.38-1.50)1.38 (1.32-1.45)1.72 (1.58-1.88)

Reference Reference Reference1.21 (1.14-1.29) 1.24 (1.20-1.28) 1.17 (1.12-1.22)1.20 (1.12-1.29) 1.25 (1.21-1.29) 1.16 (1.10-1.22)1.28 (1.11-1.47) 1.14 (1.05-1.23) 1.23 (1.11-1.36)

RefNSNS

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DNR Cut-off Mortality

42. Kazaure DNR 2.20 (1.80-2.80)

27. O’Brien DNR 2.45 (1.86-3.24)

35. Nafiu DNR 3.09 (2.31-4.14)

36. Nelson DNR 1.55 (1.17-2.06)2.14 (1.44-3.18)

Dialysis Cut-off Mortality

42. Kazaure Dependent 2.40 (1.60-3.70)

29. Crawford Dependent 5.70 (3.80-8.50)

36. Nelson Dependent 2.00 (1.56-2.57)2.30 (1.78-2.97)

Hypertension Cut-off Mortality

26. Kheterpal

33. Greenblatt

34. Kneuertz 1.22 (0.96-1.55)

Smoking Cut-off Mortality

28. Veltkamp Current or stopped < 1 year ago

31. Dhungel Current

36. Nelson Current 0.75 (0.56-0.99) 0.98 90.71-1.36)

34. Kneuertz Current 1.19 (0.90- 1.59)

37. Neumayer Current

Preoperative albumin Cut-off Mortality

33. Greenblatt > 3.4 mg/dl2.8-3.39 mg/dl<2.8 mg/dl

Reference1.33 (1.15-1.52)1.13 (0.91-1.39)

42. Kazaure < 3,5 g/dl 2.20 (1.60- 3.00)

27. O’Brien < 3,5 g/ml

30. Davenport < 3,5 g/ml

37. Neumayer < 3,5 g/ml

Gender Cut-off Mortality

27. O’Brien Female 0.41(0.21-0.79)

29. Crawford Female

32. Giles Female 2.70 (1.40-4.50)

28. Veltkamp Male

30. Davenport Male

33. Greenblatt Male

34. Kneuertz Male 1.21 (0.97-1.51)

44. Obeid Male 1.19 (1.03-1.16)

53. Trencheva Male


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DNR Cut-off Mortality

42. Kazaure DNR 2.20 (1.80-2.80)

27. O’Brien DNR 2.45 (1.86-3.24)

35. Nafiu DNR 3.09 (2.31-4.14)

36. Nelson DNR 1.55 (1.17-2.06)2.14 (1.44-3.18)

Dialysis Cut-off Mortality

42. Kazaure Dependent 2.40 (1.60-3.70)

29. Crawford Dependent 5.70 (3.80-8.50)

36. Nelson Dependent 2.00 (1.56-2.57)2.30 (1.78-2.97)

Hypertension Cut-off Mortality

26. Kheterpal

33. Greenblatt

34. Kneuertz 1.22 (0.96-1.55)

Smoking Cut-off Mortality

28. Veltkamp Current or stopped < 1 year ago

31. Dhungel Current

36. Nelson Current 0.75 (0.56-0.99) 0.98 90.71-1.36)

34. Kneuertz Current 1.19 (0.90- 1.59)

37. Neumayer Current

Preoperative albumin Cut-off Mortality

33. Greenblatt > 3.4 mg/dl2.8-3.39 mg/dl<2.8 mg/dl

Reference1.33 (1.15-1.52)1.13 (0.91-1.39)

42. Kazaure < 3,5 g/dl 2.20 (1.60- 3.00)

27. O’Brien < 3,5 g/ml

30. Davenport < 3,5 g/ml

37. Neumayer < 3,5 g/ml

Gender Cut-off Mortality

27. O’Brien Female 0.41(0.21-0.79)

29. Crawford Female

32. Giles Female 2.70 (1.40-4.50)

28. Veltkamp Male

30. Davenport Male

33. Greenblatt Male

34. Kneuertz Male 1.21 (0.97-1.51)

44. Obeid Male 1.19 (1.03-1.16)

53. Trencheva Male

Morbidity Remarks

1.30 (1.17-1.44)

AKABKA

Morbidity Remarks

In no-DNR patients

Lower extremity revascularizations

AKABKA

Morbidity Remarks

1.70 (1.00-2.90) CAE

1.60 (1.13-2.27) After pancreaticoduodenectomy

1.12 (1.03-1.22) HPB surgery

Morbidity Remarks

1.30 (1.00-1.70) Serious morbidity

1.62 (1.01-2.57) Respiratory complications after esophagectomy

AKABKA

1.19 (1.08-1.32) HPB surgery

1.23 (1.14-1.32)

Morbidity Remarks

Reference1.52 (1.06-2.19)1.67 (1.01-2.75)


Respiratory complications after esophagectomy

1.13 (1.04-1.22) SSI

1.18 (1.03-1.36) CAE

1.13 (1.04-1.22) SSI

Morbidity Remarks

1.02 (1.02-1.03)

1.40 (1.20-1.70) Lower extremity revascularization

1.30 (1.10-1.50)1.60 (1.01-2.20)

Overall morbidity after AAA repairSSI after AAA repair

1.40 (1.10-1.80)

1.26 (1.03-1.53) CAE

1.15 (1.02-1.29) Pancreaticoduodenectomy

1.16 (1.07-1.26)

1.19 (1.03-1.16) OR voor Clavien classes 4 or 5

2.34 (1.11-5.19) Anastomotic leakage in colorectal surgery

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Recent weight loss Cut-off Mortality

27. O’Brien > 10% last 6 months 1.65 (1.36-1.99)

28. Veltkamp > 10 kilo in 3 months

30. Davenport > 10%

35. Nafiu Undefined 2.40 (1.82-3.18)

Pre-operative Sepsis Cut-off Mortality

24. Gupta

42.Kazaure 2.20 (1.60-3.00)

30. Davenport

34. Kneuertz 2.97 (2.02-4.37)

36. Nelson 1.69 (1.36-2.09) 2.09 (1.63-2.69)

Functional status Cut-off Mortality

24. Gupta IndependentTotally dependentPartially dependent


27. O’Brien IndependentDependent

Reference1.44 (1.22-1.69)

29. Crawford IndependentDependent

Reference2.30 (1.60-3.40)

33. Greenblatt IndependentPartially or totally independent

Reference1.64 (0.82-3.26)

35. Nafiu IndependentDependent

Reference3.78 (3.27-4.48)

36. Nelson Independent Partially dependentAKABKATotally dependentAKABKA

Reference

1.11 (0.82-1.51)1.68 (1.26-2.23)

2.42 (1.78-3.30)2.15 (1.50-3.10)

Increased operation time Cut-off Mortality

45. Ata Every 10 minute increase

26. Kheterpal Operative duration > 3.8 hour

31. Dhungel Prolonged operative time


CH

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131

Recent weight loss Cut-off Mortality

27. O’Brien > 10% last 6 months 1.65 (1.36-1.99)

28. Veltkamp > 10 kilo in 3 months

30. Davenport > 10%

35. Nafiu Undefined 2.40 (1.82-3.18)

Pre-operative Sepsis Cut-off Mortality

24. Gupta

42.Kazaure 2.20 (1.60-3.00)

30. Davenport

34. Kneuertz 2.97 (2.02-4.37)

36. Nelson 1.69 (1.36-2.09) 2.09 (1.63-2.69)

Functional status Cut-off Mortality



27. O’Brien IndependentDependent

Reference1.44 (1.22-1.69)

29. Crawford IndependentDependent

Reference2.30 (1.60-3.40)

33. Greenblatt IndependentPartially or totally independent

Reference1.64 (0.82-3.26)

35. Nafiu IndependentDependent

Reference3.78 (3.27-4.48)

36. Nelson Independent Partially dependentAKABKATotally dependentAKABKA

Reference

1.11 (0.82-1.51)1.68 (1.26-2.23)

2.42 (1.78-3.30)2.15 (1.50-3.10)

Increased operation time Cut-off Mortality

45. Ata Every 10 minute increase

26. Kheterpal Operative duration > 3.8 hour

31. Dhungel Prolonged operative time

Morbidity Remarks

1.37 (1.21-1.55)

1.80 (1.10-3.10)1.60 (0.90-2.80)

Major complicationMinor complication

1.61 (1.33-1.93) CAE

Elderly vascular patients

Morbidity Remarks

1.32 (1.16-1.49) Referencee Group: Preoperative systemic inflammatory response syndrome

In patients with DNR status: 1.8(1.5-2.2)

1.56 (1.27-1.92) CAE


AKABKA

Morbidity Remarks

Reference4.07(3.68-4.51)2.16(1.98-2.34)

Post -operative respiratory failure

Reference2.79 (2.36-3.30)1.92 (1.65-2.23)

CAE

1.36 (1.23-1.51)

2.00 (1.70-2.40)



AKABKA

Morbidity Remarks

1.06 (1.04-1.08)

2.20 (1.30-3.70) CAE

1.00 (1.00-1.01) CAE

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Emergency operation Cut-off Mortality

41. Glance Emergency surgery

42. Kazaure 1.90 (1.40-2.60)

26. Kheterpal

27. O’Brien 2.33 (1.99-2.73)

28. Veltkamp

29. Crawford 2.90 (1.70-4.80)

30. Davenport

34. Kneuertz 1.94 (1.20-3.12)

35. Nafiu 3.33 (2.86-3.89)

37. Neumayer

Age Cut-off Mortality

40. Fahrat 1.04

25. Gupta Per year of increase

42. Kazaure 65-79=>80

1.66 (1.2-2.1)2.0 (1.6-2.6)

26. Kheterpal > 68

27. O’Brien vague 1.05 (1.04-1.05)

28. Veltkamp <4040-6970-79>79

29. Crawford > 80 2.6 (1.7-4.8)

30. Davenport < 40 40-65>65

31. Dhungel vague 1.08 (1.02-1.16)

32. Giles Per decade 1.30 (1.20-1.60)

33. Greenblatt < 5050-5960-6970-79>80

Reference1.29 (0.64-2.16)0.98 (0.81-1.18)1.13 (0.94-1.37)1.39 (1.10-1.75)

34. Kneurtz <6565-7475-84>85

Reference1.89 (1.43-2.45)3.06 (2.26-4.14)5.32 (3.14-9.01)

35. Nafiu Vague 1.03 (1.02-1.04)


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Emergency operation Cut-off Mortality

41. Glance Emergency surgery

42. Kazaure 1.90 (1.40-2.60)

26. Kheterpal

27. O’Brien 2.33 (1.99-2.73)

28. Veltkamp

29. Crawford 2.90 (1.70-4.80)

30. Davenport

34. Kneuertz 1.94 (1.20-3.12)

35. Nafiu 3.33 (2.86-3.89)

37. Neumayer

Age Cut-off Mortality

40. Fahrat 1.04

25. Gupta Per year of increase

42. Kazaure 65-79=>80

1.66 (1.2-2.1)2.0 (1.6-2.6)

26. Kheterpal > 68

27. O’Brien vague 1.05 (1.04-1.05)

28. Veltkamp <4040-6970-79>79

29. Crawford > 80 2.6 (1.7-4.8)

30. Davenport < 40 40-65>65

31. Dhungel vague 1.08 (1.02-1.16)

32. Giles Per decade 1.30 (1.20-1.60)

33. Greenblatt < 5050-5960-6970-79>80

Reference1.29 (0.64-2.16)0.98 (0.81-1.18)1.13 (0.94-1.37)1.39 (1.10-1.75)

34. Kneurtz <6565-7475-84>85

Reference1.89 (1.43-2.45)3.06 (2.26-4.14)5.32 (3.14-9.01)

35. Nafiu Vague 1.03 (1.02-1.04)

Morbidity Remarks

2.54 No range

NS in DNR patients

2.20 (1.20- 4.10) CAE

1.93 (1.75-2.13)

1.90 (1.40-2.50)

2.50 (1.90-3.20) Lower extremity revascularizations

1.71 (1.46-2.01) CAE

1.18 (0.92-1.52)

1.50 (1.35-1.67)

Morbidity Remarks

1.02 (1.01-1.02) CAE

Age in DNR patients

2.30 (1.40-3.80) CAE

1.02 (1.02-1.03)

Reference 1.70 (1.30-2.40)2.20 (1.50-3.30)1.50 (0.90-2.70)

1.70 (1.30-2.20) Infra-inguinal bypass surgery

Reference2.74 (1.48-5.05)4.62 (2.50-8.52)

CAE

1.03 (1.00-1.07) Esophagectomy

1.10 (1.10-1.20) AAA repair

Reference1.29 (0.64-2.16)1.07 (0.54-2.10)1.54 (0.79-3.01)1.93 (0.93-4.01)


Reference1.10 (0.99-1.21)1.29 (1.15-1.46)1.16 (1.07-1.26)

HPB surgery


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36. Nelson <6060-69

70-79

>80

Reference1.14 (0.81-1.62) AKA1.75 (1.22-2.53) BKA1.44 (1.03-4.02) AKA2.94 (2.04-4.23) BKA1.98 (1.41-4.78) AKA3.56 (2.39-5.29) BKA

37. Neumayer < 40>40

HR hazard ratio CHF congestive heart failure, PCI percutaneous coronary intervention, MI myocard infection, CAE cardiovascular adverse event, COPD chronic obstructive pulmonary disease, CVA cerebro vascular accident, AKA above-knee amputation, BKA below-knee amputation, SSI surgical site infection, UTI urinary tract infection, AAA abdominal aortic aneurysm, ASA American Society of Anesthesiology classification, DVT deep vein trombosis HPB hepatopancreatobiliary# Articles without signicant ORs (46,49,50,51,52) are not shown


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36. Nelson <6060-69

70-79

>80

Reference1.14 (0.81-1.62) AKA1.75 (1.22-2.53) BKA1.44 (1.03-4.02) AKA2.94 (2.04-4.23) BKA1.98 (1.41-4.78) AKA3.56 (2.39-5.29) BKA

37. Neumayer < 40>40

HR hazard ratio CHF congestive heart failure, PCI percutaneous coronary intervention, MI myocard infection, CAE cardiovascular adverse event, COPD chronic obstructive pulmonary disease, CVA cerebro vascular accident, AKA above-knee amputation, BKA below-knee amputation, SSI surgical site infection, UTI urinary tract infection, AAA abdominal aortic aneurysm, ASA American Society of Anesthesiology classification, DVT deep vein trombosis HPB hepatopancreatobiliary# Articles without signicant ORs (46,49,50,51,52) are not shown

Reference1.24 (1.07-1.44)

SSI

‘Blue spinner’s’ body was recreated from a twig wrapped with blue thread. A few threads are still hanging loose almost as though the butterfly is slowly unwinding and breaking free from its cocoon. Anne ten Donkelaar.

Chapter 8

Trigger Tool versus Standardized Clinical Registry

to Identify Surgical Complications

Annelies VisserAnnelijn E Slaman

Corrie M van LeijenDirk J Gouma

J Carel GoslingsDirk T Ubbink

Submitted

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ABSTRACT

Background: Traditionally, registering complications after surgery is based on voluntary reporting or incident reports. These methods may fail to detect the total number of complications. A trigger tool was developed to detect complications in hospitalized surgical patients. In this diagnostic study we compared its sensitivity, specificity and time required with the standard standardized clinical registry method by surgical staff and residents.

Methods: A set of 31 potential triggers was chosen based on a systematic review and availability in hospital databases. The trigger tool was developed using multivariable regression and ROC analyses. A reference standard consisted of 300 patients; 150 with and 150 without complications. Sensitivity and specificity of the trigger tool and standardized clinical registry were determined. The time surgeons, residents and the data manager spent on complication registration was calculated for each complication detection method.

Results: The final trigger tool consisted of 9 triggers. Sensitivities of the trigger tool and standardized clinical registry were 70.7% vs. 78.7%, respectively, while specificities were 70% vs. 100.0%, respectively. Sensitivity values to detect major complications were 97.2% vs. 80.6%, respectively.Total time staff and residents spent using the standardized clinical registry was 429 hours per year. Using the trigger tool, the database manager would spend 310 hours per year.

Conclusion: The proposed trigger tool to detect surgical patients with complications appeared as accurate as a standardized clinical registry and even more accurate to detect major complications, while saving surgeons’ time.


