M
onitorin
g drug th
erapy in
hosp
italized patien
ts Esth
er Uijten
daal 2014
Monitoring drug therapy in hospitalized patients
Esther Uijtendaal
UITNODIGING
Voor het bijwonen van deopenbare verdedigingvan het proefschriftt
Monitoring drug therapy in hospitalized
patients
Dinsdag 2 december 2014om 16:15 uur
in het Academiegebouw,Universiteit Utrecht
Domplein 29 te Utrecht
Feestelijke borrel na afloop van de promotie in het
Academiegebouw
Esther UijtendaalOverboslaan 20
3722 BL [email protected]
Paranimfen:
Marcel [email protected]
06-52803009
Hanneke den [email protected]
06-52097419
M
onitorin
g drug th
erapy in
hosp
italized patien
ts Esth
er Uijten
daal 2014
Monitoring drug therapy in hospitalized patients
Esther Uijtendaal
UITNODIGING
Voor het bijwonen van deopenbare verdedigingvan het proefschriftt
Monitoring drug therapy in hospitalized
patients
Dinsdag 2 december 2014om 16:15 uur
in het Academiegebouw,Universiteit Utrecht
Domplein 29 te Utrecht
Feestelijke borrel na afloop van de promotie in het
Academiegebouw
Esther UijtendaalOverboslaan 20
3722 BL [email protected]
Paranimfen:
Marcel [email protected]
06-52803009
Hanneke den [email protected]
06-52097419
Monitoring drug therapy in hospitalized patients
Cover design by: Marieke Velthuis
Lay-out by: Gildeprint – The Netherlands
Printed by: Gildeprint – The Netherlands
The work in this thesis was performed at the department of Clinical Pharmacy in
close collaboration with the department of Clinical Chemistry and Haematology
(Division of Laboratory and Pharmacy) and the department of Intensive Care
(Division of Vital Functions) of the University Medical Center Utrecht, the
Netherlands.
E.V. Uijtendaal
Monitoring drug therapy in hospitalized patients
Thesis Utrecht University – with ref. – with summary in Dutch
ISBN/EAN: 978-90-393-6237-2
©2014 E.V.Uijtendaal
Monitoring drug therapy in hospitalized patients
Het monitoren van geneesmiddeltherapie bij in het ziekenhuis opgenomen patiënten
(met een samenvatting in het Nederlands)
PROEFSCHRIFT
ter verkrijging van de graad van doctor aan de Universiteit Utrecht
op gezag van de rector magnificus, prof.dr. G.J. van der Zwaan,
ingevolge het besluit van het college voor promoties
in het openbaar te verdedigen op
dinsdag 2 december 2014 des middags te 4.15 uur
door
Esther Véronique Uijtendaalgeboren op 1 april 1967
te ‘s-Hertogenbosch
Promotoren: Prof. dr. A. C. G. Egberts
Prof. dr. W.W. van Solinge
Copromotor: Dr. J.E.F. Zwart- van Rijkom
Voor mijn ouders
Table of Contents
Chapter 1 General introduction, aims, and outline of the thesis 9
Chapter 2 Drug-drug interactions in hospitalized patients
2.1 Frequency and nature of drug-drug interactions in a Dutch 21
university hospital
2.2 Analysis of potential drug-drug interactions in medical 37
intensive care unit patients
Chapter 3 Monitoring drug therapy in hospitalized patients
3.1 Frequency and determinants of laboratory measurements for 53
serum potassium, sodium and serum creatinine in hospitalized
patients
3.2 Frequency of laboratory measurement and hyperkalaemia 73
in hospitalised patients using serum potassium concentration
increasing drugs
3.3 Serum potassium influencing interacting drugs: 91
Risk-modifying strategies also needed at discontinuation
3.4 Influence of a strict glucose protocol on serum potassium 103
levels in intensive care patients
Chapter 4 General discussion and summary 123
Samenvatting 151
List of co-authors 159
List of publications 161
Dankwoord 163
Curriculum vitae 165
1General introduction, aims and
outline of the thesis
Chapter 1 | General introduction, aims, and outline of the thesis
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Background
Medical errors have increasingly been acknowledged as a major public health
problem, since these errors received worldwide attention due to the publication
of the US Institute of Medicines report “To Err is Human: Building a Safer Health
System”1. The report describes that adverse events (AEs), defined as injuries due
to medical management (rather than the underlying disease), occur in 2.9 - 3.7
percent of all hospitalized patients in the US and that AEs lead or significantly
contribute to death in 6.6-13.6% of these patients2,3,4. The leading cause of AEs was
the use of drugs, accounting for 19.4% of the AEs3. Based on these findings, Bates
et al. started research on the relation between the use of drugs, AEs and their
preventability. Bates defined adverse drug events (ADEs) as injuries resulting from
medical intervention related to a drug, encompassing adverse drug reactions (i.e.
noxious and unintended effects resulting from appropriate use of drugs) as well
as medication errors (i.e. adverse drug events resulting from inappropriate drug
use). He reported that ADEs and preventable ADEs occurred in 6.5% and 5.5% of the
hospitalized patients respectively and that 28% of all ADEs were preventable5. The
preventable ADEs (i.e. medication errors) were also associated with an additional
length of stay of 4.6 days. The post event costs attributable to a preventable ADE was
estimated at $46856.
Also ADEs occurring in ambulatory care contribute to the medical error public
health problem. The Dutch HARM (Hospital Admissions Related to Medication)
study showed for example that 5.6% of all unplanned admissions were medication
related and that 46.5% of these were judged preventable7. Of the patients admitted
due to a preventable medication related problem, 6.3% died and 9.3% experienced
a disability after discharge.
The available evidence indicating that an important part of health damage and
associated costs due to ADEs can be prevented, led to awareness among health
care professionals, as well as policy makers, that strategies have to be developed
to prevent these ADEs and associated medical consequences and costs. In order to
develop better strategies for the prevention of ADEs, both causes and the process
of pharmaceutical care (figure 1) were analyzed8,9. The most frequently identified
underlying causes of ADEs are lack of dissemination of drug knowledge and
inadequate and timely availability of relevant patient information such as laboratory
test results9. Within the process of pharmaceutical care, errors associated with
preventable ADEs most frequently occur during the prescription and monitoring
stages8. Monitoring errors were defined as inadequate monitoring (biomarker or
clinical) of drug therapies or a delayed response or failure to adequately respond to
signs and symptoms or laboratory evidence of drug toxicity.
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prescribingtranscription &verification
dispensing administration& use
monitoring evaluation
Figure 1: the stages of the pharmaceutical care process
As such, risk mitigation strategies to prevent ADEs should focus primarily on
the prescribing and monitoring stage and facilitate the dissemination of drug
knowledge and the availability of patient information.
Drug-drug interactionsWithin the stages of prescribing and monitoring, drug-drug interactions (DDIs)
are of special interest as ADEs resulting from DDIs are often predictable and
preventable10,11. ADEs resulting from DDIs may, for example, be prevented if DDIs
are recognized timely. Generation of alerts by a computerized order entry (CPOE)
system at stage of prescribing may be helpful in this. Prescribers indeed believe that
DDI alerting improves their prescribing, especially when these alerts are evidence-
based12. The Dutch G-standard is such an evidence based DDI guideline which is
implemented in most CPOE systems of Dutch hospitals13. The G-standard does
not only support the alerting of potential DDIs, but also provides comprehensive
background information on the mechanism and level of evidence of the interaction
and advises for mitigation of the patient risk related to the interaction for the
individual patient. This risk usually consists of an increased risk of side effects,
and less often a risk of a decreased efficacy. Data, however, on both occurrence
in hospitals and clinical consequences for patients are scare. Several studies have
investigated the frequency and nature of DDIs in community setting14,15,16, but
results from these studies cannot be simply extrapolated to the hospital setting
as patients, medications and monitoring practices may differ. Risk mitigation
strategies that are often advised are discontinuation/substitution of one of the
drugs, dose adjustment, adding a risk modifying drug or monitoring biomarkers17.
However, it was shown that physicians override the majority of the alerts18. It is not
known, whether prescribers did follow the advice of the risk modifying strategy
before overriding the alert. It is therefore unknown if patients are still at risk for
an ADE resulting from these DDIs. To determine the most effective risk mitigation
strategies for prevention of ADEs resulting from DDIs in hospitalized patients, it is
of interest to investigate which DDIs are responsible for most of the alerts.
Within the hospital setting, patients admitted to the Intensive Care Unit (ICU)
constitute a special vulnerable group. Compared to patients admitted to general
hospital departments, ICU patients are more severely ill and their capacity to
counteract and deal with (patho)physiological disturbances is often diminished19,20.
Moreover, the medications used are often classified as “high-risk”21, which means
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that drugs may have a small therapeutic index. Medications involved in DDIs on
the ICU differ therefore from medications involved in DDIs of the general hospital
setting. Consequently, ADE resulting from these DDIs may also differ. The high
technological environment of the ICU, however, enables caregivers to monitor
patients intensively and detect disturbances earlier. Moreover, due to the parenteral
access for medications, deviations can also be corrected faster. Despite, Cullen et al
found that ADEs were likely to be more severe in ICUs than in non-ICUs22, although
the duration of the injury was short. To what extent DDIs contributed to these ADEs
was not reported.
Monitoring drug therapy Monitoring drug therapy is a process of checking in time to see whether a drug
works, while protecting the patient from adverse drug effects23. Monitoring
may consist of clinical monitoring and/or biomarker monitoring. Biomarker
monitoring may support clinical monitoring when treatment goals cannot be
directly observed. This biomarker monitoring may consist of measurements such
as physical parameters like blood pressure and ECG or measurement of laboratory
parameters like drug concentrations or endogenous substances such as potassium.
To mitigate drug related problems, drug labels often advise to measure laboratory
markers such as international normalized ratio (INR), serum creatinine and
serum electrolytes23,24. Drug labels, however, often lack clarity on monitoring. Of
all drug labels that advise to monitor laboratory values, only 17% of the provided
information on ‘what to monitor’, ‘critical value’ and ‘how to respond’24,25. This
lack of clarity may hamper the adherence to monitoring advices. Raebel showed
that the proportion of drug dispensing’s without all recommended laboratory
monitoring varied from 29-46% in ambulatory care26. Comparable rates were found
in vulnerable patient groups, kidney function for example, was determined in only
66% of the elderly outpatients patients with a renally cleared cardiovascular drug 24.
Rates do not seem to be better in hospitalized patients. Drenth et al. showed that
serum creatinine levels were measured in only 59% of the hospitalized elderly
patients, although this rate was not specific for patients using renally cleared
drugs27.
Inadequate monitoring may have serious consequences for patients. For example,
inadequate monitoring of drug therapy was an important cause of drug related
hospitalizations7,28,29. The exchange of information on a limited set of seven
laboratory markers, e.g. drug serum levels, INR, serum creatinine, potassium,
sodium levels and pharmacogenetic markers, therefore became obliged by the
Dutch law in January 201230. Due to practical implementation problems this law
was amended again in July 2013, now only demanding to communicate laboratory
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values on kidney function when these deviate from normal limits31. Whether
health care providers adhere to this obligation, however, is not known.
Measurement of laboratory markers is not only frequently advised in drug labels to
evaluate drug therapy, but is also one of the most frequently advised risk mitigation
strategy of DDIs. It was shown that 33% of all clinical significant drug-drug
combinations advise to monitor laboratory makers, and that at least one laboratory
test was required in 37% of the patients with a DDI32. Tests on renal function and
serum potassium were most frequently advised as preventive measures in DDIs,
serum creatinine accounted for 42% and serum potassium for 20% of the tests.
Of these markers, serum potassium has special attention in medication management.