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INTRODUCTION

Registration of surgical complications is important to assess and improve quality of surgical care.1 Also, analysing surgical complication registration outcomes can and should lead to improved patient outcomes.2 Despite widespread acknowledgement that complications should be reduced, controversy exists how to detect and record these complications.3

Traditional efforts to detect complications have focused on voluntary reporting or incident reports4. These methods have often been poorly successful in the detection of complications.3 For example, registration during verbal hand-off meetings yields a registration rate of only 86% of all complications.5 Moreover, to achieve adequate reporting of complications a sufficient number and diversity of surgeons should participate the daily verbal hand-off meetings, but this is time-consuming for highly qualified surgeons. Hence, hospitals would benefit from a more effective way to identify complications and to complete their registration.Various risk scoring models have been introduced as a prediction tool for postoperative complications and to guide the care of high-risk patients.6 For example the ASA (American Society of Anaesthesiology) classification,7 the APACHE (Acute Physiology and Chronic Health Evaluation) score,8 and POSSUM (Psychological and Operative Severity Score for the Enumeration of mortality and morbidity).9 However, these systems have their limitations, including inter-observer variation (ASA), complexity (APACHE), and overestimation of mortality in lower risk groups (POSSUM).6 Because of these limitations Donati et al.

developed another,10 more practical model to assess operative mortality risk, based on the ASA-classification. However, this model does not include postoperative complications.Another attempt to design a more uniform, practical, and efficient complication registration method came from the Institute for Healthcare Improvement (IHI), who developed the global trigger tool (GTT).4 A ‘trigger’ can be defined as a specific factor that is derived from the patient’s medical record and is associated with an increased risk for complications. These factors can be patient-specific (e.g. lab results, BMI), surgical procedure-specific (e.g., complexity of the procedure), or hospitalization-specific (e.g., length of hospital stay). A ‘trigger tool’ is a set of triggers that identifies patients who are likely to have suffered a complication and thereby indicates which patient records should be checked for complications, for instance by a data manager.The benefits of (some form of) the GTT to detect complications have been studied in terms of inter-rate reliability among different reviewers on reporting complications.3,11-17 Two studies showed a high specificity (92.0% and 99.0%), but low sensitivity (23.0% and 28.0%).18,19 The high specificity means that the methods could be used to replace expensive manual chart reviews because less ‘falsely positive’ charts need to be checked. However, in order not to miss any complications, the sensitivity of the method should also be high.

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The aim of this study was to develop a new trigger tool to assess the accuracy, usefulness and efficiency compared with the standardized clinical registry.

METHODS

Patients & SettingThis study comprised a model development and diagnostic accuracy study, based on a one-year sample of hospitalized surgical patients. The study was performed at the department of Surgery of a tertiary referral university hospital in Amsterdam. All patients (n=4534) above the age of 17 admitted to, or operated by a surgeon from this department between July 2012 and June 2013 were included in this study. This surgical department provides general, gastrointestinal, hepatopancreatobiliary, vascular, and trauma surgical services.

Standardized Clinical Registry of ComplicationsCurrently, surgical residents collect preoperative, intraoperative, and postoperative data for each surgical patient real-time. The attending staff may supplement the complications identified by the residents. Subsequently the database manager reviews the charts of the patients identified with a complication for possible additional complications. This process is defined here as the ‘standardized clinical registry’ (SCR).All complications are registered and categorized by severity based on the Clavien-Dindo classification in the departments’ complication database.20 A ‘complication’ is defined according to national and international standards as ‘an unintended and unwanted outcome or state during medical care that is so harmful to the patients’ health that it requires (adjustment of) treatment or leads to permanent damage’.21 Complications that occur after discharge are not registered unless the patient is readmitted within 30 days after discharge. Development of the Trigger Tool A set of potentially relevant triggers was chosen based on a) a previous systematic review of the literature in which these triggers were found to be significantly associated with surgical complications (data not shown), b) questionnaires containing the potential triggers found in the literature answered by 12 surgeons within our hospital to validate or supplement this set of potential triggers and c) availability of the trigger in electronic hospital databases. Correctness of the department’s database on complications was monitored by two investigators. Univariable logistic regression analysis was performed to find triggers that occurred substantially more often in the group with complications in the departments’ complication database. Variables with p<0.20 were entered into a stepwise multivariable logistic regression analysis to find significant independent triggers (p<0.05). This


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association was expressed as their Odds Ratio (OR), 95% confidence intervals and p-values. The optimum cut-off values were set to be more specific but less sensitive using univariable analysis in order to avoid that too many records needed to be checked to find complications.

Reference Standard In order to validate and compare the trigger tool, a reference standard was formed by a random sample of 150 records from the departments’ complication database in the same study period with at least one verified complication and another 150 records without a complication. The medical files were reviewed by two investigators (AES and CMvL). If the investigators did find a complication in the latter 150 records, this record was discarded and added to the records with complications. In case of uncertainties interpreting the texts of the resources about complications, the investigators consulted each other or their supervisors. This procedure was continued until the group without complications also contained 150 verified records.

Validation of the Trigger ToolThe independent triggers from the departments’ complication database were subsequently entered in another multivariable analysis, now using the reference standard in order to check their validity. Triggers were kept in the model if they again contributed significantly to the model. If not, we decided to remove the trigger from the model unless the trigger had a low incidence (<10 patients) in the reference standard and was a significant factor in the univariable analysis. The remaining independent triggers formed the final trigger tool.

Comparing the Standardized Clinical Registry with the Trigger ToolThe SCR and trigger tool were compared with the reference standard to calculate their sensitivity and specificity as to the detection of one or more complications and the severity, type, and number of complications registered.

Time and Costs of Trigger Tool versus Standardized Clinical Registry MethodTime spent on the SCR and trigger tool methods were calculated based on the cumulative time spent to report complications by the attending surgical staff attending the handoff meetings. This was clocked by an independent observer during 6 randomly chosen handoffs. The time spent was expressed in hours. The numbers of cases in the patient cohort that should be checked by the database manager based on either the SCR or the trigger tool results were calculated. Furthermore the time spent on the SCR during the morning hand-off was measured and expressed in hours of the attendants. We based the hour calculation on an attendance of 60% of the total staff and residents at the morning hand-off and a contract of 40 hours a week.

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All statistical analyses were performed using IBM SPSS Statistics v.20 (IBM, Armonk, NY, USA).

RESULTS

Patients & SettingA total of 4534 patients admitted to the hospital between June 2012 and July 2013 were included in this study. Their mean age was 55 years (range 18-99). Of these, 2529 (55.8%) were men and 2520 patients (55.6%) underwent operative treatment. In 795 of the 4534 (17.5%) patient records, one or more complications were documented in the departments’ database.

Development of the Trigger ToolThe systematic review provided 26 potential triggers that were significantly associated with the occurrence of surgical complications (data not shown). The inventory among the hospital’s surgeons yielded 8 additional potential triggers (Figure 1). This led to a total of 34 potential triggers for data collection. Of these, 31 were readily available from hospital databases (Appendix 1). Univariable analysis found 23 out of 31 potential triggers with a p<0.20 (Table 1).


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Figure 1. Flow chart of the triggers eventually included in the trigger tool.

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Table 1. Outcome of univariable analysis of variables possibly associated with complications, expressed as p-values and 95% confidence intervals (CI). Significant values are printed in bold.

Variables p-value 95% CI NRetrieved from the systematic reviewSex 0.090 0.749 – 1.021 4534Age (years) <0.001 1.007 – 1.011 4534BMI 0.678 0.984 – 1.026 2154ASA-score <0.001 1.239 – 1.678 2141MET-score 0.963 0.945 – 1.062 1643Emergency procedure <0.001 1.873 – 2.650 4534Urgency code at moment of admission 0.298 0.881 – 1.511 4534Highest urgency code in admission period <0.001 1.470 – 1.742 4534Time required above the scheduled procedure time <0.001 1.007 – 1.011 2254DNR <0.001 0.309 – 0.464 4168Smoking 0.220 0.912 – 1.491 1956COPD/Asthma/Emphysema 0.003 1.148 – 1.990 2209Hypertension 0.230 0.920 – 1.412 2288Increased serum creatinine 0.666 0.734 – 1.218 1856Hyponatremia 0.098 0.952 – 1.792 1427Hypernatremia 0.769 0.417 – 3.263 1427Sodium level outside reference range 0.091 0.959 – 1.773 1427Increased leukocyte count 0.015 1.062 – 1.757 1556Decreased serum albumin <0.001 2.811 – 7.382 665Use of corticosteroids 0.188 0.919 – 1.535 4534Active alcohol abuse 0.737 0.852 – 1.120 2066Retrieved from the inventory among surgeonsSurgical procedure (yes/no) <0.001 1.966 – 2.747 4534oesophageal resection <0.001 4.397 – 11.171 4534Whipple procedure <0.001 6.451 – 18.328 4534AAAA <0.001 4.574 – 22.513 4534Multi-trauma patient <0.001 1.629 – 5.538 4534Length of stay <0.001 7.350 – 10.815 4534Admission to ICU <0.001 4.290 – 6.396 4534Reoperation <0.001 10.917 – 18.540 4534Increased C-reactive protein 0.035 1.030 – 2.270 980Complexity of procedure <0.001 1.139 – 1.240 2520

Abbreviations: BMI: Body Mass Index, ASA-score: American Society of Anaesthesiology score, MET-score: Fitness score based on anaesthesiology questionnaire, DNR: do not resuscitate, COPD: chronic obstructive pulmonary disease, AAAA: acute (or ruptured) abdominal aortic aneurysm, ICU: intensive care unit. Cut-off values: serum creatinine: women >95µmol/L, men >110 µmol/L, Hyponatremia: <135 mmol/L, leukocyte count >10.5x109 cells/L, serum albumin <35g/L, use of corticosteroids in 42 days before hospitalization, multi-trauma patient: Injury Severity Score (ISS) >16, increased C-reactive protein >5 mg/L, sodium level outside reference range: <135 mmol/L or >145 mmol/L.


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Next, multiple models were constructed by means of multivariable analysis, since surgical procedure-specific triggers interacted with other potential triggers (e.g. ‘oesophageal resection’ with ‘ICU-stay’) and to differentiate between patients with and without a surgical procedure during hospitalization. This resulted in 11 independent triggers, 4 of which were continuous variables: length of hospital stay, extension of standard surgical procedure time, complexity of procedure, and age. Their cut-off values were set to: length of stay ≥14 days, technical complexity of procedure ≥ class 6, age ≥85 years, and time required above the scheduled procedure time ≥110 minutes. Table 2 shows the results of the trigger tool with the 11 potential triggers again tested in the multivariable analysis, now dichotomized based on these cut-off values.

Table 2. Multivariable analysis using the departments’ databaseTrigger p-value OR Lower 95% CI Upper 95% CIModel 1Length of stay ≥14 days <0.001 4.948 3.754 6.523DNR <0.001 2.177 1.501 3.155Reoperation <0.001 7.755 5.384 11.168Whipple procedure <0.001 8.201 4.494 14.964AAAA <0.001 8.913 1.995 39.816Oesophagus resection <0.001 4.906 2.818 8.542Age ≥85 years 0.009 2.944 1.237 7.005Time required above the scheduled procedure time≥110 minutes

<0.001 3.660 2.407 5.562

Model 2DNR <0.001 2.937 2.166 3.982Time required above the scheduled procedure time≥110minutes

<0.001 4.731 3.307 6.767

Complexity of surgery <0.001 2.007 1.578 2.552Urgency operation <0.001 1.613 1.290 2.016

Model 3Length of stay ≥14 days <0.001 6.399 5.208 7.863ICU-stay <0.001 2.796 2.226 3.512DNR <0.001 2.256 1.799 2.830

Abbreviations: DNR: do not resuscitate, AAAA: acute (or ruptured) abdominal aortic aneurysm, ICU: intensive care unit.

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Validation of the Trigger ToolThe multivariable analysis, now using the reference standard, showed 6 out of the 11 potential triggers to be significant (Table 3).

Table 3. Multivariable analysis using reference standard

Trigger p-value OR Lower 95% CI Upper 95% CI

Model 1

Length of stay ≥14 days <0.001 35.139 8.253 149.616

DNR 0.010 0.352 0.159 0.777

Reoperation 0.008 16.379 2.056 130.483

Model 2

DNR 0.001 0.086 0.019 0.382

Complexity of surgery ≥6 0.001 4.273 1.776 10.285

Urgency operation 0.073 1.849 0.945 3.616

Model 3

Length of stay ≥14 days <0.001 38.016 8.953 161.423

ICU-stay 0.012 3.675 1.327 10.177

DNR 0.002 0.294 0.136 0.634

Abbreviations: DNR: do not resuscitate, ICU: intensive care unit

The triggers ‘time required above the scheduled procedure time ≥110 minutes’ and ‘age ≥85’ were found not to be significantly associated with the presence of complications (p>0.20). The incidence of 3 potential triggers was low, but these triggers were significant in the univariable analysis. Therefore, these triggers were nevertheless included in the trigger tool (i.e., oesophagectomy (n=6), Whipple procedure (i.e., pancreatoduodenectomy; n=7), and abdominal aortic aneurysm surgical procedure (n=6). Thus, 9 significant independent triggers were included in the final trigger tool, containing: emergency procedure, complexity of surgical procedure above class 6, Do Not Resuscitate policy (DNR), ICU-stay, length of hospital stay of more than 14 days, reoperation, oesophagectomy, Whipple procedure, acute (or ruptured) abdominal aortic aneurysm surgical procedure. ROC-curve analysis was subsequently performed to determine the number of positive triggers needed to detect complications most accurately. The trigger tool performed best already if one out of the nine triggers in the trigger tool would be present (sensitivity 70.7%, specificity 70.0%; AUC 0.764, Figure 2).


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Figure 2. ROC-curve of the number of positive triggers needed to detect complications

Comparing Standardized Clinical Registry with Trigger ToolPatient RecordsThe sensitivity values of the SCR and trigger tool methods to detect complications as compared to the reference standard were 78.7% (118/150) and 70.7% (106/150), respectively, while specificity values were 100% (150/150) and 70.0% (105/150), respectively. Hence, the verbal method would miss 21.3% of the records with complications, while the trigger method would miss 29.7% (table 3).The sensitivity to detect records with major complications (severity grade ≥ 2; re-operation,) was higher for the trigger tool than for the SCR; 97.2% and 80.6%, respectively (Table 4). If a combination of the trigger tool and the SCR was used, 138 out of the 150 records with complications would be detected (sensitivity 92.0%).

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Table 4. Standardized clinical registry versus trigger tool; overview of study results

Completeness Standardized clinical registry

Trigger tool

Reference standard1

Complicated records (sensitivity) 78.7 70.7

Complicated records with highest severity (sensitivity)

≥ severity grade 2 80.6 97.2

≥ severity grade 3 91.7 100

≥ severity grade 4 83.3 100

Efficiency Standardized clinical registry

Trigger tool

Departments’ database2

Check records by Database Manager (%) 17.5 41.5

Hours spent:

Database manager 133 310

Surgeons and residents3 296 0

Total 429 310

1.N=3002. N=45343. Based on 20 attendants during morning hand-off

Missed ComplicationsThe SCR missed 31 records with one or more complications; the trigger tool missed 45 records, which means a total of 71 missed complications using the SCR vs. 53 using the trigger tool. All complications missed by the trigger tool were minor complications (severity grade<2), especially wound problems. The SCR also missed mainly minor complications, but these were categorized as functional disturbance (i.e. hypertension or electrolyte derailment). Two severe complications were also missed by the SCR (re-operation and death).

Time and Costs of Trigger Tool versus Standardized Clinical Registry MethodStandardized Clinical RegistryThe mean duration of the SCR during the morning handoffs was 5 minutes and 42 seconds. These were performed 3 times a week, i.e., 17.1 minutes per week. This equals 889 minutes, i.e., 14.8 hours per year. In our hospital 60% of the surgeons and residents (i.e., 20 persons) attend the morning handoff, equalling 296 hours. The database manager checks a mean of 795 records with complications (17.5% of all admissions have complications) per year, which involves 133 hours of work per year


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Trigger ToolShould the trigger tool be used, the costs of the surgeons would be saved, whereas the database manager should now check 1862 out of 4534 records, given a positive predictive value of 41% of the trigger tool. This means 310 hours per year spent by the database manager.

DISCUSSION

Based on our study results, the proposed trigger tool appears as accurate as a SCR in terms of sensitivity and specificity as to the detection of complications that occur in hospitalized surgical patients, but the proposed trigger tool does save time for surgeons. On the other hand the proposed trigger tool is less time-efficient for the database manager as compared to the SCR, as it requires checking a relatively high number of files of patients not suffering from a complication. The nominal wage of the surgeon or resident is likely to be higher than the wage of the database manager. At least in our hospital the use of the trigger tool would save up to €40,000 yearly. Thus, the trigger tool will save costs, as it requires less effort from surgeons. On the other hand, the SCR during the morning handoff does provide awareness among surgeons of the complications suffered by their patients to deliberate and reflect on. However, the trigger tool we developed detected a higher number and a higher proportion of more severe complications. Only some mild complications would have been missed, for example wound infection with no need for a re-operation, or cardiac complications. This study is one of the few that used a reference standard to assess the comprehensiveness of the detection of patients with complications by either method. Most studies have used a ‘silver’ standard or even no reference standard at all.18,19,22 The sensitivity of the trigger tool in this study compares favourably to other forms of term searching tools, such as scanning the discharge letters for words suggestive for complications,18,22 or a natural language processing detecting method.19

As an alternative to the detection methods investigated here, the clinical observation method is the investigation of potential complications by a trained observer of all patients and providers, who is alerted by a pre-defined list of clinical event ‘triggers’. Clinical observation is a powerful tool for identifying incidents and errors in medical care, especially when compared with self-report or voluntary reporting mechanisms.19,23 This observation method, however, uses clinical observers and also focus groups to identify complications, which would imply a huge manual effort. Another option, the IHI collaborative ‘Global Trigger Tool’ (GTT), appears to detect more complications than other conventional approaches but requires substantial manual effort.3,11,12

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The proposed trigger tool (tailored to a Dutch hospital context) was indeed highly resource-intensive and required manual database searching. A fully electronic database including all admitted patients and their health care utilization characteristics would facilitate the use of this trigger tool. Unfortunately, symptoms, diagnoses and physical findings are usually recorded as narrative texts, but are yet unavailable in coded form. Nursing files and surgical discharge letters were found very helpful to find complications.5 The trigger tool method could be simplified by the retrieval of complications from patient nursing files and surgical discharge letters. In addition, nurses might play an important role in the process of complication registration. Application of the trigger tool should also be simplified by systematized electronic storage information in hospital data systems, in order to detect patients ‘at risk’ more easily.

LimitationsAlthough tested on a reliable reference standard (n=300), little is known about the risks of the trigger tool in a larger population regarding missed complications. For practical reasons the number of patients in the reference standard was limited, which may have led to fewer significant triggers. Further external validation is warranted to assess the value of this trigger tool before it can be implemented in clinical practice.This trigger tool is very helpful after discharge in the detection of complications by identifying high risk patient’ records. Another use of ‘triggers’ could be their functioning as a so-called ‘red-flags’, highlighting the patients who are sensitive for developing complications, which could be useful in the improvement of complication prevention in clinics. The predictors included in the trigger tool are factors unknown before admission and therefore the trigger tool cannot be used for that purpose.Although studies on the GTT are common, little is known about the use of a customized trigger tool,16 or sensitivity outcomes against a reference standard. The need for an efficient and inexpensive means to detect complications makes further research on an electronic (trigger tool) approach attractive. The time surgeons and residents spent was based on the number of surgeons and residents during the SCR in our hospital. Obviously, this number may differ in other hospitals and may alter the time difference as found here.

CONCLUSION

The use of a customized set of triggers as proposed here to detect surgical complications results in high sensitivity for the detection of major complications. On the other hand, mild complications would be missed, for example wound infection with no need for a re-operation. The proposed trigger tool appears as accurate as a SCR in terms of sensitivity


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and specificity as to the detection of minor and major complications. Additionally it saves time for surgeons. We therefore advocate the use of a trigger tool, provided that it is simplified by a systematized electronic storage of patient characteristics and trigger valuable information in hospital data systems.

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REFERENCES

1. Veen EJ, Janssen-Heijnen MLG, Leenen LPH, et al. The registration of complications in surgery: a learning curve. World J Surg 2005;29:402–9.

2. Nicolay CR, Purkayastha S, Greenhalg A, et al. Systematic review of the application of quality improvement methodologies from the manufacturing industry to surgical healthcare. Br J Surg 2012;99:324–35.

3. Resar RK, Rozich JD, Simmonds T, et al. Trigger Tool to Identify Adverse Events in the Intensive Care Unit. Jt Comm J Qual Patient Saf 2006;32:585–90.

4. Edition S. IHI Global Trigger Tool for Measuring Adverse Events. 2009.

5. Ubbink DT, Visser A, Gouma DJ, et al. Registration of surgical adverse outcomes: a reliability study in a university hospital. BMJ Open 2012;2:1–7.

6. Shah N, Hamilton M. Clinical review: Can we predict which patients are at risk of complications following surgery? Critical Care 2013;17:226.

7. Saklad M. Grading of patients for surgical procedures. Anesthesiology 1941;2:281-284.

8. Knaus WA, Draper EA, Wagner DP, et al. APACHE II: a severity of disease classification system. Crit Care Med 1985;13:818-29.

9. Copeland GP, Jones D, Walters M. POSSUM: a scoring system for surgical audit. Br J Surg 1991;78:355-60.

10. Donati A, Ruzzi M, Adrario E, et al. A new and feasible model for predicting operative risk. Br J Anaesth 2004;93:393-9.

11. Griffin FA, Classen DC. Detection of adverse events in surgical patients using the Trigger Tool approach. Qual Saf Health Care 2008;17:253–8.

12. Mattsson TO, Knudsen JL, Lauritsen J, et al. Assessment of the global trigger tool to measure, monitor and evaluate patient safety in cancer patients: reliability concerns are raised. BMJ Qual Saf 2013;22:571–9.

13. Naessens JM, O’Byrne TJ, Johnson MG, et al. Measuring hospital adverse events: assessing inter-rater reliability and trigger performance of the Global Trigger Tool. Int J Qual Health Care 2013;4:266–74.

14. Schildmeijer K, Nilsson, L, Arestedt K, et al. Assessment of adverse events in medical care: lack of consistency between experienced teams using the global trigger tool. BMJ Qual Saf 2012 21:307–14.

15. Von Plessen C, Kodal AM, Anhøj J. Experiences with global trigger tool reviews in five Danish hospitals: an implementation study. BMJ Open 2012;2(5):e001324. doi:10.1136/bmjopen-2012-001324.

16. Szekendi MK, Sullivan C, Bobb A, et al. Active surveillance using electronic triggers to detect adverse events in hospitalized patients. Qual Saf Health Care 2006;15:184–90.

17. O’Leary KJ, Devisetty VK, Patel AR, et al. Comparison of traditional trigger tool to data warehouse based screening for identifying hospital adverse events. BMJ Qual Saf 2013;22(2):130-8.

18. Forster AJ, Andrade J, Van Walraven C. Validation of a Discharge Summary Term Search Method to Detect Adverse Events. J Am Med Inform Assoc 2005;12(2):200-6.

19. Melton GB, Hripcsak G. Automated Detection of Adverse Events Using Natural Language Processing of Discharge Summaries. J Am Med Inform Assoc 2005;12(4):448-57.

20. Clavien PA, Barkun J, de Oliveira ML, et al. The Clavien-Dindo classification of surgical complications: five-year experience. Ann Surg 2009;250(2):187–96.

21. Kievit J, Jeekel J, Sanders FBM. Complicaties registreren. Landelijke database voor beter inzicht. Medisch Contact 1999;54:1363-5.

22. Murff HJ, Forster AJ, Peterson JF, et al. Electronically Screening Discharge


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Summaries for Adverse Medical Events. J Am Med Inform Assoc 2003;10(4):339-50.

23. Backman C, Forster AJ, Vanderloo S. Barriers and succes factors to the implementation of a multi-site prospective adverse event surveillance system. Int J Qual Health Care 2014;26(4):418-25.

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Appendix. Definition of triggersComplete list of triggers definitions in alphabetical order used in univariable analysis(Table 1).Trigger DefinitionAge Amount of years between date of birth and final hospitalization date on surgical

department, round off downwards

Decreased serum albumin Yes: Level of lowest measured serum Albumin on day of hospitalization with the following cut-off level:

- < 35 g/LNo: Level of lowest measured serum Albumin on the day of hospitalization which does not reach the cut-off level.

Active alcohol abuse Yes: ‘Active’ No: ‘Not applicable’ or ‘previous drinker’ Inclusion criteriaWhen double different registrations for one patient number (different outcomes at multiple registration moments) were determined, outcome closest to admission date were included.

ASA-score Fitness of a patient right before a procedure classified according to ‘Physical Status Classification System’ASA Physical Status 1 - A normal healthy patientASA Physical Status 2 - A patient with mild systemic diseaseASA Physical Status 3 - A patient with severe systemic diseaseASA Physical Status 4 - A patient with severe systemic disease that is a constant threat to lifeASA Physical Status 5 - A moribund patient who is not expected to survive without the operationASA Physical Status 6 - A declared brain-dead patient whose organs are being removed for donor purposesIncluded: The first registered ASA-score during a hospitalization on surgical department

BMI Weight in kilograms divided by the square of length in meters.COPD/Asthma/Emphysema

Yes: ‘Light’, ‘Medium’ or ‘Severe’ No: ‘No’ or ‘Unknown’ Inclusion criteriaWhen double different registrations for one patient number (different outcomes at multiple registration moments) were determined, outcome closest to admission date were included.

Increased serum creatinine

Yes: Level of highest measured serum Creatinine on the day of admission with the following cut-off levels:

- Women: > 95 micromoles/L- Men: > 110 micromoles/L

No: Level of highest measured serum Creatinine on the day of hospitalization which does not reach the cut-off levels.

Increased C-reactive protein (CRP)

Yes: Level of highest measured serum CRP on the day of hospitalization with the following cut-off level:

- > 5 mg/LNo: Level of highest measured serum CRP on the day of hospitalization which does not reach the cut-off level.

Highest urgency code in admission period

Highest urgency code registered during hospitalization.S1: Direct urgencyS2: Urgency, procedure occurred during the same part of the dayS3: Semi-urgency, procedure within 24 hours

Hypertension Yes: ‘Yes, with medication’/ ‘Yes, with diet’/ ‘Yes, not regulated’ according to PDMS databaseNo: ‘No’ or ‘Unknown’ according to PDMS databaseInclusion criteriaWhen double different registrations for one patient number (different outcomes at multiple registration moments) were determined, Outcome closest to admission date were included.


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Admission to IC Yes: IC admission during hospitalization at surgical departmentNo: No IC admission during hospitalization at surgical department

Length of stay Difference in days between admission date and final date of hospitalization + 1Increased leukocyte count Yes: Level of highest measured serum Leucocytes on the day of hospitalization with the

following cut-off level:- > 10.5 x 109 cells/L

No: Level of highest measured serum Leukocytes on the day of hospitalization which does not reach the cut-off levels.

MET score Fitness score of a patient registered on the basis of a questionnaire including questions on activities which could still be performed by the patient. Inclusion criteria:

- A registered MET score within a maximum of 30 days before admission date- The most recent MET score registered before admission date

MET score: 0 – 9 point

DNR Yes: Resuscitation = - Code A - Code C when ‘resuscitation yes’

No: No resuscitation = - Code D- Code C when ‘resuscitation no’

Inclusion criteria:- Most recent registered resuscitation code according to AMC resuscitation

protocol Code A: No treatment limitationsCode B: No treatment limitations, permission is required every day (this code did not appear in our database)Code C: Treatment limitations. When patient is Code C classified further specification of treatment is registered. Resuscitation could be registered as ‘yes’ or ‘no’Code D: No treatment

Reoperation Yes: Reoperation within - Same hospitalization period- 30 days after discharge date

Inclusion criteria:- Reoperation at the same location of previous action

Or- Operating a situation which results from previous intervention

No: No reoperation

Smoking Yes: ‘Active’ smoker according to PDMS databaseNo: ‘Not applicable’ or ‘previous smoker’ according to PDMS databaseInclusion criteriaWhen double different registrations for one patient number (different outcomes at multiple registration moments) were determined, Outcome closest to admission date was included.

Sodium level outside reference range

Yes: Level serum Sodium measured on the day of admission most extreme outside reference area. Cut off levels Sodium outside reference area:

- < 135 mmol/L- > 145 mmol/L

No: Level serum Sodium between reference area. Reference area:- 135 – 145 mmol/L

Use of corticosteroids Yes: One or more corticosteroid prescriptions known at the AMC pharmacy within 42 days before admission date or at admission date. No discrimination between different kind of steroids products or using period. No: No corticosteroid prescriptions known

Surgical procedure Yes: Surgical procedure during admission performed by a surgeon from the department of surgery. This surgical department provides general, gastrointestinal, hepatopancreatobiliary, vascular, and trauma surgical care. No: No surgical procedure during admission

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Time required above the scheduled procedure time

Difference in minutes between planned time in OR for surgical procedure and realized time in OR. Yes: Difference in minutes between planned time in OR for surgical procedure and realized time in OR.No: Difference in minutes between planned time in OR for surgical procedure and realized time in OR.

Type of procedure: AAAA Yes: Admissions which included a procedure for ruptured or symptomatic abdominal aortic aneurysm.Operation code (Dutch Hospital Data*):333535, 333530, 333538, 333153T, 333530H Exclusion:

- Elective procedures - Acute rupture thoracic aneurysm - Duplicates

The following procedures were checked on inclusion criteria:333538, 333153TNo: Admissions with no AAAA procedure

Type of procedure: Multi damage control surgery

Yes: Admissions on Trauma department at AMC hospital with ISS score >= 16. No: Admissions with no ISS score or ISS score <16

Type of procedure: Oesophagus resection

Yes: Admissions which included a resection of the oesophagus procedureOperation code (Dutch Hospital Data)334345, 334322, 334327No: Admissions with no esophagus prodecure

Type of procedure: Whipple

Yes: Admissions which included a Whipple procedure.Operation code (Dutch Hospital Data)335417, 335417A, 335430No: Admissions with no Whipple procedure

Urgency code at moment of admission

Yes: A procedure with urgency code S1/S2/S3 at the day of admissionS1: Direct urgencyS2: Urgency, procedure occurred during the same part of the dayS3: Semi-urgency, procedure within 24 hoursNo: A procedure with no S urgency or no procedure at the day of admission

Urgency code during hospitalization

Yes: A procedure with urgency code S1/S2/S3 during hospitalization. S1: Direct urgencyS2: Urgency, procedure occurred during the same part of the dayS3: Semi-urgency, procedure within 24 hoursNo: A procedure with no urgency code during hospitalization.

Complexity procedure Highest registered complexity of a procedure within a hospitalization. Weight class range: 1 – 7

* www.dutchhospitaldata.nl


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‘Gold fray’. The wings seemed to be frayed, like a dress which has its seam missing and the fabric has frayed. The wings are fixed with gold leaf to stop further fraying. Anne ten Donkelaar.

Chapter 9

Hospital Costs of Complications after

Pancreatoduodenectomy

Katrien TB SantemaAnnelies Visser

Olivier RC BuschMarcel W Dijkgraaf

J Carel GoslingsDirk J GoumaDirk T Ubbink

HPB 2015; Provisionally accepted

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ABSTRACT

Background: Pancreatoduodenectomy (PD) is a highly advanced procedure associated with considerable postoperative complications and substantial costs. We assessed the hospital costs associated with complications after PD.

Methods: A retrospective cohort study was conducted on 100 consecutive patients who underwent a (pylorus-preserving) pancreatoduodenectomy (PP)PD between January 2012 and July 2013. Per patient, all complications occurring during admission or in the 30-day period after discharge were documented. Hospital costs related to the (PP)PD were defined as the costs of all medical interventions and resources during the hospitalization period as recorded by the electronic supply tracking system.

Results: Median hospital costs ranged from €17,482 for a patient without complications to €55,623 for a patient with postoperative haemorrhage. After adjusting for patient characteristics, postoperative haemorrhage was associated with a 39.6% increase in total hospital costs. Other factors significantly associated with an increase in total hospital costs were: the presence of a malignancy other than pancreatic adenocarcinoma (29.4% cost increase), the severity grade of a complication (34.3% - 70.6% increase) and the presence of postoperative infection (32.4% increase).

Conclusions: This study provides an in-depth analysis of hospital costs and identifies factors that are associated with substantial cost consequences of specific complications occurring after PD.


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INTRODUCTION

Health care costs are rising worldwide and therefore cost containment is one of the most important challenges in future medicine. Health care expenditures are considered to be at least in part influenced by the aging population, but the increase of performing specialised surgical procedures also contributes to high hospital costs.1-3 Postoperative complications also substantially increase the use of additional resources per patient and prolong hospital stay, raising medical costs even further.4-8 Pancreatoduodenectomy (PD) is a typical example of a complex, highly specialised surgical procedure. Despite a reduction in the mortality of PD below 5% in high-volume centres, PD is still accompanied with a substantial morbidity and postoperative complication rates varying between 40 and 60%.9-12 Important surgical complications after PD include anastomotic leakage, in particular of the pancreatojejunostomy and leading to a pancreatic fistula (PF), haemorrhage, and delayed gastric emptying (DGE).13-15

Reducing complications has become a desirable goal for quality improvement initiatives to optimize patient outcomes and to reduce hospital costs.16-18 Previous studies have already identified factors that can predict postoperative complications. Examples of such predictors are duodenal or ampullary lesions that generally present with a non-dilated pancreatic duct and a soft pancreas, which more frequently result in leakage of the pancreatic anastomosis, pancreatic fistula, and a subsequently higher risk of postoperative haemorrhage, but also preoperative nausea, which is associated with a higher incidence of DGE and prolonged hospital stay.19, 20 Although patients at risk for developing complications after a PD can be identified, limited information is currently available about costs of specific complications .21 An in-depth cost evaluation of pancreatic surgery, in particular regarding procedures with and without specific complications might gain insight into the economic burden of those complications. This could be helpful to predict hospital costs after pancreatic surgery. Information about hospital costs might also be helpful to suggest changes in the management of complications with the aim of reducing health care expenditures. The aim of this study was therefore to quantify the cost consequences of complications occurring in hospitalized patients after PD. Furthermore we assessed which factors are associated with an increase in total hospital costs.