Not only is serum potassium one of the most frequently involved laboratory markers
in DDIs, but the clinical consequences of deviating serum potassium levels can also
be severe. For example, serious hyperkalemia may occur at any time in patients
treated for heart failure with spironolactone combined with an angiotensin
converting enzyme inhibitor and evolve rapidly to cause lethal arrhythmia. It was
shown that patients admitted to the hospital with hyperkalemia were twenty times
more likely to use an ACE inhibitor combined with spironolactone than when the
ACE inhibitor was not combined with spironolactone10. Moreover, it was shown that
publication of the Randomized Aldactone Evaluation Study (RALES) that advised
to add spironolactone to standard treatment of patients with severe heart failure,
led to an increased rate of prescription of spironolactone but also to an increased
rate of hyperkalemia, and associated mortality33. Whether serum potassium levels
of these patients were adequately monitored is not known, but literature suggests
that adherence to monitoring guidelines for serum potassium increasing drugs are
generally poorly met34,35. Monitoring of serum potassium levels therefore deserves
extra attention.
Thesis objectivesThe objective of this thesis is to describe the frequency and potential clinical
relevance of drug therapy monitoring with laboratory markers in hospitalized
patients with a special focus on DDIs, potassium and ICU patients.
Thesis outlineChapter 2 describes the frequency and nature of DDIs in both hospitalized patients
and patients admitted to a general intensive care unit (ICU). Hospitalized patients
are considered as a more vulnerable patient group as compared to outpatients, they
may be more severely ill and their capacity to counteract with disturbances may be
diminished. The frequency and nature of DDIs occurring in hospitalized patients
may differ from DDIs occurring in outpatients as different medications are used
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and drug therapy may change more frequently (chapter 2.1). Special attention is
drawn to ICU patients (chapter 2.2). Due to the technological environment, ICU
patients are intensively monitored and management advices may not have the
same relevance as for patients admitted to general hospital departments. Insight in
the frequency of DDIs and the most frequently advised management strategies, may
lead to customization of DDI signaling for these specific vulnerable patient groups.
Chapter 3 focuses on laboratory monitoring in hospitalized patients. In chapter
3.1, the frequency of laboratory monitoring of serum potassium, serum sodium
and serum creatinine values is described for different kinds of hospitalized patient
groups.
DDIs are recognized as a risk factor to develop an ADE. The DDI between two or
more serum potassium increasing drugs, for example, is recognized as a risk factor
to develop life threatening hyperkalemia. The DDIs between serum potassium
increasing drugs are therefore included in the G-standard and computerized
physician order entry systems (CPOEs) using this standard will signal and advice to
monitor laboratory values when this DDI occurs. It is not known whether physicians
adhere to the monitoring advice. Chapter 3.2 therefore examines the frequency of
serum potassium measurements and hyperkalemia in hospitalized patients using
one or more potassium increasing drugs and the determinants thereof.
Serum potassium levels may not only alter due to the start of potassium influencing
medication, but also due to discontinuation. CPOEs, however, generally do not signal
when a drug is stopped. To investigate whether monitoring is also relevant after
discontinuation, serum potassium levels before discontinuation are compared to
serum potassium levels after discontinuation in patients who stopped a potassium
increasing drug and patients who stopped a potassium lowering drug (chapter 3.3).
Many ICU’s have implemented a tight glucose control (TGC) protocol to keep
serum glucose levels between target levels. However, not only elevated glucose
levels are associated with an increased mortality. Hypoglycemia is also associated
with mortality and TGC may increase the risk on hypoglycemia. Moreover, TGC
may also influence serum potassium levels and both hyper and hypokalemia
are also associated with mortality. Outcomes, however, seem to depend on
accurate monitoring of glucose levels. Chapter 3.4 describes the influence of the
implementation of a TGC protocol on both serum glucose and serum potassium
levels.
In the final chapter, chapter 4, the results of the different studies are discussed
and put into a broader perspective of current need of monitoring drug therapy.
Furthermore, directions for future research are given.
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population. J Manag Care Pharm 2003;9:513–522
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case of drug–drug interactions. Drug Saf 2006;29:723–732
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interactions in elderly people. Lancet 2007;370:185–191
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614
2Drug-drug interactions in
hospitalized patients
2.1Frequency and nature of drug-drug
interactions in a Dutch university
hospital
Jeannette E.F. Zwart-van Rijkom
Esther V. Uijtendaal
Maarten J. ten Berg
Wouter W. van Solinge
Antoine C.G. Egberts
Br J Clin Pharmacol 2009:68:187–193
Chapter 2.1 | Frequency and nature of drug-drug interactions in a Dutch university hospital
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Abstract
Aim
Drug-drug interactions (DDIs) may lead to often preventable adverse drug events
and health damage. Especially within hospitals this might be an important
factor, as patients are severely ill and multiple medications may be prescribed
simultaneously. The objective of this study was to measure the frequency and
nature of DDI alerts in a Dutch university hospital.
Methods
All patients hospitalized in the University Medical Center Utrecht in 2006 who
were prescribed at least one medication were included. The frequency of DDIs
was calculated as: i) the percentage of patients experiencing at least one DDI, and
ii) the percentage of prescriptions generating a DDI alert. Based on the national
professional guideline, DDIs were classified into categories of potential clinical
outcome, management advice, clinical relevance (A-F) and available evidence (0-4).
Results
Of the 21,277 admissions included, 5,909 (27.8%) encountered at least one DDI.
Overall, the prescribing physician received a DDI alert in 9.6 % of all prescriptions.
The most frequently occurring potential clinical consequence of the DDIs was an
increased risk of side-effects such as increased bleeding risk (22.0%), hypotension
(14.9%), nephrotoxicity (12.6%) and electrolyte disturbances (10.5%). Almost half
(48.6%) of the DDIs could be managed by monitoring laboratory values.
Conclusions
Computerized DDI alerts may be a useful tool to prevent adverse drug events within
hospitals, but they may also result in ‘alert fatigue’. The specificity of alerts could
significantly improve by the use of more sophisticated clinical decision support
systems taking into account, for example, laboratory values.
23
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Introduction
Adverse drug events are increasingly acknowledged as an area of major concern
in medical care. The HARM-study recently reported that 2.4% of all hospital
admissions and 5.6% of all emergency admissions in the Netherlands were related
to adverse drug events, of which almost half were considered preventable1. Drug-
drug interactions (DDI) constitute one of the potential mechanisms leading to
often preventable adverse drug events and health damage2,3. Several studies have
investigated the frequency and nature of DDIs in community settings4-7.
Within hospitals, the problem of DDIs may deserve extra attention, as more
medications may be prescribed simultaneously, more complex schemes and
compounds may be used, and the number of (dose) changes may be larger.
Moreover, compared with outpatients, hospitalized patients are more severely ill,
and the capacity to counter-act and deal with disturbances is often diminished.
DDIs may therefore occur more frequently within hospitals than in an outpatient
setting and their consequences may be more severe. Krähenbühl et al. estimated
that 17% of all adverse drug events in hospitalized patients are caused by DDIs
and that approximately 1% of the patients will experience an adverse drug event
during hospitalization due to a DDI8. At discharge about 60% of patients were
found to have at least one potentially interacting drug combination9,10. Two studies
performed in patients visiting an emergency room, found that when medications
were added, a potential adverse DDI was introduced in 5-10% of cases11,12. In two
internal medicine wards in Finland potentially serious interactions were detected
in 1.4% of all prescriptions, as such possibly affecting 6.8% of patients13. Overall,
data on the occurrence and consequences of DDI alerts within hospitals is scarce.
Therefore, the objective of this study was to measure the frequency and nature of
DDI alerts in a large university hospital in the Netherlands and to make suggestions
for improving the management of DDIs.
Methods
SettingThe University Medical Center Utrecht (UMCU) is a 1042-bed academic medical
centre located in the centre of the Netherlands. It consists of an adults’ hospital
and a children’s hospital. In 2006 28,888 clinical hospitalizations took place.
All medications for hospitalized patients are prescribed using a computerized
physician order entry (CPOE) system. For research purposes all prescriptions are
routinely exported in the Utrecht Patient Oriented Database (UPOD). UPOD is an
infrastructure of relational databases comprising data on patient demographics,
Chapter 2.1 | Frequency and nature of drug-drug interactions in a Dutch university hospital
24
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hospital discharge diagnoses, medical procedures, medication orders and
laboratory tests for all patients treated at the UMCU since 2004, and has been
described in detail elsewhere14.
Study populationAll patients hospitalized in the UMCU in 2006 for >24 hours and who were
prescribed at least one medication were included. If a patient was admitted during
the study period to the hospital more than once, the individual hospitalizations
contributed to the study multiple times. Prescriptions from the intensive care (IC)
units were not included in this study, because in the IC units a different CPOE
system is used and the prescription data are not (yet) stored in the UPOD database.
DDIsIn the Netherlands, a working group of the Scientific Institute of Dutch Pharmacists
developed and maintains an evidence base and professional guideline for the
management of DDIs, described in detail elsewhere15. In this professional guideline,
the G-standard, DDIs are classified on a six-point potential clinical relevance scale
ranging from not very serious to potentially lethal (categories A-F), and on a five-point
evidence scale ranging from not proven to very well proven (categories 0-4). This
classification in evidence-relevance categories is described in brief in Appendix 1.
Based on these categories, 331 DDIs have been classified as ‘relevant’, meaning
that they should generate a direct pop-up DDI alert at the moment of prescribing
and that assessment of these combinations of drugs is considered necessary.
Since the generated DDI alerts had not been logged during the study period, they
were reconstructed by combining the G-standard from December 2006 with the
prescription data in UPOD.
In addition, the DDIs were classified in ‘potential clinical outcome’ categories and
‘management advice’ categories. The Dutch professional guideline, the G-standard,
provides textual information about background, mechanism and advice regarding
each DDI. This professional guideline text was converted into ‘potential clinical
outcome’ categories and ‘management advice’ categories separately by two hospital
pharmacist (J.E.F.Z-v.R. and E.V.U). Differences in classification were subsequently
discussed and settled. One DDI could have multiple outcomes or management
advices.
Data analysisThe frequency of DDIs was calculated as: i) the percentage of patients treated with
medication that experienced at least one potential DDI, and ii) the percentage
of all medication orders generating a DDI alert. The frequencies were calculated
25
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separately for each clinical specialism in the children’s and the adults’ hospital.
The nature of the DDIs was described by listing the 10 most frequent alerts for
the children’s and the adults’ hospital. The percentage of alerts in each clinical
outcome and management advice category was calculated.
Finally, the percentages of alerts in each of the G-standard evidence-relevance
categories in our hospital were compared with the percentages found in Dutch
community pharmacies by Buurma et al.4.
Results
In total, 21,277 patients who had been prescribed at least one medication were
admitted to the hospital in 2006. Of these patients, 5909 (27.8%) encountered at
least one DDI; on average these 5909 patients experienced 20,058/5909=3.4 DDIs.
The percentage of patients experiencing at least one DDI during the hospital
admission varied from 3.4% in the paediatric surgery department to 62.0 % in the
adults’ nephrology department (see Table 1).
In 2006, a total of 208,187 prescriptions was registered for clinical patients of which
20,058 (9.6%) resulted in a DDI alert. The percentage of prescriptions generating
an alert varied from 1.6% in the paediatric surgery department to 17.2 % in the
adults’ cardiology department. Although the professional guideline identifies 331
different relevant DDIs, overall only 10 DDIs made up >50% of all DDI alerts, and
50 DDIs made up > 90% of all DDI alerts. The 10 most frequently occurring DDIs in
the adults’ hospital and the children’s hospital respectively are shown in Table 2.
In Table 3 it is shown that an increased risk of side-effects was the most frequently
occurring potential clinical consequence of the DDIs in our patient population
(81.2% of alerts). This risk encompassed an increased bleeding risk (22.0%),
hypotension (14.9%), nephrotoxicity (12.6%) and electrolyte disturbances (10.5%).