METHODS

Study DesignWe conducted a retrospective cohort study at a tertiary-referral university hospital in the Netherlands. This is a retrospective review of a database with real-time data capture. Data

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on a consecutive series of adult patients who underwent a PPPD or classical PD between January 2012 and July 2013 were prospectively included in this database. Data gathered during this 1.5 year observation period included the minimum follow-up period of 30 days following discharge. The following clinical data were included: age, gender, comorbidities, American Society of Anaesthesiologists (ASA) classification, type of PD, need for vascular resection, (histo)pathologic diagnosis, length of hospital stay, readmissions, reoperations and length of ICU stay. We used the STROBE statement to ensure the proper reporting of this observational study.22

ComplicationsAll complications as documented in a local department’s database of the Dutch National Surgical Complication Registry (Landelijke Heelkunde Complicatie Registratie, LHCR) were analysed. The LHCR was developed by the Dutch Society of Surgeons and is a slightly modified version of the Clavien-Dindo classification.23, 24 The following definition of a complication was used in the registry and this study: ‘an unintended and undesired outcome or state occurring during or following medical care that is so harmful to the patients’ health that it requires (adjustment of) treatment or leads to permanent damage’.25

Variables registered in the complication registry comprise patient characteristics, admission characteristics, and complications occurring during admission or in the 30-day period after discharge leading to re-admission, reoperation, or death. All complications reported during morning handovers with the attendance of the complete surgical staff and residents are encoded in this registration, as well as complications reported in the discharge letter. The reliability of this complication database was independently audited.26

The complication registry categorises each complication into four grades of severity: Grade 1, temporary health disadvantage recovering without reoperation (grade1 management includes radiological or endoscopic interventions; similar to Dindo grade I, II and IIIa); grade 2, recovery after reoperation (similar to Dindo grade IIIb); grade 3, (probably) permanent damage or function loss (similar to Dindo grade IV when permanent); and grade 4, death (similar to Dindo grade V). Patients were followed until their complication had recovered or it was obvious that the complication resulted in permanent damage or death. When multiple complications were reported in one patient, the recorded level of severity was determined by the most severe complication. Minor complications, such as an electrolyte imbalance or fever, even without clinical consequences, were also registered in the department’s complication registration and were classified as severity grade 1. Furthermore, the complication registry was searched by two investigators independently (TBS and AV) to select three important and specific complications for pancreatic surgery,


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i.e., ‘postoperative haemorrhage’, ‘anastomotic leakage’ and ‘DGE’. If DGE occurred without postoperative haemorrhage or an anastomotic site leakage, the complication was labelled as ‘isolated DGE’. If a patient had a combination of postoperative haemorrhage and anastomotic leakage, the patient was analysed in both of these complication groups. If the type of complication was unclear based on the complication registry, the investigators checked the information from the discharge letter and the (electronic) medical record. Discrepancies between investigators were resolved by discussion. Admission to the ICU is not part of standard postoperative care after PD and patients are only admitted to the ICU in case of severe complications.

CostsWe included all hospital costs per patient, including outpatient visits and readmissions, that were directly related to the provided care in relation with the PD. Cost related to the diagnostic pathway before the operation and additional costs of non-related diagnoses or procedures in the past were not taken into account. Electronic supply tracking allowed accurate determination of hospital costs for a specific diagnosis and were provided by the financial department of the hospital. Unit costs for a specific product included the front-office costs of personnel (e.g. nurses, surgeons), material use, as well as the back-office costs of facility and overhead. In our hospital all specialists (surgeons) are employed by the hospital with a fixed yearly salary.Total hospital costs per patient were calculated as the product-sum of volumes and unit costs of care and were subdivided in seven cost domains: ‘general diagnostics’ (e.g., laboratory, microbiology and pathology investigations), ‘imaging’ (e.g., CT or MRI scans), ‘outpatient clinic’ (e.g., visits to outpatient clinic or emergency department), ‘clinical care’ (ward care and one-day hospital admissions), ‘surgical’ (operating room and surgical supplies), ‘ICU’ (critical care) and ‘other costs’. The cost domain ‘other costs’ included costs for blood transfusion, percutaneous drainage, and similar procedures.Incurred hospital costs in the period before the PD were not taken into account, because of the wide variety of diagnostic pathways prior to surgery, partly performed in other hospitals. The time horizon of the study was restricted to the index admission and readmissions for treatment of complications within 30 days of initial hospital discharge. Considering the limited time horizon, no discounting of costs took place to account for time preference. Unit costs were expressed for the base year 2014. Only in-hospital costs were included in this study. Costs for additional care, such as nursing facilities or home care, were not included in this study.

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Statistical AnalysisStatistical analyses were performed using Statistical Package for the Social Sciences version 21 (SPSS Inc., Armonk, NY, USA). Descriptive statistics were expressed as means and standard deviations, or medians and inter-quartile ranges (IQR), whenever appropriate. Risk differences were calculated and presented with 95% confidence intervals (CI). The Student t and Mann-Whitney U-tests were used to analyse differences between two groups with normally or non-normally distributed continuous variables (such as costs), respectively. The Chi-square test was used to compare percentages (e.g., patient characteristics) and the Kruskal-Wallis test was used to compare more than two non-normally distributed groups (e.g., the three specific complications chosen for further analysis). Kaplan-Meier estimates of total hospital costs were obtained and compared between three patient groups (without complications, with grade 1 complications, and with grade 2 complications), using log-rank test statistics. The level of significance was defined as a P-value less than 0.05.Univariable and stepwise multivariable linear regression was used to explore possible associations between specific patient characteristics and total costs. Log-transformation of the dependent variable ‘total costs’ was performed because of its non-linear distribution. Categorical variables (e.g., postoperative diagnosis, complication severity) were recoded into dummy variables before analysing the data. Possible predictors were entered in the multivariable analysis when showing a (nearly) significant (i.e. p<0.10) difference between patients with and without complications according to the univariable linear regression analysis. Results from the regression analyses are expressed as regression coefficients, 95% CI and P-values.

RESULTS

Between January 2012 and July 2013, 100 consecutive adult patients underwent a pancreatoduodenectomy and were included in this study. Mean age at surgery was 64.0 years ± 10.0 years, while more patients were males (59%). Eighty-five of the patients underwent a PPPD, the remaining patients underwent standard PD. Overall, 73% of the patients sustained one or more complications. Of the three selected complications, anastomotic leakage (PJ,HJ,GJ) was the most common (24/100). Isolated DGE and postoperative haemorrhage were reported less frequently (in 18 and 12 patients, respectively). In 8 of the latter patients, leakage and postoperative haemorrhage occurred simultaneously. Mortality during admission and in the 30-day period after discharge was 1%. Unplanned readmission within 30 days after discharge was required in ten patients (10%) with a grade 1 complication and in one patient (1%) with a grade 2 complication. Characteristics of patients with and without a complication are summarised in Table 1.


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Patients with complications after surgery more frequently showed a history of cardiac disease and hospital- and ICU stay in this group were significantly longer. There were no significant differences in age, sex, ASA-classification, type of surgical treatment and postoperative diagnosis between the groups without or with one or more complications.

Costs Related to the Occurrence of a Complication Median total hospital costs per patient were € 25,047 (IQR 18,430–44,600), while mean total hospital costs were € 37,416 (SD 29,814). Having selected the most severe complication for each patient, 58/73 (79.5%) complications were classified as severity grade 1 (without reoperation), 13/73 (17.8%) as severity grade 2 (with reoperation), one (1/73; 1.4%) as grade 3 (permanent damage or function loss), and one as grade 4 (death). Table 2a presents a comparison of hospital costs between patients without complications, those with a grade 1 or a grade 2 complication.

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Table 1. Characteristics of patients without or with one or more complications after pancreatoduodenectomy

No complicationN = 27

With ≥ 1 complicationN = 73

RD or MD(95% CI)

p-value

Age at surgery in years (SD) 64.2 (11.9) 64.0 (9.3) MD 0.20 (-4.29 to 4.69) 0.105

Sex (%) Male 15 (55.6) 44 (60.3) RD -0.047 (-0.257 to 0.157) 0.670

Type of resection (%) Pylorus -preserving PD 23 (85.2) 62 (84.9) RD 0.003 (-0.186 to 0.136) 0.975

ASA classification (%) I II III/IV

6 (22.2)20 (74.1)1 (3.7)

16 (21.9)48 (65.8)9 (12.3)

0.434

Comorbidity (%) Cardiac disease Pulmonary disease Diabetes HypertensionHistologic diagnosis (%) Pancreatic adenocarcinoma Ampullary adenocarcinoma Distal CBD adenocarcinoma Other (pre)malign* Other benign

Vascular resection, yes (%)

Median length of hospital stay in days (IQR)Median length of ICU stay in days (IQR)

0 (0.0)2 (7.4)7 (25.9)10 (37.0)

10 (37.0)2 (7.4)5 (18.5)6 (22.2)4 (14.8)

3 (11.1)

8 (7 - 10

0 (0 - 0)

12 (16.4)5 (6.8)14 (19.2)19 (26.0)

22 (30.1)6 (8.2)16 (21.9)26 (35.6)3 (4.1)

7 (9.6)

15 (10 – 26)

0 (0 – 1†)

RD 0.164 (-0.266 to -0.223)RD 0.006 (-0.092 to 0.170)RD 0.068 (-0.098 to 0.269)RD 0.110 (-0.098 to 0.318)

0.0250.9230.4620.2810.314

0.822

<0.001

0.005

Severity of complications (%) Grade 1 Grade 2 Grade 3 Grade 4Number of complications (%) 1 2 3 4 ≥5Type of complication (%) Postoperative haemorrhage Anastomotic leakage Isolated delayed gastric emptying Postoperative infection (local or systemic)

58 (79.5)13 (17.8)1 (1.4)1 (1.4)

28 (38.4)19 (26.0) 10 (13.7)6 (8.2)10 (13,7)

12 (16.4)24 (32.9)18 (24.7)33 (45.2)

ASA, American Society of Anaesthesiologists: CBD, Common bile duct; IQR, Inter quartile range; MD, Mean Difference; PD, Pancreatoduodenectomy; RD, Risk Difference; SD, Standard Deviation; CI, Confidence Interval. *Other (pre)malignant: e.g. multiple endocrine neoplasia in the pancreatic head area or duodenal carcinoma.†Range 0 – 33 days.


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Table 2a. Analysis of costs (in Euros) for patients without and with a grade 1 or grade 2 complication after pancreatoduodenectomy

No complicationN = 27

Grade 1 complicationN = 58

Grade 2 complicationN = 13

p-value#

Median costs (IQR) Total costs General diagnostics Imaging Outpatient clinic Clinical care ICU Surgical Other costs

17,4821,869166

1,2114,975

07,772942

15,831 – 20,8001,630 – 2,590

0 – 658880 – 2,144

4422 – 6,6340 – 0

7,772 – 7,772*658 – 1.537

28,3803,363764

1,32510,504

07,7721,917

21,182 – 46,2782,299 – 4,990434 – 2,502928 – 2,289

7,608 – 15,3800 – 0†

7,772 – 7,772‡

1,015 – 7,589

57,0604,7312,0511,950

17,6903,950

14,5276,220

40,641 – 90,4543,978 – 7,1951,033 – 4,068800 – 2,661

14,373 – 36,0770 – 7,684

8,674 – 18,5304,640 – 20,131

<.001<.001<.0010.610<.001<.0010.002<.001

IQR, Inter Quartile Range.*Range € 3,683 – € 15,544; †Range € 0 – € 42,646; ‡Range € 3,683 – 43,032, #Kruskal-Wallis test.

The Kaplan-Meier curves for the three study groups are shown in Figure 1.

Figure 1. Kaplan-Meier curves of the proportion of patients without a complication or with a grade 1 or grade 2 complication and their total hospital costs

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Difference between the cost curves was statistically significant (log-rank test, p<0.001). In a small proportion of patients’ grade 1 and 2 complications caused an increase in total hospital costs up to about €150,000.Patients with a grade 2 complication had significantly higher hospital costs (p values ranged from <0.005 to 0.012; data not shown) than patients with a grade 1 complication in all domains except for the domains ‘general diagnostics’, ‘imaging’ and ‘outpatient clinic’. They also had a significantly longer hospital stay than those with grade 1 complications (medians 29 vs. 14 days, respectively; p=0.002). This was also true for the length of ICU stay (1 vs. 0 days, respectively; p=0.004). Patients with a grade 1 complication had a higher ASA classification (p=0.007) and more often a history of heart disease (p=<0.001) than patients with grade 2 complications. There were no differences in other comorbidity, age, sex, type of surgical treatment, and postoperative diagnosis between the two severity groups (data not shown).

Costs of Common ComplicationsHospital costs of the three selected complications; anastomotic site leakage, isolated DGE and postoperative haemorrhage, are summarised in Table 2b. Anastomotic leakage occurred in 24/100 patients. In 8 cases this was in combination with a late postoperative haemorrhage. Patients with an anastomotic leakage had a median length of hospital stay of 26 days (IQR 15–36 days) and 0 days ICU stay (IQR 0–4; range 0–33). There was no need for reoperation in 54.2% of the patients with anastomotic leakage (N=13), and accordingly these were classified as severity grade 1. Nine patients were classified as severity grade 2 (37.5%) and one patient was classified as having a grade 3 complication (because this patient needed a permanent ileostomy due to the complication) One patient died after anastomotic leakage in combination with late postoperative haemorrhage (grade 4). Median total hospital costs for a patient with an anastomotic leakage were €53,760, more than 3 times the total hospital costs of a patient without complications.Patients with isolated DGE (occurring in 18/100 patients, all classified as grade 1) had a median length of hospital stay of 14 days (IQR 10–25) and 0 days ICU stay (IQR 0–0; range 0–4). Median total hospital costs for a patient with isolated DGE were €26,825, which was only half of the costs for a patient anastomotic leakage, but still more than 50% higher than the median total hospital costs of €17,482 for a patient without complications. Postoperative haemorrhage was reported in 12/100 patients. As mentioned before, in eight patients this was in combination with anastomotic leakage. In patients with postoperative haemorrhage, the median length of hospital stay was 23 days (IQR 13–39 days) and 3 days ICU stay (IQR 0–5 days). There was no need for reoperation in five patients (5/12; 41.7%; severity grade 1) and five patients were classified as severity


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grade 2 (6/12; 50.0%). The median total hospital costs for a patient with postoperative haemorrhage were €55,623. Median hospital costs in the different domains for each of these three specific complications versus median costs for patients without a complication are summarized in Figure 2.

Table 2b. Analysis of costs (in Euros) of the three most common complications after pancreatoduodenectomy.

Postoperative haemorrhageN = 12/100

Anastomotic leakageN = 24/100

Isolated DGEN = 18/100

P value‡

Median costs (IQR) Total costs General diagnostics Imaging Outpatient clinic Clinical care ICU Surgical Other costs

55,6235,2032,1211,37014,3075,70912,88510,298

35,825 – 101,4893,398 – 9,092783 – 3,791786 – 1,900

12.310 – 35,700988 – 11,5557365 – 15,1137,589 – 23,098

53,7604,6551,5771,370

17,1713,9508,6746,476

24,449 – 90,5773,023 – 9,092729 – 3,791762 – 2,382

9,674– 30,7480 – 7,793

7,772 – 14.4261,880 – 10,913

26,8253,907680

1,38612,16207,7721,288

22,179 – 39,4612,606 – 5,555328 – 2,882890 –1,952

7,601– 15,0640 – 0*

7,772 – 7,772†

776 – 2,788

0.0160.4680.1090.8850.1170.0020.270<.001

IQR, Inter Quartile range. *Range € 0 – € 9,660; †Range € 3,683 – € 24,567. ‡Kruskal-Wallis test.

Figure 2. Median costs per domain by the occurrence of a specific complication after pancreatoduodenectomy

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Clinical care and surgical costs contributed mostly to the total hospital costs of complications.When comparing the costs domains in patients with postoperative haemorrhage and anastomotic leakage, none of the domains showed statistically significant differences (p-values ranged from 0.164-0.830). Compared to patients with isolated DGE, the cost domains ‘total costs’, ‘ICU’ and ‘other costs’ were significantly higher in the group with postoperative haemorrhage (p-values 0.004, 0.003, 0.001, respectively) and anastomotic leakage (p-values 0.035, 0.006, 0.003, respectively).

Regression AnalysesResults of the univariable and multivariable linear regression analyses are shown in Table 3. Significant predictors of total hospital costs were histologic diagnosis, complication severity, postoperative haemorrhage, anastomotic site leakage and presence of postoperative infection. Postoperative haemorrhage was associated with a 39.6% increase in total hospital costs. For an average patient, total hospital costs increased with €11,485 if postoperative haemorrhage occurred (increase from €28,973 to €40,458). Presence of a malignancy other than pancreatic adenocarcinoma (e.g. duodenum carcinoma) was also associated with higher total hospital costs (29.4% increase). Furthermore, the occurrence of a grade 1 (34.3% increase) or a grade 2-4 (70.6% increase) complication and the presence of postoperative infection (32.4% increase) were associated with higher hospital costs. This model explained almost 50 percent (R2 =0.479) of the variance in total hospital costs.