The DDI could also lead to a decreased effectiveness of the medication in 25.2%
of cases. This may have severe consequences, for example in the case of antibiotics
(ongoing infection) or immunosupressives (rejection of the transplant). With
respect to the management almost half (48.6%) of all DDI alerts gave the advice to
monitor laboratory values. In 36.5% of the occurring DDIs it was advised to avoid
the combination, in 35.7% to apply a risk-modifying strategy (such as the addition
of a proton pump inhibitor for gastric protection) and in 17.2% tot adjust the dose.
Chapter 2.1 | Frequency and nature of drug-drug interactions in a Dutch university hospital
26
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le 1
Fr
equ
ency
of
dru
g-d
rug
inte
ract
ion
ale
rts
aler
ts
Ad
mis
sio
ns
Pres
crip
tio
ns
nu
mb
er o
f ad
mis
sio
ns
wit
h
≥1 a
lert
s*
nu
mb
er o
f ad
mis
sio
ns*
%av
erag
e n
um
ber
of
aler
ts p
er a
dm
issi
on
w
ith
≥1
aler
ts
aler
tsp
resc
rip
tio
ns
%
TOTA
L5,
909
21,2
7727
.8%
3.4
20,0
5820
8,18
79.
6%
Ad
ult
’s h
osp
ital
5,38
415
,389
32.7
%3.
418
,373
167,
757
11.0
%
Car
dio
logy
an
d c
ard
iosu
rger
y1,
363
2,33
658
.3%
3.7
5,02
429
,175
17.2
%
Ger
iatr
ics
172
279
61.6
%4.
576
75,
043
15.2
%
Nep
hro
logy
304
490
62.0
%3.
41,
026
7,15
014
.3%
Gen
eral
in
tern
al m
edic
ine
566
1,39
540
.6%
3.5
1,99
514
,813
13.5
%
Lun
g d
isea
ses
403
798
50.5
%4.
01,
610
12,2
7513
.1%
On
colo
gy a
nd
hae
mat
olog
y33
495
934
.8%
4.5
1,49
812
,759
11.7
%
Der
mat
olog
y59
200
29.5
%5.
029
72,
920
10.2
%
Neu
rolo
gy a
nd
neu
rosu
rger
y69
82,
193
31.8
%2.
81,
958
22,4
158.
7%
Surg
ery
1,49
76,
668
22.5
%2.
74,
024
55,4
467.
3%
Psyc
hia
try
6454
911
.7%
2.7
174
5,76
13.
0%
Ch
ild
ren
’s h
osp
ital
525
5,85
310
.9%
3.2
1,68
540
,430
4.2%
Paed
iatr
ic h
aem
atol
ogy
& n
eph
rolo
gy18
083
421
.6%
4.4
797
9,95
48.
0%
Paed
iatr
ic n
euro
logy
9367
913
.7%
2.8
261
4,43
45.
9%
Paed
iatr
ic c
ard
iolo
gy42
292
14.4
%2.
189
2,88
63.
1%
Gyn
aeco
logy
an
d o
bste
tric
s17
223
897.
2%2.
033
812
,697
2.7%
Gen
eral
pae
dia
tric
s50
656
7.6%
2.1
107
4,69
62.
3%
Paed
iatr
ic s
urg
ery
3710
933.
4%2.
593
5,76
31.
6%
* N
umbe
rs d
o no
t add
up
as p
atie
nts
can
stay
at d
iffe
rent
dep
artm
ents
dur
ing
one
adm
issi
on.
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Table 2 Frequency and nature of the 10 most frequently encountered drug-drug interactions
% of alerts
Cumu-lative %
Evidence-relevance category
Adult’s hospital
1 ACE-Inhibitors + diuretics 12.1% 12.1% 3D
2 RAS-inhibitors + potassium (saving agents) 8.5% 20.7% 2F
3 Coumarins + antibiotics (excl. co-trimoxazole/metronidazole/ cefamandole)
7.5% 28.2% 3D
4 NSAIDs (excl. COXIB’s) + corticosteroids 6.0% 34.2% 3D
5 QT-prolongators + QT-prolongators(excl. clarithromycin/erythromycin/ voriconazole)
4.0% 38.2% 1E
6 Bisphosphonates + antacids/iron/calcium 3.6% 41.9% 0A
7 Betablockers selective + insulin 3.6% 45.4% 3B
8 Diuretics + NSAIDs 3.0% 48.5% 3D
9 Betablockers + NSAIDs 2.5% 51.0% 3C
10 Betablockers + oral antidiabetics 2.3% 53.3% 3B
Children’s hospital
1 Ciclosporin + nephrotoxic compounds 9.3% 9.3% 3C
2 Ciclosporin + CYP3A4-inhibitors 8.7% 18.0% 3D
3 Ciclosporin + cotrimoxazol/trimethoprim 8.0% 25.9% 3D
4 NSAIDs (excl. COXIB’s) + corticosteroids 7.8% 33.8% 3D
5 QT-prolongators + QT-prolongators(excl. clarithromycin/erythromycin/ voriconazole)
6.2% 39.9% 1E
6 QT-prolongators + clarithromycin/ erythromycin/voriconazole
4.7% 44.6% 3E
7 Midazolam/alprazolam + enzyme inductors 4.5% 49.1% 3C
8 Midazolam/alprazolam + CYP3A4-inhibitors 3.8% 52.9% 3B
9 Diuretics + NSAIDs 3.6% 56.5% 3D
10 Phenytoin + valproic acid 3.4% 59.9% 3A
ACE, angiotensin converting enzyme; RAS, renin-angiotensin system; NSAID, nonsteroidal anti-inflammatory drug
Chapter 2.1 | Frequency and nature of drug-drug interactions in a Dutch university hospital
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Table 3 Mechanism and advice of drug-drug interactions
Potential Clinical Outcome No. of alerts
% * No. of alerts
% *
Increased risk of side effects/toxicity 16,280 81.2%
Bleeding risk (incl. gastrointestinal ulcer risk) 4,410 22.0%
Hypotension 2,982 14.9%
Hephrotoxicity 2,535 12.6%
Electrolyte disturbances 2,115 10.5%
Cardiac arrhythmias (incl. QT-prolongation) 1,696 8.5%
Masking of hypoglycaemia 1,226 6.1%
Miscellaneous antiepileptics side-effects 415 2.1%
Risk of serotonin syndrome 232 1.2%
Other 669 3.3%
Risk of decreased effectiveness 5,054 25.2%
Anti-hypertensive drugs 1,665 8.3%
Antibiotics and antimycotics 722 3.6%
Biphosphonates 688 3.4%
Thyreomimetics 441 2.2%
Immunomodifiers 396 2.0%
Anti-epileptic drugs 229 1.1%
Other 913 4.6%
Advised Management Strategy No. of alerts
% * No. of alerts
% * No. of alerts
% *
Monitoring 16,268 81.1%
Clinical monitoring of toxicity/effectiveness 3,823 19.1%
Blood pressure monitoring 1,539 7.7%
ECG monitoring 1,151 5.7%
Monitoring of laboratory values 9,755 48.6%
Kidney function (serum creatinin) 2,463 12.3%
Blood clotting time (International Normalised Ratio)
2,408 12.0%
Potassium 2,059 10.3%
Drugs (Therapeutic Drug Monitoring) 1,651 8.2%
Glucose 802 4.0%
Differential blood count 163 0.8%
Liver function 147 0.7%
Sodium 62 0.3%
Avoid combination 7,330 36.5%
Risk modifying strategy 7,169 35.7%
Taking medication when sitting or laying down 2,982 14.9%
Separate moments of oral administration 2,305 11.5%
Add gastric protection (protonpump-inhibitor) 1,789 8.9%
Other 88 0.4%
Adjust dose / titrate dose slowly 3,429 17.1%
Other 889 4.4%
* Percentages do not add up to 100% as one alert could encompass multiple outcomes or management advice.
29
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The evidence for the vast majority (77 %) of the DDI alerts was of relatively high
quality (categories 3-4). The potential clinical relevance was high in 21% (categories
E-F), medium in 58% (categories C-D) and low or not classified in 21% of the DDIs
(categories A-B). This spreading over the different evidence-relevance categories is
presented in Figure 1 and is similar to that found in Dutch community pharmacies
by Buurma et al.4.
0%
10%
20%
30%
40%
0A
1A, 1B, 1
C, 1D 1E 1F 2D 2E 2F 3A 3B 3C 3D 3E 3F 4A 4C 4D
not cla
ssifie
d
Evidence-relevance category
Perc
enta
ge o
f DD
I ale
rts
university hospital (this study)
community pharmacies (Buurma et al.)
Figure 1 Percentage of drug-drug interaction (DDI) alerts by evidence-relevance category in our study compared with the percentage in Dutch community pharmacies in the study of Buurma et al.4
Discussion
We found that of all patients admitted to the hospital and being prescribed
medication, exclusive of IC patients, 28% experienced at least one potential DDI.
On average this group of patients experienced 3.4 DDIs. Overall, the prescribing
physician received a DDI alert in 9.6 % of all prescriptions; this percentage largely
varied between clinical specialisms. The most frequently occurring potential
clinical consequence of the DDIs was an increased risk of side-effects such as
increased bleeding risk (22.0%), hypotension (14.9%), nephrotoxicity (12.6%) and
electrolyte disturbances (10.5%). Almost half (48.6%) of the DDIs could be managed
by monitoring laboratory values.
All admissions in 2006, including re-admissions within the same year, were
included in our study as separate cases. Compared with first admissions, the risk
for DDIs may be increased during subsequent admissions, as these patients may be
more severely ill. To check whether or not this was the case, we also performed the
analysis including for each patient only the first admission in 2006. The outcomes
Chapter 2.1 | Frequency and nature of drug-drug interactions in a Dutch university hospital
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of both analyses were similar (29% of first admissions experiencing at least one
potential DDI; average number of DDIs 3.2). We consider it therefore justified to
combine both first and subsequent admissions in the analysis.
The 10 most frequently encountered DDIs in the adults’ hospital did not consist
of specific ‘high-tech’ hospital medications, but of medications that are used and
initiated on a large scale in the community setting as well. This suggests that
home medication caused a substantial part of the DDIs. In contrast to the adults’
hospital, the DDIs in the children’s hospital do seem to be caused by specific
‘hospital medication’, such as, for example, ciclosporin and midazolam. It can be
expected that children, in general, will be on less (or no) home medication on (first)
admittance to the hospital than adults. Another explanation for our findings in the
adults’ hospital may be found in the fact that we used the professional guideline,
the G-standard. The G-standard is in fact the national standard at this moment
and it is used in all hospitals in the Netherlands. However, it has primarily been
developed for its use within community pharmacies. DDIs concerning specific
hospital medications, e.g. anaesthetics and cytostatic agents, were largely missing
in the G-standard in 2006. It may be worthwhile considering if our results would
be different with the inclusion of the intensive care units and operating rooms and
with the use of a specific clinical DDI reference database. However, to our knowledge
such a specific clinical DDI database does not yet exist. Dawson and Karalliedde16
published an overview of DDIs that are important to the clinical anesthetist. This
publication could be a good starting point.
Another kind of interactions that is not included in the professional guideline
used but is of high importance in the hospital setting is the chemical or physical
incompatibility of two intravenous medications when mixed in the same infusion
bag or syringe or when administered simultaneously through the same catheter.
These types of interactions were not included in our study. Ideally, to improve
patient safety, a CPOE system should also signal this kind of DDI and make proposals
for its management.
A limitation may be that the number of separate DDIs may be overestimated, as the
same alert may be generated several times, each time the prescription is altered (re-
start, dose change). On the other hand, the number of ‘DDI risk moments’ may be
underestimated, as a DDI alert was generated only when a DDI interaction started.