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Table 3. Univariable and multivariable analyses of possible factors predicting total costs Univariable analysis Multivariable analysis

Ratio for cost increase

95% CI p-value Ratio for cost increase

95% CI p-value

AgeGenderOperation procedure PPPD vs standard PD ASA classification ASA I ASA II ASA III/IV Histologic diagnosis Pancreatic adenocarcinoma Ampullary adenocarcinoma Distal CBD adenocarcinoma Other (pre)malignant* Other benignVascular resectionComplication severity No complication Grade 1 Grade 2, 3 or 4 Readmission within 30 daysPostoperative haemorrhageAnastomotic leakageDelayed gastric emptyingPostoperative Infection (local or systemic)Co morbidity Heart disease†

Pulmonary disease‡

Diabetes Hypertension

0.9960.873

0.882

RC0.8940.772

RC0.9921.4191.6100.9190.760

RC1.7232.8491.3002.1631.8921.2031.833

1.1331.0570.9320.879

0.984 – 1.0090.680 – 1.119

0.626 – 1.244

0.661 – 1.2090.608 – 1.556

0.627 – 1.5701.024 – 1.9661.203 – 2.1530.566 – 1.4910.506 – 1.140

1.355 – 2.1902.061 – 3.9400.881 – 1.9171.531 – 3.0551.462 – 2.4490.887 – 1.6320.887 – 2.394

0.777 – 1.6520.653 – 1.7100.690 – 1.2610.672 – 1.151

0.5680.279

0.471

0.4620.906

0.9730.0360.0020.7300.183

<0.001<0.0010.184

<0.001<0.0010.231

<0.001

0.5130.8200.6470.346

RC0.9021.1301.2940.881

RC1.3431.706

1.3961.253

1.324

0.624 – 1.3190.622 – 1.493 1.019– 1.6440.585 – 1.324

1.050 – 1.7141.167 – 2.495

1.002 – 1.9500.957 – 1.637

1.057 – 1.660

0.5840.3810.0350.538

0.0190.006

0.0490.099

0.015

ASA, American Society of Anaesthesiologists; CBD, Common bile duct; RC, Reference category; PD, Pancreatoduodenectomy; PPPD, Pylorus-preserving pancreatoduodenectomy; CI, Confidence Interval.*Other (pre)malignant: e.g. multiple endocrine neoplasia in the pancreatic head area or duodenal carcinoma.†Including a history of angina pectoris, heart failure, myocardial infarction or an arrhythmia. ‡Including a history of asthma, chronic obstructive pulmonary disease (COPD) or pulmonary tuberculosis.

DISCUSSION

This study showed that the total hospitals costs after PD double if a complication occurs. In case of a complication requiring a reoperation to recover the total hospital costs even triple. Furthermore, the occurrence of postoperative haemorrhage is independently associated with a 39.6% cost increase, mainly due to increased hospital stay. Numerous ways to reduce length of hospital stay are currently described.27, 28 Length of hospital stay can

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be influenced by implementing specific protocols or programmes or through cooperation with other hospitals and skilled nursing facilities.29, 30 Prevention and early diagnosis of complications could also contribute to a reduction of the length of hospital stay by the implementation of the several evidence-based bundles for postoperative wound infection, pneumonia or sepsis.31 Additionally, we found that high hospital costs after PD are associated with diagnosis, for example multiple endocrine neoplasia (MEN) in the pancreatic head area, duodenal carcinoma or suspected adenomas, severity grade of a complication, and presence of postoperative infection. The management of these complications is usually more difficult and consists of more expensive diagnostic procedures and interventions. As a possibly feasible option in the future management of complications we recommend firstly optimal and early diagnostic work-up in patients with clinical post operative problems; secondly if complications are diagnosed early intervention by non-operative procedures, because these are less costly than operative interventions and showed no differences in success rates in a previous study and will lead to a shorter hospital stay.32

Age and ASA classification did not seem associated with hospital costs. This is in contrast with other studies showing that these preoperatively identifiable factors are associated with an increased risk for a complication especially with age > 70-75 or ASA classification of 2 or higher.33-37 These discrepancies could be attributed to the selection of patients for PD. Only patients in a good preoperative performance state were accepted for surgery and they were not excluded only based on age. This selection of patients is reflected in the fact that few patients classified as ASA III/IV (10%) were included in this study. A recent study supported our findings and also showed no association between age and morbidity after PD.38 Because complications result in high hospital costs, it is obvious that cost savings could be achieved by reducing the incidence of complications after PD. However, it remains difficult to act upon the presence of predicting factors for complications. In this study, predicting factors such as malnutrition and preoperative cholangitis were not taken into account because the decision to perform surgery is based on other medical grounds as mentioned before but cholangitis was treated preoperatively by antibiotics and drainage. Therefore selection could have influenced the outcome of these predicting factors. Previous studies showed the relationship between high hospital-volume and surgeon-volume on a lower incidence of complications and quality of care.11, 39-41 It seems fair to say that cost savings can be achieved nation-wide by performing pancreatic surgery only in high-volume hospitals and by experienced surgeons.21 The overall complication rate in the present study was higher than in previous reports from our hospital as well as from other contemporary studies despite 1% mortality.9-12 This is most likely because the patient sample from present study contains mostly patients with


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a diagnosis different than pancreatic adenocarcinoma leading to more complications. Furthermore, a broader definition of a complication was used in this study and various resources were checked to track all complications that had actually occurred.26. The registry also includes mild complications, such as a slight electrolyte imbalance without any clinical consequence, or any form of delirium after surgery. Some limitations to our study are worth mentioning. First, the data obtained were from consecutive patients from a single tertiary university hospital during an 18-month admission period. Therefore, the generalizability regarding costs to other hospitals or time periods is unclear. However, currently the majority of (PP)PD procedures are performed in high-volume tertiary referral hospitals. Second, we derived the costs of complications by top-down comparing hospital costs in different patient groups rather than directly attributing bottom-up which hospital resources were spent to the management of each complication. Third, we have tried to include only the hospital costs that were directly related to the PD. However, it is possible that we missed some hospital costs because they seemed not directly related (e.g. visits to the ophthalmology outpatient clinic) to the PD but might have been in reality, or vice versa. Yet, it is not likely this would have influenced our results and conclusions substantially because these additional hospital costs will be only minor. Fourth, we did not have follow-up data on potential complication-related readmissions that took place in a hospital elsewhere. However, their number would be low or even zero, because patients who underwent PD are well informed about possible postoperative complications and the importance of returning to the index hospital.

CONCLUSION

By providing an in-depth analysis of hospital costs due to complications after pancreatic surgery the impact of complication expressed as costs are identified and clarified. With this knowledge we can and will advocate further efforts to reduce hospital costs by shortening the length of hospital stay by implementing specific protocols or programmes or through cooperation with other hospitals and skilled nursing facilities. Inherently, efforts should be made to reduce to cost by making efforts to prevent complications and reduce length of stay, not only to facilitate cost containment in surgical care but also to improve quality of patient care.

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3. van Elk R, Mot E, Franses PH. Modeling healthcare expenditures: overview of the literature and evidence from a panel time-series model. Expert Rev Pharmacoecon Outcomes Res 2010;10(1):25-35.

4. Forster AJ, Kyeremanteng K, Hooper J, et al. The impact of adverse events in the intensive care unit on hospital mortality and length of stay. BMC Health Serv Res 2008;8:259.

5. Kaushal R, Bates DW, Franz C, et al. Costs of adverse events in intensive care units. Crit Care Med 2007;35(11):2479-83.

6. Zhan C, Miller MR. Excess length of stay, charges, and mortality attributable to medical injuries during hospitalization. JAMA 2003;290(14):1868-74.

7. Zoucas E, Lydrup ML. Hospital costs associated with surgical morbidity after elective colorectal procedures: a retrospective observational cohort study in 530 patients. Patient Saf Surg 2014;8(1):2.

8. Khan NA, Quan H, Bugar JM, et al. Association of postoperative complications with hospital costs and length of stay in a tertiary care center. J Gen Intern Med 2006;21(2):177-80.

9. Gouma DJ, Nieveen van Dijkum EJ, Obertop H. The standard diagnostic work-up and surgical treatment of pancreatic head tumours. Eur J Surg Oncol 1999;25(2):113-23.

10. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin 2013;63(1):11-30.

11. van Heek NT, Kuhlmann KF, Scholten RJ, et al. Hospital volume and mortality after pancreatic resection: a systematic review and an evaluation of intervention in the Netherlands. Ann Surg 2005;242(6):781-8, discussion 8-90.

12. DeOliveira ML, Winter JM, Schafer M, et al. Assessment of complications after pancreatic surgery: A novel grading system applied to 633 patients undergoing pancreaticoduodenectomy. Ann Surg 2006;244(6):931-7; discussion 7-9.

13. Bassi C, Dervenis C, Butturini G, et al. Postoperative pancreatic fistula: an international study group (ISGPF) definition. Surgery 2005;138(1):8-13.

14. Wente MN, Veit JA, Bassi C, et al. Postpancreatectomy hemorrhage (PPH): an International Study Group of Pancreatic Surgery (ISGPS) definition. Surgery 2007;142(1):20-5.

15. Wente MN, Bassi C, Dervenis C, et al. Delayed gastric emptying (DGE) after pancreatic surgery: a suggested definition by the International Study Group of Pancreatic Surgery (ISGPS). Surgery 2007;142(5):761-8.

16. Howell AM, Panesar SS, Burns EM, et al. Reducing the burden of surgical harm: a systematic review of the interventions used to reduce adverse events in surgery. Ann Surg 2014;259(4):630-41.

17. de Vries EN, Prins HA, Crolla RM, et al. Effect of a comprehensive surgical safety system on patient outcomes. N Engl J Med 2010;363(20):1928-37.

18. Dimick JB, Pronovost PJ, Cowan JA, et al. Complications and costs after high-risk surgery: where should we focus quality improvement initiatives? J Am Coll Surg 2003;196(5):671-8.

19. de Castro SM, Busch OR, van Gulik TM, et al. Incidence and management of pancreatic leakage after pancreatoduodenectomy. Br J Surg 2005;92(9):1117-23.

20. Atema JJ, Eshuis WJ, Busch OR, et al. Association of preoperative symptoms of gastric outlet obstruction with delayed gastric emptying after pancreatoduodenectomy. Surgery 2013;154(3):583-8.

21. Short MN, Aloia TA, Ho V. The influence of complications on the costs


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22. von Elm E, Altman DG, Egger M, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ 2007;335(7624):806-8.

23. Clavien PA, Barkun J, de Oliveira ML, et al. The Clavien-Dindo classification of surgical complications: five-year experience. Ann Surg 2009;250(2):187-96.

24. Marang-van de Mheen PJ, Kievit J. [Automated registration of adverse events in surgical patients in the Netherlands: the current status]. Ned Tijdschr Geneeskd 2003;147(26):1273-7.

25. Goslings JC, Gouma DJ. What is a surgical complication? World J Surg 2008;32(6):952.

26. Ubbink DT, Visser A, Gouma DJ, et al. Registration of surgical adverse outcomes: a reliability study in a university hospital. BMJ open 2012;2(3).

27. Wind J, Polle SW, Fung Kon Jin PH, et al. Systematic review of enhanced recovery programmes in colonic surgery. Br J Surg 2006;93(7):800-9.

28. Borghans I, Kool RB, Lagoe RJ, et al. Fifty ways to reduce length of stay: An inventory of how hospital staff would reduce the length of stay in their hospital. Health Policy 2012;104(3):222-33.

29. Miller TE, Thacker JK, White WD, et al. Reduced length of hospital stay in colorectal surgery after implementation of an enhanced recovery protocol. Anesth Analg 2014;118(5):1052-61.

30. Rotter T, Kinsman L, James E, et al. The effects of clinical pathways on professional practice, patient outcomes, length of stay, and hospital costs: Cochrane systematic review and meta-analysis. Eval Health Prof 2012;35(1):3-27.

31. Stulberg JJ, Delaney CP, Neuhauser DV, et al. Adherence to surgical care improvement project measures and the association with postoperative infections. JAMA 2010;303(24):2479-85.

32. Tol JA, Busch OR, van Delden OM, van Lienden KP, van Gulik TM, Gouma DJ. Shifting role of operative and nonoperative interventions in managing complications after pancreatoduodenectomy: what is the preferred intervention? Surgery. 2014;156(3):622-31.

33. Adham M, Bredt LC, Robert M, et al. Pancreatic resection in elderly patients: should it be denied? Langenbecks Arch Surg 2014;399(4):449-59.

34. Kneuertz PJ, Pitt HA, Bilimoria KY, et al. Risk of morbidity and mortality following hepato-pancreato-biliary surgery. J Gastrointest Surg 2012;16(9):1727-35.

35. Veltkamp SC, Kemmeren JM, van der Graaf Y, et al. Prediction of serious complications in patients admitted to a surgical ward. Br J Surg 2002;89(1):94-102.

36. Gupta H, Gupta PK, Fang X, et al. Development and Validation of a Risk Calculator Predicting Postoperative Respiratory Failure. Chest 2011;140(5):1207-15.

37. Neumayer L, Hosokawa P, Itani K, et al. Multivariable predictors of postoperative surgical site infection after general and vascular surgery: Results from the Patient Safety in Surgery Study. J Am Coll Surg 2007;204(6):1178-87.

38. Greenblatt DY, Kelly KJ, Rajamanickam V, et al. Preoperative factors predict perioperative morbidity and mortality after pancreaticoduodenectomy. Ann Surg Oncol 2011;18(8):2126-35.

39. Ho V, Aloia T. Hospital volume, surgeon volume, and patient costs for cancer surgery. Med Care 2008;46(7):718-25.

40. Kennedy TJ, Cassera MA, Wolf R, et al. Surgeon volume versus morbidity and cost in patients undergoing pancreaticoduodenectomy in an academic community medical center. J Gastrointest Surg 2010;14(12):1990-6.

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‘White Air” has a balloon filled up with white air. It helps the little butterfly to rice up. Anne ten Donkelaar.

Chapter 10

Summary and Future Perspectives

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SUMMARY AND FUTURE PERSPECTIVES

Transparency about complications occurring within a specific hospital and department, and even on a national level, gives insight into the quality of care and potential areas for improvement. The research in this thesis focussed on the quality of the complication registration in hospitalised surgical patients, in particular as to the recording of detailed information about the incidence, number and grade of occurring complications. Additionally, the gaps in the current complication registration and several initiatives for improving the quality of complication data were addressed. Finally, insight in the hospital costs of complications in surgical patients was gained. This thesis comprises two parts. The first part addressed the quality and quantity of the complication registration; the second part addressed the improvement of the quality of the complication registration.

PART I

In Chapter 2 we analysed the complication rate in an academic hospital over a 6-year period to study the effect of this registry and reporting system on the incidence of complications and possible trends in time. During the period 2004–2009, all adult surgical patients admitted to and discharged from the Department of Surgery were selected for this time trend study. The Dutch national surgical complication registry (LHCR) was used in the analysis, in which complications are registered according to a three-tiered matrix-like classification system. This study showed a significant increase of the complication rate (13% to 18%). The increase was mainly due to a rise in less severe complications, in particular delirium (0.4% to 4.8%) and gastro-intestinal dysfunction (2%-14%). A higher age, male gender, higher American Society of Anaesthesiologists (ASA) class, and surgical complexity were associated with a higher complication rate.

In Chapter 3 we studied the completeness of the national complication registry database (LHCR) as used at the Department of Surgery in our hospital. The department’s complication database currently used, only contains complications that are reported during morning hand-offs and in discharge letters. We compared this registry with relevant information from other available resources in a retrospective reliability analysis. From the 3252 patients admitted to the surgical wards in 2010, the authors randomly selected a cohort of 180 cases, oversampling those with complications. The authors checked whether the number and severity of complications as recorded in the department’s complication database agreed with those reported in morning hand-offs, discharge letters, and medical and nursing files. Of all patients with complications 86% were recorded in the

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department’s complication database, 14% was not recorded. Missing complications were relatively ‘minor complications’, meaning reversible without the need for (re)operation. Only 38% of these complications were reported in the morning hand-offs and discharge letters but minor complications were better reported in the medical and nursing files (rep. 60% and 67%). Therefore, all likely resources should be incorporated for an optimum registration of complications. The nursing file, although not (yet) digitised, seems to be the most appropriate additional resource for this purpose. Furthermore, the registration of surgical complications appeared largely depending on the reliability of the underlying sources. Reluctance or negligence among surgeons to report complications due to possible disincentives like fear, shame, loss of reputation, and peer disapproval should be addressed. The shame-and-blame culture should be abandoned by creating awareness that surgical complications are frequently system errors rather than individual unreliability.

Post-discharge complications of patients are not systematically included in the department’s complication database as used in our hospital. These complications are registered only if they result in re-admission or re-intervention in our hospital. In Chapter 4 we investigated which method, a telephone interview or a questionnaire by mail, would be the best method to collect post-discharge complications as reported by the patients. We performed a randomised clinical equivalence trial. From December 2008 until August 2009, all adult surgical patients admitted to a university hospital were randomised to be approached by mail or by phone 30 days after discharge, 890 by means of a telephone interview and 705 through a questionnaire, to collect information about post-discharge complications. In all, 1595 patients were contacted. The response rate was higher in the telephone group than in the questionnaire group (63.8% vs. 51.3%). This trial showed that about 40% of surgical patients reported experiencing one or more complications during the 30-day period after discharge, most of which required additional non-operative or surgical treatment. The two survey methods did not differ significantly in their ability to appreciate post-discharge complications as reported by the patients: 43.3% in the telephone group versus 39.6% in the questionnaire group. Length of stay, ASA class, and type of surgery, but not the survey techniques compared here, significantly influenced the number of complications reported. Complications categorised as a ‘‘functional disorder’’ (e.g., ileus, gastro paresis, weight loss) were reported twice as much in the telephone group. In the questionnaire group, the most frequently (19%) reported complication belonged to the category ‘‘other’’ (e.g., mental disorder). The decision to use either method may be determined by the institution, costs involved and labour requirement. Certainly, information about complications after discharge is valuable for improving the quality of care and for informing the patient about the benefits and risks of a treatment.