A warning may also be in place when the DDI stops. For example, in the case of an
enzyme induction DDI, the plasma concentration of a drug may decrease and a
dose increase may be required. As a consequence, when the interacting medication
is stopped, the dose may need to be decreased again. In common practice of our
hospital, this alert is not generated, and therefore, and to avoid double-counting,
was not included in our study.
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Patient safety may be improved by decreasing the frequency of preventable adverse
drug events Computerized alerts may be a useful tool to signal DDIs, but they may
also result in ‘alert fatigue’ and the overriding of important signals, especially when
the system produces an overload of signals that are of minor clinical relevance17,18.
Although 79% of the alerts were of high or medium potential clinical relevance,
the actual relevance of some signals may still be low. Currently, the computerized
DDI surveillance systems that are routinely used in the Netherlands only take into
account the concomitant use of two drugs. Until now they have been unable to check
for additional relevant data such as laboratory values, times of administration,
absence of gastric protection, age, etc, which could increase the specificity of the
signal. For example, the concomitant use of a renin-angiotensin system inhibitor
and potassium always results in an alert warning for hyperkalaemia, even when
the serum potassium level is low. In this study we did not gather the information
to assess the actual relevance of the generated DDI alerts; this may be the topic of
our further research.
From Table 3, it can be expected that the specificity of alerts could significantly
improve by the use of more sophisticated clinical decision support systems
(CDSS)19,20, taking into account, for example, laboratory values (48.6% of alerts),
times of administration (11.5%) and the absence of gastric protection 8.9%). This
may be important knowledge in finding strategies to combat ‘alert-fatigue’.
Another important finding in this respect is that a relatively small number of
combinations (10 different DDIs) is responsible for a large number of alerts (>50%).
As these are very well-known DDIs, it may be worthwhile to discuss with the medical
board if and when these alerts have indeed to be shown. Turning-off some of these
top 10 DDI alerts may substantially decrease the ‘alert overload’ and in this way
increase the attention to dangerous and less known DDIs. In a series of qualitative
interviews, Van der Sijs et al.23 found that indeed the majority of respondents wanted
to turn off DDI alerts to reduce alert overload. Since the top 10 of alerts varies
among clinical specialism and since also the knowledge and monitoring practices
vary between clinical specialisms within the hospital, it may be wise to suppress
DDI alerts differentially between prescribers. This also requires more sophisticated
CDSS than we have available at the moment.
In conclusion, 28% of all patients admitted to our hospital are exposed to at least
one potential DDI. An increased risk of side-effects (e.g. increased bleeding risk,
hypotension, nephrotoxicity, electrolyte disturbances) was the most prevalent
potential clinical consequence. Almost half of the DDIs could be managed by
monitoring laboratory values. Prescribing physicians receive an automated DDI
alert in nearly 10% of all prescriptions. Further research is needed to investigate the
clinical relevance of these DDIs and to develop methods to increase the specificity
of automated CDSS alerts.
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Acknowledgements
The authors are grateful to Hanneke den Breeijen for the data-analysis and to their colleagues
at the Utrecht Institute for Pharmaceutical Sciences and the UMC Utrecht for their support in
establishing and maintaining UPOD.
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Appendix 1 Classification of drug-drug interactions according to the professional guideline by WINAp15
Category Description
Potential clinical relevance
A No inconvenience, insignificant effect
B Short-lived inconvenience (<24-48 hours) without residual symptoms
C Long-lived inconvenience (48-168 hours) without residual symptoms
D Long-lived inconvenience (>168 hours) with residual symptoms or handicap
E Potential failure of life-saving therapy, increased risk of pregnancy (without risks concerning mother and/or fetus), cardiac arrhythmia, rhabdomyolysis, malignant hypertension, pseudopheochromocytome, multi-organ failure
F Death, torsade de pointes, ventricular arrhythmia, myocardial infarction, serotonin syndrome, hyperpyrexia (42°C)
Quality of evidence
4 Controlled, published study with clinically relevant end-points
3 Controlled, published study with relevant surrogate end-points
2 Well-documented, published case reports; analysis of case series
1 In-complete, published case reports
0 Animal studies, in vitro studies, data on file
- No evidence
Not classified
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patients hospitalized for drug toxicity. JAMA 2003;289:1652-1658
3. Bagheri H, Michel F, Lapeyre-Mestre M, Lagier E, Cambus JP, Valdiguie P, Montastruc JL.. Detection and
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4. Buurma H, De Smet PA, Egberts AC. Clinical risk management in Dutch community pharmacies: the
case of drug-drug interactions. Drug Saf 2006;29:723-732.
5. Merlo J, Liedholm H, Lindblad U, Bjorck-Linne A, Falt J, Lindberg G, Melander A. Prescriptions with
potential drug interactions dispensed at Swedish pharmacies in January 1999: cross sectional study.
BMJ 2001;323:427-428
6. Buurma H, Schalekamp T, Egberts AC, De Smet PA. Compliance with national guidelines for the
management of drug-drug interactions in Dutch community pharmacies. Ann Pharmacother
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7. Peng CC, Glassman PA, Marks IR, Fowler C, Castiglione B, Good CB. Retrospective drug utilization
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8. Krahenbuhl-Melcher A, Schlienger R, Lampert M, Haschke M, Drewe J, Krahenbuhl S. Drug-related
problems in hospitals: a review of the recent literature. Drug Saf 2007;30:379-407
9. Egger SS, Drewe J, Schlienger RG. Potential drug-drug interactions in the medication of medical
patients at hospital discharge. Eur J Clin Pharmacol 2003;58:773-778
10. Kohler GI, Bode-Boger SM, Busse R, Hoopmann M, Welte T, Boger RH. Drug-drug interactions
in medical patients: effects of in-hospital treatment and relation to multiple drug use. Int J Clin
Pharmacol Ther 2000;38:504-513
11. Heininger-Rothbucher D, Bischinger S, Ulmer H, Pechlaner C, Speer G, Wiedermann CJ. Incidence and
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- a tool for detecting drug interactions in hospital. Eur J Clin Pharmacol 1997;53:13-17
14. ten Berg MJ, Huisman A, van den Bemt PMLA, Schobben AFAM, Egberts ACG, van Sollinge WW.