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In Chapter 5 patient-reported complications from the previous study (chapter 4) were compared with surgeon-reported complications to determine the frequency, type, and grade of post-discharge complications in a prospective cohort study. These patient-reported complications were compared with the surgeon-reported post-discharge complications and letters from the outpatient clinic as documented in the patients’ medical files.A total of 976 patients were included. Patients reported more complications (659) than surgeons (465). The most frequently reported complication types were symptoms without specific diagnoses (e.g. pain or fever), infections, and functional disorders (e.g. bowel or cardiac problems, weight loss). Patients reported significantly more complications related to injury by mechanical, physical or chemical cause to the surgical site (RD 2.2%; 95% CI 0.8–3.5), and psychological disturbances (RD 4.3%; 95% CI 2.7–5.8), whereas surgeons did not register any such complications. Surgeons reported significantly more abnormal wound healing than patients (RD 5.9%; 95% CI 2.9–8.9).Of the patient-reported post-discharge complications 94% were treated non-invasively. Patients sought medical help or advice for 84% (N = 527) of the complications they reported. Of these, more than half (N = 291) were presented to the outpatient clinic (27 of which in another hospital), while 17% (N=92) of the complications were seen in the Emergency Department. For the remaining 27% (N=144) of the complications, GPs were consulted. The complications presented to GPs concerned pain (N = 39), infection (N = 32), and wound problems (N = 11). These complications were all treated non-surgically.One in four post-discharge complications in surgical patients were missed by the attending surgeon. Most of these patients with complications were diagnosed and treated by the GPs. Surgeons should anticipate to common post-discharge complications and communicate with their patients about what to do, should this happen, to avoid unnecessary involvement of, or referral to, other healthcare professionals.

PART II The trend to develop national benchmarking data, including those regarding complications in hospitalised surgical patients, is growing. To obtain high-quality benchmarking data a reliable and uniform registration by the participating surgical departments is required. In Chapter 6 the amount of agreement and potential differences in the application and interpretation of the definition of a complication was investigated among the surgical departments of 7 Dutch hospitals. Twenty-four clinical cases were formulated including general, trauma, gastrointestinal and vascular surgery. These were based on points of discussion about the definition and ambiguities regarding complication registration as encountered in daily practice at the Academic Medical Centre in Amsterdam (AMC). The clinical cases were presented to the surgical staff and residents in seven Dutch hospitals using an electronic response system.

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A total of 134 participants responded. More than 50% of participants were practising at a university medical centre, almost 40% in a tertiary referral hospital and around 10% in a general training hospital. The main interpretation differences were found regarding: 1) Complications considered to be directly related to the surgical procedure, such as

gastro paresis after a gastrectomy or ongoing bowel paralysis following adhesiolysis. 2) Complications occurring after treatment by other specialities such as interventional

radiology procedures. 3) Severity grading; criteria regarding when to consider a complication as a ‘(probably)

permanent damage or function loss’. 4) Registering a cancelled operation as a complication.5) Patients with serial complications during hospital stay. For example intra-abdominal

abscess and wound infection; registered as one or 2 complications.

Given the considerable differences in interpretation of the current definition of a complication, it is unlikely that uniform registration of complications is currently performed. This uniformity may be enhanced by additions to the current definition, by more agreement about specific clinical situations, and by training of surgeons, thereby improving comparisons at both local and national levels. This seems a prerequisite before such data can be used at the public domain and function as one of the parameters for the quality of healthcare.

In Chapter 7 a systematic review was performed to summarise known factors that can detect surgical complications. We searched all publications addressing predictive factors for the development of surgical complications in adult patients admitted to the gastrointestinal, vascular or general surgery departments. A final set of 30 articles yielded a total of 53 predictive factors studied in various settings, surgical specialties, and disorders. To focus the analysis the 25 most robust and clinically applicable factors (defined as appearing in at least 3 studies) were selected. These factors were then categorised into 4 different groups: Patient-related factors, Co-morbidities, Laboratory values, and Surgery-related factors. The most predictive factors for morbidity in these groups were BMI (with odds ratios (ORs) ranging from 1.80-6.30), higher age (ORs 1.02-4.62), ASA classification (ORs 1.77-7.10), dyspnoea (ORs 1.23-1.30), serum creatinine level (ORs 1.39-2.14), emergency surgery (ORs 1.50-2.54) and functional status (ORs 1.36-4.07). Thus, an overview of several predictors for surgical complications was found that are likely candidates to be used in a trigger tool to help identify patients at risk for a complication.

In Chapter 8 these predictive factors were used to develop a trigger tool that might be useful to improve complication detection. Simultaneously, a diagnostic study was performed to compare the sensitivity, specificity, and time consumption of the new trigger tool with the


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standardised clinical registration method. A set of 31 potential triggers was chosen based on the systematic review (chapter 7) and availability of those triggers in hospital databases. A reference standard consisted of 300 patients, 150 with and 150 without complications. The developed trigger tool consisted of 9 triggers, containing: emergency procedure, complexity of surgical procedure above severity class 6, Do Not Resuscitate (DNR) policy, ICU-stay, length of hospital stay of more than 14 days, reoperation, oesophagectomy, pancreatoduodenectomy, acute (or ruptured) abdominal aortic aneurysm surgical procedure. Sensitivities of the trigger tool and standardized clinical registry were 70.7% vs. 78.7%, respectively, while specificities were 70% vs. 100.0%, respectively. Sensitivity values to detect major complications were 97.2% vs. 80.6%, respectively. Time spent by the attendants during morning hand-offs using the standardised clinical registration was 296 hours per year and 133 hours for the database manager. Using the trigger tool, only the database manager would spend 310 hours. Thus, this trigger tool appeared effective to detect patients at risk of complications, particularly the more severe complications, and might save costs.

To estimate the impact of complications in terms of the costs involved, Chapter 9 contains a retrospective cohort study, conducted in 100 consecutive patients who underwent a (pylorus-preserving) pancreatoduodenectomy (PP)PD between January 2012 and July 2013. For each patient, complications occurring during admission or in the 30-day period after discharge were documented. Hospital costs related to the (PP)PD were defined as all costs of medical interventions and resources during the hospitalisation period as recorded by the electronic supply tracking system.Median hospital costs ranged from €17,482 for a patient without complications to €55,623 for a patient with postoperative haemorrhage. After adjusting for patient characteristics, postoperative haemorrhage was associated with an increase of 39.6% in total hospital costs mainly due to increased hospital stay. Other factors significantly associated with an increase in total hospital costs were: the presence of a malignancy other than pancreatic adenocarcinoma (29.4% cost increase), the severity grade of a complication (34.3% - 70.6% increase) and the presence of a postoperative infection (32.4% increase). Complications can lead to substantial extra costs for the hospital, particularly for diseases commonly treated in tertiary care institutions. With this knowledge we can and will advocate further efforts to reduce hospital costs by shortening the length of hospital stay.

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FUTURE PERSPECTIVES

Quality control and transparency of outcomes of (surgical) care have become and will remain a priority for hospitals. Recording of complication rates is one of the parameters of quality control. The validity of registration and prevention of complications is an ongoing challenge. One in every 150 patients admitted to a hospital dies as a consequence of a complication, while almost two thirds of in-hospital events are associated with surgical care.1 The burden for patients and surgeons may be unbearable. Hence, the need for improvement is clear. For the last 15 years substantial investments have been made to improve patient safety. Although improving is a relative slow process, some milestones have been reached. For example, the implementation of the SURPASS checklist in The Netherlands led to a reduction in surgical complications and mortality in hospitals with a high standard of care.2

Improving RegistrationSuccessful improvement depends on the validity of complication registry. To obtain high-quality data, reliable and uniform registration by the participating surgical departments is required.3 Also, interpretation differences regarding specific clinical situations should be reconciled and regulated by the professional society, and by training of (future) surgeons, thereby improving the dataset for comparisons at both local and national levels. Major complications are rarely missed in the registrations. In contrast, the minor complications that occur during hospitalisation or after discharge tend to be overlooked. This implies that a possible rising trend in minor complications will be missed, which would preclude improvement initiatives that could easily lead to a shorter hospital stay and a reduction of costs and patient burden. For the purpose of obtaining and utilising valid and complete data, hospitals ought to register any complications.Effort should be made to implement systematic electronic storage and automated triggers in modern hospital data systems. Automated triggering would help surgeons as it reduces their time spent in complication registration, but supposes a greater role for hospital data managers.

Improving Quality of care Surgeons are obliged to compare the possible unintended harm versus the intended positive results of their interventions and to communicate these findings with their patients before a treatment choice is made.


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Shared Decision-making One of the promising possibilities to achieve better healthcare quality is to enhance the role of patients in decision-making and management as to their health and care, based on individual benefits and risks.4 The growing ICT-opportunities and internet facilities allow for a better integration of patient data and preferences, medical guidelines and evidence on the effectiveness of treatment options, prediction models of health prognosis and risks (complications) of interventions, and decision support systems. Effort should be made to engage (surgical) patients for whom several treatment options are available in their healthcare process and selection for treatment to improve the quality of their care and life. This could be achieved by offering the patients and their clinicians an interactive patient-specific support and decision-making system in the hospital setting. Here also, valid data from complication registries is mandatory for these prediction models and risks of interventions in decision-making systems.

Cost EffectivenessTotal hospital costs after surgery increase substantially if a complication occurs. The increase in hospital costs due to complications can mainly be ascribed to a prolonged hospital stay.5,6 Length of hospital stay can be influenced by implementing specific protocols or programmes to reduce hospital stay (e.g., fast-track surgery) or through cooperation with other hospitals and skilled nursing facilities. The use of patient-related or surgery-related triggers as so-called ‘red flags’, highlighting the patients who are at risk of developing complications, could also be useful in the improvement of complication prevention. Prior to admission and surgery some factors modifying the outcome could be addressed, like a preoperative nutritional intervention, preoperative alcohol and smoking cessation, which may diminish the risk of developing complications.7-9 Prevention and early diagnosis of complications by the implementation of several evidence-based bundles could contribute to reduction.

Prevention of ComplicationsComplications should preferably be avoided, but some complications may be accepted as a ‘calculated risk’ if it is outweighed by the anticipated positive effect(s) of the intervention. Actions should be taken for implementing methods like the SAVE-method; a multidisciplinary, proactive and structured complication meeting, to detect potentially preventable complications.10 The main purpose of these methods, based on chart reviews, is not to count preventable harm, but to gain an insight into the bottleneck of the healthcare process, and to eventually improve the quality of care. The bottlenecks can occur in processes at department level as well as processes at hospital level.

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The Need for Complication Registration in the FutureAlthough academic hospitals seem more subspecialty-driven, this represents only a small part of all surgical care. Most subspecialties have their specific registration system such as the national audits for colorectal, pancreatic, and oesophageal surgery.11 A generic registration still is of importance for general departments of surgery in smaller hospitals but equally for the academic hospitals. Moreover, complication registration is an outcome-driven registration. It enables us to review trends in complications, such as an increasing postoperative infection rate. These trends should be reviewed and analysed on the higher level of general surgery because the processes or actions for improvement may transcend the subspecialty.

A further development could be a shift in perspective from a specialty-driven to a patient-driven registration. Working in multidisciplinary teams has become increasingly more important in healthcare; GI-surgeons form teams with endoscopists, vascular surgeons with interventional radiologists. Some years ago, the report entitled “To err is human” also argued in favour of teamwork, a concept that might be able to prevent a large number of avoidable complications.10 This pleads for a more consistent registration of all complications, meaning that all complications of a patient developed during the hospital stay at any location should be registered, regardless of which specialty identified the complication. The data can subsequently be used for improvement actions. Thus, registration of complications may evolve from a necessary burden to an essential tool for improving quality of surgical care. Furthermore, patient-specific information may be used in decision-making within a specific hospital, department or individual patient. The responsibility for the treatment choice and its potential risk for complications should be in the hands of both surgeon and patient.Promising future possibilities will lead to a joint responsibility of multidisciplinary teams and the very patient, ensuring high quality of care.


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REFERENCES

1. de Vries EN, Ramrattan MA, Smorenburg SM, et al. The incidence and nature of in-hospital adverse events: a systematic review. Qual Saf Health Care. 2008;17:216-23.

2. de Vries EN, Prins HA, Crolla RMPH et al. Effect of comprehensive surgical safety system on patient outcomes. N Eng J Med. 2010; 63:1928-37.

3. Veen EJ, Janssen-Heijnen MLG, Bosma E, et al. The accuracy of Complications Documented in a Prospective Complication Registry. J Surg Res 2012;173:54.

4. Stiggelbout AM, Van der Weijden T, De Wit MP, et al.Shared decision making: really putting patients at the centre of healthcare. BMJ. 2012 Jan 27;344:e256.

5. Rotter T, Kinsman L, James E, et al. The effects of clinical pathways on professional practice, patient outcomes, length of stay, and hospital costs: Cochrane systematic review and meta-analysis. Eval Health Prof. 2012;35(1):3-27.

6. Hoonhout LHF, de Bruijne MC, Wagner C, et al. Direct medical costs of adverse events in Dutch hospitals. BMC Health Services Research 2009, 9:27.

7. Burden S, Todd C, Hill J et al. Pre-operative nutrition support in patients undergoing gastointestinal surgery. Cochrane Database Syst Rev. 2012;11:CD008879.

8. Oppendal K, Møller AM, Pedersen B, et al. Preoperative alcohol cessation prior to elective surgery Cochrane Database Syst Rev. 2012;7:CD008343.

9. Wiggers LC, Smets EM, Oort FJ, Peters RJ, Storm-Versloot MN, Vermeulen H, de Haes HC, Legemate DA. The effect of a minimal intervention strategy in addition to nicotine replacement therapy to support smoking cessation in cardiovascular outpatients: a randomized clinical trial. Eur J Cardiovasc Prev Rehabil. 2006 Dec;13(6):931-7.

10. http://www.clinicalaudit.nl

‘White Air” has a balloon filled up with white air. It helps the little butterfly to rice up. Anne ten Donkelaar.

Chapter 11

Samenvatting en Toekomstperspectieven

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SAMENVATTING

Transparantie in complicaties binnen een specifiek ziekenhuis en afdeling, en zelfs op nationaal niveau, geeft inzicht in de kwaliteit van zorg en in de potentiële verbetergebieden. Het onderzoek in dit proefschrift richt zich op de kwaliteit van de complicatieregistratie bij chirurgische patiënten. Het richt zich met name op de registratie van gedetailleerde informatie over de incidentie, het aantal en de ernst van de opgetreden complicaties. Bovendien richt het onderzoek zich op de lacune van de huidige registratie en op verschillende initiatieven om de kwaliteit van de complicatiedata te verbeteren. Ten slotte geeft het onderzoek inzicht in de ziekenhuiskosten van complicaties bij chirurgische patiënten.Dit proefschrift bestaat uit 2 delen. Het eerste deel richt zich op de kwaliteit en kwantiteit van de complicatieregistratie, het tweede deel richt zich op het verbeteren van de kwaliteit van de complicatieregistratie.

DEEL 1

In hoofdstuk 2 hebben we de complicaties in een academisch ziekenhuis over de periode van zes jaar geanalyseerd om het effect te onderzoeken van de registratie en het registratiesysteem op de incidentie van complicaties en de mogelijke trends in deze periode. Gedurende de periode 2004-2009 werden alle opgenomen en ontslagen volwassen chirurgische patiënten geselecteerd voor deze time-trend studie. De Nationale Heelkunde Complicatie Registatie (LHCR) werd gebruikt bij de analyse, waar complicaties zijn geregistreerd volgens een 3-assenstelsel classificatiesysteem. Dit onderzoek liet een significante stijging zien van het complicatiepercentage (van 13% naar 18%). Deze stijging werd hoofdzakelijk veroorzaakt door minder ernstige complicaties, met name delier (van 0.4% naar 4.8%) en gastro-intestinale stoornissen (2% naar 14%). Een hogere leeftijd, het mannelijk geslacht, een hogere ASA klasse (American Society of Anaesthesiologists) en de complexiteit (zwaarteklasse) van de operatie werden geassocieerd met een hoger complicatiepercentage.

In hoofdstuk 3 onderzochten wij de volledigheid van de LHCR zoals gebruikt op de afdeling chirurgie van ons ziekenhuis. De huidige complicatie database van de afdeling bevat alleen complicaties die zijn gerapporteerd tijdens de overdracht en in de ontslagbrieven. We vergeleken deze registratie met relevante informatie uit andere beschikbare bronnen middels een retrospectieve betrouwbaarheidsanalyse.Van de opgenomen patiënten op de chirurgische afdelingen in 2010 (N=3252) werd een groep van 180 patiënten geselecteerd middels een aselecte steekproef, met een

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hogere sample frequentie van de patiënten met complicaties. De auteurs controleerden of het aantal en de ernst van de geregistreerde complicaties overeen kwamen met de complicaties die gerapporteerd werden tijdens de overdracht, in de ontslagbrief, medische status en verpleegstatus. Van alle patiënten met complicaties werd 86% geregistreerd in de complicatie database van de afdeling, 14% werd niet geregistreerd. De niet geregistreerde complicaties waren relatief milde complicaties, namelijk complicaties die goed behandeld konden worden zonder reoperatie en zonder blijvende schade. Slechts 38% van de milde complicaties werd gerapporteerd tijdens de overdracht en in de ontslagbrief. Deze werden beter gerapporteerd in de medische en verpleegkundige status (rep. 60% en 67%). Derhalve zouden al deze bronnen gebruikt moeten worden voor een optimale registratie van complicaties. Om dit doel te bereiken lijkt de verpleegkundige status, hoewel (nog) niet gedigitaliseerd, de meest geschikte additionele bron. Bovendien lijkt de registratie van chirurgische complicaties in grote mate afhankelijk te zijn van de betrouwbaarheid van de onderliggende bronnen. Aan de terughoudendheid of nalatigheid onder chirurgen om complicaties te rapporten door mogelijke negatieve prikkels als angst, schaamte, reputatieverlies en afkeur van hun meerderen, zou de nodige aandacht moeten worden geschonken. De ‘shame-and-blame’ cultuur zou moeten verdwijnen door bewustwording te creëren aangaande het feit dat complicaties vaker systeemfouten zijn dan individuele fouten.

Complicaties na ontslag zijn niet systematisch geïncludeerd in de huidige complicatieregistratie van de afdeling in ons ziekenhuis. Deze complicaties worden alleen geregistreerd als ze resulteren in heropname of reoperatie. In hoofdstuk 4 onderzochten we welke methode, een telefonisch interview of een schriftelijke vragenlijst, de beste methode zou zijn om complicaties na ontslag, gerapporteerd door de patiënt, te verzamelen. We hebben een gerandomiseerd klinisch onderzoek (RCT) uitgevoerd. In de periode van december 2008 tot augustus 2009 werden alle volwassen chirurgische patiënten, opgenomen in een universitair medisch centrum, gerandomiseerd voor een benadering per mail of per telefoon: 890 via een telefonisch interview en 705 via een schriftelijke vragenlijst. In totaal werden 1595 patiënten benaderd. De respons in de telefoongroep was hoger dan in de vragenlijstgroep (63.8% vs. 51.3%). Het onderzoek toonde aan dat 40% van de chirurgische patiënten een of meer complicaties rapporteerde gedurende de 30 dagen na ontslag periode. De meesten werden behandeld zonder chirurgische interventies. De twee methoden verschilden niet significant in hun vermogen om patiënt-gerapporteerde complicaties na ontslag te verzamelen: 43.3% in de telefoongroep versus 39.6% in de vragenlijstgroep. Ligduur, ASA klasse en type operatie, maar niet de methodes die hier worden vergeleken, beïnvloedden significant het aantal gerapporteerde complicaties. Complicaties in de categorie ‘gestoorde functie’ (bijv. gastroparese, gewichtsverlies) werden


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twee maal zoveel gerapporteerd in de telefoongroep. In de vragenlijstgroep kwamen de meest gerapporteerde complicaties uit de categorie ‘overig’ (19%) (bijv. psychische stoornis). De beslissing voor het gebruik van een de twee methoden zal afhankelijk zijn van de registratiewensen van de instelling, de kosten die ermee gemoeid zijn, en de personele inzet die de verschillende methodes vereisen. Het staat vast dat informatie over complicaties na ontslag waardevol is bij het verbeteren van de kwaliteit van zorg en bij het informeren van de patiënt over de voordelen en risico’s van een behandeling.

In hoofdstuk 5 werden in een prospectieve cohort studie de frequenties, het type en de ernst van de complicaties in de periode na ontslag te bepaald van de door de patiënt gerapporteerde complicaties uit de voorgaande studie (hoofdstuk 4) en de door de chirurg gerapporteerde complicaties. Deze patiënt-gerapporteerde complicaties werden vergeleken met de door de chirurg gerapporteerde complicaties in de medische status. In totaal werden 976 patiënten geïncludeerd. Patiënten rapporteerden meer complicaties (659) dan chirurgen (465). De meest gerapporteerde complicaties waren ‘symptomen zonder diagnose’ (bijv. pijn of koorts), infecties en ‘gestoorde functie’ (bijv. darm of cardiale problemen en gewichtsverlies). Patiënten rapporteerden significant meer complicaties gerelateerd aan letsel in het operatiegebied zoals losse hechtingen (RD 2.2%; 95% CI 0.8–3.5), en aan psychische problemen (RD 4.3%; 95% CI 2.7–5.8) terwijl chirurgen deze complicaties niet rapporteerden. Chirurgen rapporteerden significant meer afwijkende wondgenezing dan patiënten (RD 5.9%; 95% CI 2.9–8.9).Van de patiënt-gerapporteerde complicaties na ontslag werd 94% niet invasief behandeld. Patiënten zochten medische hulp of advies in 84% (N=527) van de complicaties die ze rapporteerden. Van deze complicaties werden meer dan de helft (N=291) van deze complicaties gepresenteerd op de polikliniek (waarvan 27 in een ander ziekenhuis), terwijl 17% (N=92) van de complicaties werden gepresenteerd op de eerste hulp. De huisarts werd geconsulteerd voor de overige 27% (N=144) van de complicaties. De complicaties die aan de huisarts werden gepresenteerd waren met name pijn (N=39), infectie (N=32) en wondproblemen (N=11). Deze complicaties werden allen niet chirurgisch behandeld.Deze studie toonde aan dat een op de vier complicaties na ontslag bij chirurgische patiënten werden gemist door de chirurg. De meeste van deze complicaties werden gediagnostiseerd en behandeld door de huisarts. Chirurgen zouden moeten anticiperen op veel voorkomende complicaties na ontslag en met de patiënt bespreken hoe ze moeten handelen mocht een complicatie optreden. Met als doel onnodige betrokkenheid van, of doorverwijzing naar, andere zorgprofessionals te voorkomen.

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De tendens om op landelijk spiegelinformatie te ontwikkelen en te gebruiken groeit, ook wat betreft complicaties bij chirurgische patiënten. Een voorwaarde voor deze benchmarking is een hoge kwaliteit van de data. Daarvoor moet de registratie van de verschillende chirurgische afdelingen betrouwbaar en uniform zijn. Dit kan alleen als alle ziekenhuizen in vergelijkbare situaties dezelfde complicaties eenduidig registreren. In hoofdstuk 6 werd de mate van overeenstemming in het toepassen en interpreteren van de definitie van een complicatie binnen en tussen de afdeling chirurgie van 7 Nederlandse ziekenhuizen onderzocht. Vierentwintig klinische casussen werden opgesteld over algemene chirurgie, trauma chirurgie, gastro-intestinale chirurgie en vaatchirurgie. De casussen werden geformuleerd op basis van discussiepunten, definities en twijfelgevallen bij het registreren van complicaties in de dagelijkse praktijk in het Academisch Medisch Centrum Amsterdam (AMC). De casussen werden voorgelegd aan de chirurgen en assistenten van 7 Nederlandse ziekenhuizen en de antwoorden werden vastgelegd met elektronische stemkastjes.In totaal hebben 134 chirurgen en assistenten aan het onderzoek deelgenomen. Meer dan 50% van de deelnemers was werkzaam in een academisch ziekenhuis, bijna 40% in een topklinisch ziekenhuis en rond 10% in een algemeen opleidingsziekenhuis. De belangrijkste verschillen in interpretatie die we vonden, betroffen:1) Complicaties die direct gerelateerd waren aan de ingreep en regelmatig voorkomen,

zoals gastroparese na een gastrectomie of een persisterende ileus na adhesiolyse.2) Complicaties die optreden na een behandeling door een ander specialisme bijvoorbeeld

een radiologische interventie.3) Het bepalen van de ernst van de complicatie; de criteria welke bepalen of het gevolg

van de complicatie ‘ (waarschijnlijk) blijvende schade’ betreft.4) Het registreren van een geannuleerde operatie als complicatie.5) Patiënten met meerdere opeenvolgende complicaties tijdens de opname. Bijvoorbeeld

intra-abdominale abcessen en wondinfectie; registeren als een of twee complicaties.

Gezien de aanzienlijke verschillen in de interpretatie van de huidige definitie van een complicatie, is het onwaarschijnlijk dat de huidige registratie van complicaties op uniforme wijze wordt uitgevoerd. Het aanpassen van de huidige definitie zou dus moeten leiden tot een hogere uniformiteit. Het vervolgens opleiden en trainen van artsen om zich de definitie eigen te maken en kennis op te doen over landelijke afspraken over specifieke situaties draagt bij aan meer eenduidige registratie. Dit lijkt zeker noodzakelijk voordat de registratiegegevens op landelijk niveau gebruikt kunnen worden voor het vergelijken van de uitkomst van zorg.


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In hoofdstuk 7 werd een systematisch literatuuronderzoek uitgevoerd met als doel een overzicht te generen met factoren die chirurgische complicaties identificeren. We hebben gezocht naar alle publicaties die zich richten op voorspellende factoren voor het ontwikkelen van chirurgische complicaties bij volwassen patiënten die waren opgenomen op de gastro-intestinale, vaat of algemene chirurgische afdeling. Een uiteindelijke selectie van 30 artikelen leverde in totaal 53 voorspellende factoren op, onderzocht in verschillende settingen, subspecialismen en aandoeningen. De 25 meest robuuste en klinisch relevante factoren (gedefinieerd als in 3 studies of meer worden beschreven) werden geselecteerd om de analyse te verscherpen. Deze factoren werden vervolgens gecategoriseerd in 4 verschillende groepen: Patiënt-gerelateerde factoren, comorbiditeit, laboratorium waarden en operatie-gerelateerde factoren. De meest voorspellende factoren voor morbiditeit in deze groepen waren BMI (met odds ratios (ORs) van 1.80-6.30), hogere leeftijd (ORs 1.02-4.62), ASA classificatie (ORs 1.77-7.10), dyspneu (ORs 1.23-1.30), serum creatinine waarde (ORs 1.39-2.14), acute operaties (ORs 1.50-2.54) en functionele status (ORs 1.36-4.07). Het overzicht van de in deze studie gevonden voorspellende factoren zijn plausibele factoren die gebruikt kunnen worden in een triggertool. Het gebruik van deze triggertool draagt bij aan het eenvoudiger identificeren van patiënten met een hoger risico op complicaties.

In hoofdstuk 8 werden deze voorspellende factoren gebruikt om een triggertool te ontwikkelen dat mogelijk gebruikt kan worden om de detectie van complicaties te verbeteren. Daarnaast werd in deze studie ook een diagnostische studie uitgevoerd waarbij de sensitiviteit van, de specificiteit van en de tijdsbesteding aan de nieuwe triggertool werden vergeleken met die van de huidige standaard klinische registratie. Uit het voorgaande literatuuronderzoek (hoofdstuk 7) en op basis van de beschikbaarheid in de ziekenhuissystemen werden 31 triggers geselecteerd. Een referentiestandaard bevatte 300 patiënten; 150 met en 150 zonder complicaties. De ontwikkelde triggertool bestond uit 9 triggers; acute operatie, zwaarteklasse van de operatie boven klasse 6, niet reanimeren beleid (DNR), IC opname, ligduur van meer dan 14 dagen, reoperatie, oesophagusresectie, whipple procedure en een operatie voor een acuut (of geruptureerd) aneurysma van de abdominale aorta. Sensitiviteit van de triggertool en de standaard klinische registratie was resp. 70.0% vs. 78.7, de specificiteit was resp. 97.2% vs. 80.6%. De aanwezige chirurgen en assistenten besteden 296 uur per jaar aan de huidige methode van standaard klinische registratie en de database manager 133 uur per jaar. De triggertool methode vraagt een tijdsbesteding van 310 uur per jaar, maar alleen van de database manager. Deze triggertool is effectief in het detecteren van patiënten met een verhoogd risico op een complicatie, met name bij de complicaties met een hogere ernst, en bespaart mogelijk kosten voor de chirurgische afdeling

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Om de impact van complicaties in termen van kosten in te kunnen schatten is een retrospectieve cohort studie uitgevoerd (hoofdstuk 9). In deze studie werden 100 opeenvolgende patiënten die een (pylorus sparende) pancreatoduodenectomie, (PP)PD, hebben ondergaan tussen januari 2012 en juli 2013 geïncludeerd. Bij deze patiënten werden de opgetreden complicaties geregistreerd gedurende de periode tijdens de opname en tot 30 dagen na ontslag. Ziekenhuiskosten gerelateerd aan de (PP)PD werden gedefinieerd als alle kosten voor medische interventies en resources, zoals vastgelegd in het elektronische voorraad systeem, gedurende de opnameperiode.Mediane kosten varieerden van €17,482 voor een patiënt zonder complicaties tot €55,623 voor een patiënt met een postoperatieve bloeding. Na correctie voor patiënt-karakteristieken was postoperatieve bloeding geassocieerd met 39.6% stijging van de totale ziekenhuiskosten, met name veroorzaakt door verlengde ligduur. Andere significant geassocieerden factoren waren de aanwezigheid van een andere maligniteit dan adenocarcinoom van het pancreas (29.4% kosten stijging), de ernst van de complicatie (34.3%-70.6% toename) en de aanwezigheid van een postoperatieve infectie (32.4% toename).Complicaties kunnen leiden tot substantiële extra kosten voor het ziekenhuis, met name bij ziekenhuizen met tertiaire zorgverlening. Met deze kennis kunnen we verdere inspanningen leveren om ziekenhuiskosten te reduceren door de ligduur te verkorten.

TOEKOMSTPERSPECTIEVEN

Kwaliteitscontrole en transparantie van uitkomsten van (chirurgische zorg) zijn en blijven een prioriteit voor ziekenhuizen. Het registreren van complicaties is een van de parameters voor kwaliteitscontrole. De betrouwbaarheid van de registratie en het voorkomen van complicaties blijft een uitdaging. Een op 150 opgenomen patiënten in een ziekenhuis overlijdt als gevolg van complicaties, en bijna twee derde van de complicaties tijdens de opname zijn gerelateerd aan de chirurgische zorg.1 De last van complicaties wordt gedragen door patiënten en chirurgen. De noodzaak voor verbetering is duidelijk. De afgelopen 15 jaar zijn er aanzienlijke inspanningen geleverd om de patiëntveiligheid te verbeteren. Al lijkt verbeteren een relatief traag proces, er zijn zeker mijlpalen bereikt. Bijvoorbeeld, de implementatie van de SURPASS-checklijst in Nederland heeft geleid tot een reductie van chirurgische complicaties en sterfte in ziekenhuizen met kwalitatief hoogwaardige zorg.2

Verbeteren van de registratieSuccesvolle verbeteringen zijn afhankelijk van de betrouwbaarheid van de complicatieregistratie. Voor het verkrijgen van data van hoge kwaliteit is een betrouwbare


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en uniforme registratie door de deelnemende chirurgische afdelingen noodzakelijk.3 Over de interpretatie verschillen wat betreft de specifieke klinische situaties zou overeenstemming bereikt moeten worden. Dit zou gereguleerd moeten door de beroepsverenigingen, om dit vervolgens mee te kunnen nemen in de training van de (toekomstig) chirurgen en zo de data te verbeteren die gebruikt wordt voor vergelijkingen op zowel lokaal als nationaal niveau. Ernstige complicaties worden zelden gemist in de registratie. De mildere complicaties die optreden tijdens opname of na ontslag daarentegen worden vaak over het hoofd gezien. Dit impliceert dat een mogelijke toenemende trend in mildere complicaties gemist zou kunnen worden en dit zou verbeterinitiatieven in de weg staan. Deze verbeteringen zouden mogelijk een reductie in ligduur, een reductie in kosten en lastvermindering voor de patiënt kunnen opleveren. Om betrouwbare en volledige data te verkrijgen zouden ziekenhuizen elke complicatie moeten registreren. Er zouden meer inspanningen geleverd moeten worden om elektronische data opslag in de moderne ziekenhuissystemen te implementeren. Automatische triggers kunnen dan chirurgen attenderen op risicopatiënten en tijd besparen bij het registreren van complicaties. Dit vraagt wel om een grotere rol van de datamanagers in het ziekenhuis.

Verbeteren van de kwaliteit van zorgChirurgen hebben de plicht om de mogelijke onbedoelde schade ten opzichte van de bedoelde positieve resultaten van hun behandelingen te vergelijken en de bevindingen te communiceren met hun patiënten voordat een beslissing wordt genomen over behandeling.

Gedeelde besluitvormingEen van de veelbelovende mogelijkheden om zorg te optimaliseren is de rol van de patiënt te vergroten in gedeelde besluitvorming en in het managen van hun gezondheid en zorg gebaseerd op individuele opbrengsten en risico’s.4 De toenemende ICT-mogelijkheden en internetfaciliteiten zorgen voor een betere integratie van patiëntdata en -voorkeuren, medische richtlijnen en evidence voor de effectiviteit van een behandeloptie, voorspellende modellen voor gezondheidsprognoses en risico’s (complicaties) van interventies, en gedeelde besluitvorming systemen. Er moet naar worden gestreefd de (chirurgische) patiënt met verschillende behandelopties in het ziekteproces, te betrekken bij de selectie voor behandeling. En hiermee de kwaliteit van zorg en leven van de patiënt te verbeteren. Dit kan door de patiënten en hun behandelaars een interactief en patiënt-specifiek gedeelde besluitvorming-systeem aan te bieden. Ook hier zijn betrouwbare data uit complicatie registraties een vereiste voor voorspellende modellen en gedeelde besluitvormingssystemen.

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KosteneffectiviteitDe totale ziekenhuiskosten na chirurgie stijgen aanzienlijk als een complicatie optreedt. De stijging van de ziekenhuiskosten door complicaties kunnen hoofdzakelijk worden toegeschreven aan de verlengde ligduur.5,6 Ligduur kan worden verkort door het implementeren van specifieke protocollen of programma’s (bijv. fast-track chirurgie) of door samenwerking met andere ziekenhuizen en gekwalificeerde verpleegkundige instellingen. Het gebruik van patiënt-gerelateerde of operatie-gerelateerde triggers, zogenaamde ‘rode vlaggen’, die de patiënten met een verhoogd risico op een complicatie markeren, kunnen bijdragen in het verbeteren van de preventie van complicaties. Al voor de opname en operatie kunnen bepaalde factoren (vooraf) de uitkomst beïnvloeden, zoals preoperatieve voedingsinterventie, preoperatief stoppen met alcohol en roken. Daarmee kan het risico op het ontstaan van complicaties worden verminderd.7-9 Preventie en vroege diagnose van complicaties door de implementatie van verschillende evidence-based bundels kunnen ook bijdragen aan het verminderen van complicaties.

Preventie van ComplicatiesComplicaties moeten bij voorkeur worden vermeden maar sommige complicaties kunnen worden geaccepteerd als ‘ingecalculeerd risico’ indien de beoogde positieve effecten van de interventie zwaarder wegen dan de risico’s. Maatregelen zouden moeten worden genomen om methodes te implementeren als de SAVE-methode; een multidisciplinair, proactief en gestructureerde complicatiebespreking om complicaties die mogelijk te voorkomen zijn te identificeren.10 Het doel van deze methode, gebaseerd op status onderzoek, is niet om het aantal te voorkomen complicaties in kaart te brengen maar om inzicht te krijgen in de knelpunten van het zorgproces en uiteindelijk de kwaliteit van zorg te verbeteren. De knelpunten kunnen zich voordoen bij afdelingsprocessen maar ook bij zorgprocessen op ziekenhuisniveau.

De Noodzaak van Complicatieregistratie in de ToekomstAcademische ziekenhuizen zijn steeds meer gericht op subspecialismen echter deze zijn slechts representatief voor een beperkt deel van alle chirurgische zorg. De meeste subspecialismen hebben hun eigen specifieke registratiesysteem zoals de nationale audits voor colorectale, pancreas en oesophagus chirurgie.11 Een algemene registratie blijft belangrijk voor de algemene chirurgische afdelingen in kleinere ziekenhuizen maar ook voor de academische ziekenhuizen. Te meer omdat complicatieregistratie is gericht op uitkomsten van zorg. Het maakt het mogelijk om trends in complicaties te bekijken, zoals een toename in het percentage postoperatieve wondinfecties. Deze trends moeten geëvalueerd en geanalyseerd worden op het niveau van algemene chirurgie omdat de verbeterprocessen en maatregelen boven het niveau van subspecialisme kunnen uitstijgen.


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Een toekomstige ontwikkeling zou kunnen zijn dat de complicatieregistratie verschuift van een registratie per specialisme naar een registratie per patiënt ongeacht het specialisme. Het werken in multidisciplinaire teams is steeds belangrijker geworden in de zorg; GE-chirurgen vormen teams met endoscopisten, vaatchirurgen met interventieradiologen. Enkele jaren geleden verscheen een rapport getiteld “To err is human” waarin men ook pleitte voor het werken in teams wat ons mogelijk in staat stelt een groot aantal te voorkomen complicaties te ondervangen.10 Dit pleit voor een meer consistente registratie van alle complicaties. Wat betekent dat alle complicaties die bij een patiënt ontstaan gedurende de opname en tot 30 dagen na ontslag moet worden geregistreerd. Alle complicaties, ontstaan op alle mogelijke locaties moeten worden geregistreerd, ongeacht bij welk specialisme de complicatie is ontstaan. Deze data kunnen vervolgens gebruikt worden voor verbeteracties. De registratie van complicaties zal zo evolueren van een noodzakelijk kwaad tot een essentieel instrument bij het verbeteren van de kwaliteit van chirurgische zorg. Daarnaast kan de patiënt-specifieke informatie gebruikt worden voor gedeelde besluitvorming in een ziekenhuis, een afdeling of bij een individuele patiënt. De verantwoordelijkheid van de keuze voor behandeling en de hierbij mogelijke risico’s op complicaties moeten in de handen liggen van de chirurg en de patiënt samen. De veelbelovende mogelijkheden in de toekomst zullen leiden tot een gezamenlijke verantwoordelijkheid van het behandelteam en de patiënt zelf en zo de kwaliteit van zorg te optimaliseren.

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REFERENTIES

1. de Vries EN, Ramrattan MA, Smorenburg SM, et al. The incidence and nature of in-hospital adverse events: a systematic review. Qual Saf Health Care. 2008;17:216-23.

2. de Vries EN, Prins HA, Crolla RMPH et al. Effect of comprehensive surgical safety system on patient outcomes. N Eng J Med. 2010; 63:1928-37.

3. Veen EJ, Janssen-Heijnen MLG, Bosma E, et al. The accuracy of Complications Documented in a Prospective Complication Registry. J Surg Res 2012;173:54.

4. Stiggelbout AM, Van der Weijden T, De Wit MP, et al.Shared decision making: really putting patients at the centre of healthcare. BMJ. 2012 Jan 27;344:e256.

5. Rotter T, Kinsman L, James E, et al. The effects of clinical pathways on professional practice, patient outcomes, length of stay, and hospital costs: Cochrane systematic review and meta-analysis. Eval Health Prof. 2012;35(1):3-27.

6. Hoonhout LHF, de Bruijne MC, Wagner C, et al. Direct medical costs of adverse events in Dutch hospitals. BMC Health Services Research 2009, 9:27.

7. Burden S, Todd C, Hill J et al. Pre-operative nutrition support in patients undergoing gastointestinal surgery. Cochrane Database Syst Rev. 2012;11:CD008879.

8. Oppendal K, Møller AM, Pedersen B, et al. Preoperative alcohol cessation prior to elective surgery Cochrane Database Syst Rev. 2012;7:CD008343.

9. Wiggers LC, Smets EM, Oort FJ, Peters RJ, Storm-Versloot MN, Vermeulen H, de Haes HC, Legemate DA. The effect of a minimal intervention strategy in addition to nicotine replacement therapy to support smoking cessation in cardiovascular outpatients: a randomized clinical trial. Eur J Cardiovasc Prev Rehabil. 2006 Dec;13(6):931-7.

10. http://www.clinicalaudit.nl


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‘Thread tensioner’ is missing two wings. With thread and pins the veins of his wings were constructed. Anne ten Donkelaar.

Appendices

PhD Portfolio

Publications

Acknowledgements (Dankwoord)

Curriculum vitae

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PHD PORTFOLIO

Name PhD student: Annelies VisserPhD period: 2010-2015 Name PhD supervisor: Prof. dr. J. Carel Goslings

Year Workload(ECTS)

General courses BROK (‘Basiscursus Regelgeving en Organisatie voor Klinisch onderzoekers’)Scientific Writing in English for PublicationPractical BiostaticsClinical Epidemiology

2010

201120122012

0.9

1.51.10.6

Specific courses Evidence based surgeryEnglish writing Regina Coelie

20102010

1.02.5

Oral PresentationsQuality of care and analysis of surgical complications Researchmeeting KPI, AMC Researchmeeting Surgery, AMC Trigger tool versus standardized clinical registry to identify surgical complicationsResearchmeeting Surgery, AMC

20102010

2013

Which clinical scenarios do surgeons record as complications? A benchmarking study of seven hospitals Researchmeeting Surgery, AMCNVvH Chirurgendagen, Veldhoven

20132014 0.5

Tutoring, MentoringAKS van WijngaardenL NautaM van IperenJ Guijt B Geboers AE SlamanCM van Leijen

2010201120112012201320132013

1.01.01.01.01.01.01.0

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PUBLICATIONS

International publications

Saltzherr TP, Visser A, Ponsen KJ, Luitse JS, MD, Goslings JC. Complications in Multitrauma Patients in a Dutch Level 1 Trauma Center. The Journal of Trauma Injury, Infection, and Critical Care. 2010;69(5):1143-6.

van Westreenen HL, Visser A. Tanis PJ, Bemelman WA. Morbidity related to defunctioning ileostomy closure after ileal pouch-anal anastomosis and low colonic anastomosis. International Journal of Colorectal Disease 2012;27:49–54.

Visser A, Ubbink DT, van Wijngaarden AKS, Gouma DJ, Goslings JC. Quality of Care and Analyses of Surgical Complications. Digestive Surgery 2012;29:391–9.

Ubbink DT, Visser A, Gouma DJ, Goslings JC. Registration of surgical adverse outcomes: a reliability study in a university hospital. BMJ open. 2012;2:e000891.

Visser A, Ubbink DT, Gouma DJ, Goslings JC. Questionnaire versus telephone follow-up to detect postdischarge complications in surgical patients: randomized clinical trial. World Journal of Surgery 2012;36(11):2576-83.

Visser A, Ubbink DT, Gouma DJ, Goslings JC. Surgeons are overlooking post-discharge complications: a prospective cohort study. World Journal of Surgery 2014; 38(5):1019-25.

Visser A, Geboers B, Gouma DJ, Goslings JC, Ubbink DT. Predictors of Surgical Complications: A Systematic Review. Surgery 2015 Feb 27. pii: S0039-6060(15)00028-8.

National Publications

Visser A, Ubbink DT, Gouma DJ, Goslings JC. Complicatieregistratie moet eenduidig zijn. Medisch contact. 2014;36:1676-8.

Visser A, Ubbink DT, Gouma DJ, Goslings JC. Chirurgische complicaties in Nederlandse Ziekenhuizen. Nederlands tijdschrift voor evidence based practice. 2014;4:9-12.

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DANKWOORD

Graag wil ik alle mensen bedanken die hebben bijgedragen aan mijn promotieonderzoek. Een aantal personen wil ik in het bijzonder bedanken.

Mijn promotor Carel Goslings. Carel, wat begon 12 jaar geleden met een Cd-rom, gele briefjes, jij, ik en een zak M&M’s, is nu geëindigd met dit proefschrift. Jij hebt mij de kans gegeven om bij jou te kunnen promoveren. Ten eerste vanwege het onderwerp maar zeker ook omdat jij mijn promotor zou worden, heb ik hier eigenlijk geen moment over na hoeven denken. Je hebt me aan de ene kant heel veel vrijheid gegeven maar tegelijkertijd heb je mij ook bij vele zaken betrokken en daardoor veel verantwoordelijkheid en vertrouwen gegeven. Ik heb veel geleerd van je kritisch blik, grondige en pragmatische aanpak en je politieke intelligentie. Dank dat je het met mij hebt uitgehouden en ik ga het zeker missen.

Mijn promotor Prof. Gouma, wat een eer om van zoveel kennis gebruik te mogen maken. U heeft mij door het laatste stuk van het traject heengetrokken. Uw passie voor de wetenschap is aanstekelijk en na een goed gesprek leek alles ineens heel helder en eenvoudig. Dank daarvoor.

Mijn copromotor Dirk Ubbink. Dirk, jij bent in de wieg gelegd voor de wetenschap. Het schrijven van een kwalitatief hoogwaardig wetenschappelijk artikel en het duidelijk vertellen van ingewikkelde zaken aan niet-ingewijden gaat je als vanzelf gemakkelijk af. Een ware woordkunstenaar, nog nooit zoveel taalgrappen en verbasterde spreekwoorden voorbij zien komen. Ik wacht met smart op de eerste uitgave van de verzamelde werken van Dirk Ubbink. Heel veel dank voor de vele snelle en detailleerde correcties, inhoudelijke ideeën voor de diverse onderzoeksopzetten en je statistiek lessen. Maar vooral bedankt voor je geduld want je weet: ‘Canes latrantes non mordent’!

De commissieleden, Prof. Dr. Legemate, Prof. Dr. de Visser, Prof. Dr. van Delden, Prof. Dr. Blankensteijn, Prof. Dr. Lange en Dr. Go, wil ik danken voor hun tijd en bereidheid om mijn artikelen te bestuderen en voor hun deelname in mijn promotiecommissie.

Mede-onderzoekers, in het bijzonder Annelijn, Katrien en Catherina. Het kunnen delen met gelijkgestemden is veel waard. Een uitlaatklep, een aanmoediging, de gouden tip, gedeelde vreugde of smart, lekker hoor. Op onze verdere samenwerking in toekomst!

Anne, Sarah, Leanne, Marlies, Bart en Corrie, wat was het leuk om met jullie samen te werken. En bedankt voor al het werk dat hebben jullie verzet. Nu eindelijk tijd voor die borrel.

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De hoofdstukken in dit proefschrift zouden kwalitatief niet van dit niveau zijn geweest zonder de inbreng van alle co-auteurs. Hierbij wil ik dan ook alle co-auteurs bedanken die een bijdrage hebben geleverd aan één van de hoofdstukken.

Directeuren Jan de Wolde en Marieke Brink-Zimmerman, die mij binnen mijn huidige functie de ruimte hebben gegeven om dit avontuur aan te gaan.

Het H1-gangetje, het gouden gangetje, waar de leukste mensen van het AMC wonen en hebben gewoond. Voormalige bewoners: Ties, die van het duo, relativeren en heel hard lachen en alles valt weer mee. Yvonne, betrokken en een taalvirtuoos, bedankt voor je creatieve hulp. Marian, voormalig roomie, dank voor de ondersteuning, het luisterend oor (kan ook via de mail) en het meedenken. Huidige bewoners: Menno, huidige roomie, een opgeruimd bureau en karakter en altijd in voor een ‘practical joke’. Buurman Jaap, dank voor al je hulp bij de lay-out. Jeanet, een koffie en ‘living in the moment’, wat een goed begin van de dag. En in het bijzonder, het voormalige dreamteam: Frances en Diana. Ik ben jullie heel erg dankbaar voor de onvoorwaardelijke steun, de gezelligheid en de fantastische samenwerking. Zonder jullie was dit proefschrift er waarschijnlijk niet gekomen.

Miranda en Anne Marie, mijn Paranimfen, mijn bloedmooie, lieve vriendinnen. Ik zeg het niet vaak maar ik ben zo blij met jullie. Altijd staan jullie voor me klaar, troostend en aanmoedigend, met een leuk verhaal, met een goed advies of een kritische vraag. Niet veroordelend, maar ruimdenkend. Ik ben trots dat jullie straks naast mij staan.

Het was een hele klus om mijn promotieonderzoek te combineren met mijn werk en met mijn gezin. Speciale dank gaat dan ook uit naar mijn familie.

De ‘Woongroep’: Ingrid, Tess, Viggo en Mirre. Wat hebben we toch een geluk met elkaar. Het dagelijks drukke leven is zoveel gezelliger en lichter geworden dankzij jullie. Ja, jullie zijn ook familie.

Jeroen, Esther, Melle en Wessel. Een hele leuke grote broer met een geweldige schoonzus. En twee vet stoere neven. Wat een mazzel. Dank voor jullie steun. @Melle, probeer mij nu nog maar eens in te halen.


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Lieve pap en mam. Bedankt voor jullie hulp in alle mogelijk denkbare vormen. Thuiskomen voelt nog altijd als een warm bad.

En natuurlijk Dries, Tip en Wisse. Jullie zijn bijzonder en fantastisch.

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CURRICULUM VITAE

Annelies Visser is geboren op 3 juli 1973 in Beverwijk. In 1991 behaalde ze haar Atheneum diploma aan het Augustinus College in Beverwijk. Nadat ze was uitgeloot voor de studie Geneeskunde, startte ze in 1991 met de studie Gezondheidswetenschappen aan de Universiteit Maastricht met het plan zich het volgende studiejaar zich weer in te schrijven voor Geneeskunde. Echter, Gezondheidswetenschappen bleek een boeiende studie te zijn en na een actief studentenleven studeerde ze in 1998 af met de afstudeerrichting Beleid en Beheer. Na haar studie is ze haar carrière begonnen met het organiseren van landelijke mobiele bloedinzamelingsavonden bij het Centraal Laboratorium van de Bloedtransfusiedienst. In 2000 werkte ze als adviseur bij het onderzoeks- en adviesbureau Orbis, een adviesbureau het gebied van sociale zekerheid. Vervolgens werd ze in 2002 verantwoordelijk voor het stimuleren en begeleiden van huisartsen tot samenwerkingsverbanden bij de District Huisartsenvereniging Utrecht. Sinds 2003 is ze werkzaam in het AMC waar ze is begonnen als hoofd Opnamebureau Chirurgische Specialismen. In deze functie heeft ze samen met Prof. Goslings de Landelijke Heelkunde Complicatieregistratie (LHCR) geïntroduceerd, geïmplementeerd en geoptimaliseerd op de afdeling Chirurgie. In 2006 maakte ze de overstap naar het bedrijfsbureau als projectmedewerker Implementatie OKPlus (Chipsoft) met als aandachtsgebieden ‘wachtlijstbeheer’ en ‘operatie planning’. Naast deze baan heeft ze haar rol als projectleider LHCR altijd behouden. Als adviseur bij de Commissie Complicatieregistratie van de NVvH was ze ook op landelijk niveau actief. Na het eerste onderzoek, naar complicaties in de periode na ontslag, werd haar de mogelijkheid geboden om een promotietraject te starten (aan de faculteit der Geneeskunde). Dit traject heeft geleid tot dit proefschrift. Op dit moment is ze, naast OKPlus, werkzaam als divisie coördinator in het kader van de implementatie van Epic, een ziekenhuisbreed EPD, in samenwerking met het VUmc.Naast een passie voor complicaties heeft zij ook een passie voor toneel. Ze speelt al 15 jaar voorstellingen met haar toneelgroep. Annelies woont samen met Dries in Amsterdam en ze hebben twee hele leuke kinderen: Tip en Wisse.

Proefschrift Visser

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