Linking laboratory and medication data: new opportunities for pharmacoepidemiological research.
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JRBJ. Clinical relevance of drug-drug interactions: a structured assessment procedure. Drug Saf
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Susong C. A computer alert system to prevent injury from adverse drug events: development and
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22. Judge J, Field TS, DeFlorio M, Laprino J, Auger J, Rochon P, Bates,DW, Gurwitz JH. Prescribers’
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2.2Analysis of potential drug-drug
interactions in medical intensive
care unit patients
Esther V. Uijtendaal
Lieke L.M. van Harssel
Gerard W.K. Hugenholtz
Emile M. Kuck
Jeannette E.F. Zwart- van Rijkom
Olaf L. Cremer
Toine C.G. Egberts
Pharmacotherapy 2014;34:213-219
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Abstract
Objective
To describe the frequency and type of potential drug–drug interactions (pDDIs) in
a general intensive care unit (ICU) and to make recommendations to improve the
management of these pDDIs.
Design
Retrospective observational study.
Setting
General ICU of a tertiary care hospital.
Subjects
All patients admitted for more than 24 hours between May 2009 and December
2010, who were prescribed at least one medication.
Measurement and Main Results
Based on the G-Standaard, the Dutch national drug database, pDDIs were identified
and classified into categories of potential clinical outcome and management advice.
In total, 35,784 medication episodes were identified, resulting in 2,887 pDDIs
(8.1%). These 2,887 pDDIs occurred in 1,659 patients for a mean frequency of 1.7
(95% confidence interval [CI], 1.6-1.9) pDDIs per patient. Overall, 54% of the patients
experienced at least one pDDI with pDDIs present during 27% of all ICU admission
days. All pDDIs could be reconstructed using 81 of the 358 (23%) relevant unique
pDDI-pairs described in the G-Standaard. The most frequently occurring potential
clinical consequence was an increased risk of side effects or toxicity (91% of the
pDDIs), such as electrolyte disturbances and masking of hypoglycemia. The most
important advised management strategy was monitoring (81%), consisting of
monitoring of laboratory values (52%), clinical monitoring of toxicity or effectiveness
(48%), or monitoring of physical parameters such as electrocardiogram and blood
pressure (11%).
Conclusion
Potential drug-drug interactions occur in 54% of all ICU patients, which is two times
more than the rate seen in patients on general wards. A limited set of 20 pDDI-pairs
is responsible for more than 90% of all pDDIs. Therefore, it is worthwhile to develop
guidelines for the management of these specific pDDIs. As the vast majority of the
interactions can be managed by monitoring, advanced clinical decision support
systems linking laboratory data to prescription data may be an effective risk
management strategy.
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Introduction
Given their medical and societal impact1, 2, adverse drug events (ADEs) are
increasingly acknowledged as an area of major concern in medical care. Potential
drug–drug interactions (pDDIs) constitute one of the often preventable causes of
ADEs and patient harm. One review estimates that 17% of all preventable adverse
drug events in hospitalized patients are caused by DDIs and that approximately 1%
of patients will experience an adverse drug event during hospitalization due to a
DDI3. Other research suggests that among patients in intensive care unit (ICUs), 10-
16% of all preventable ADEs are related to a DDI and that approximately 5% of all
ICU patients will experience an ADE during hospitalization due to a pDDI4,5.
Because approximately half of the ADEs resulting from DDIs are predictable and
preventable 6, it is important to detect those pDDIs that are relevant for patients in
the ICU. Several studies have investigated the frequency and nature of pDDIs in a
general hospital setting7, 8. The occurrence of pDDIs in an ICU may, however, differs
from general wards, because a larger number of medications may be used, (dose)
changes may occur more often, and medications can often be classified as high-risk9.
Moreover, compared with patients in other departments, ICU patients are generally
more severely ill, which can interfere with their ability to metabolize drugs and
their capacity to counteract and deal with physiological disturbances. The larger
number of medications may cause pDDIs to occur more frequently in ICU patients,
and their altered capacity to counteract with physiologic disturbances may cause
the consequences to be more severe than in a general hospital setting10. However,
the technical environment of the ICU -- including bed-site diagnostic testing and
continuous monitoring -- allows for an effective risk management strategy and the
possibility of quick adjustment.
Data on the occurrence and management of pDDIs on ICUs are still limited. In some
smaller studies, the percentage of ICU patients exposed to at least one pDDI ranged
from 45% to 73%11,12,13. However, the number of patients included in these studies
was only 100-400. One recent large study of 9644 admissions found that 3892
(40%) experienced at least one pDDI. Potential clinical outcomes and management
advices were not discussed14.
Therefore, the objective of this study was to describe the type and frequency of pDDIs
occurring on a large general ICU and to make recommendations for improving the
management of these pDDIs.
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Materials and Methods
Setting and study populationThis retrospective observational study was carried out at the 32-bed ICU of the
University Medical Centre Utrecht (UMCU), which is a tertiary care teaching hospital
in the Netherlands. All medications and clinical data for patients hospitalized at
the ICU are registered in an electronic Patient Data Management System (ePDMS;
Metavision; iMDsoft, Sassenheim, the Netherlands). No computerized medication
surveillance system has been implemented in this ePDMS. However, a clinical
pharmacist reviews each patient’s medication each day and discusses relevant
pDDIs with the clinicians. All adult-patients (18 years or older) admitted to the
ICU for more than 24 hours between May 2009 and December 2010 and who were
prescribed at least one medication were eligible for inclusion. If a patient was
admitted more than once to the ICU during this time period either within the same
hospitalization period or during a subsequent admission, only the first admission
was included. Because only routinely documented patient data were used, the
ethics board waived the need for approval.
Potential drug-drug interactionsA pDDI was defined as two potentially interacting drugs that were administered
concomitantly. Relevant pDDIs were reconstructed by linking the G-Standaard
from May 2009 with the medication records from the ePDMS research database. In
the Netherlands, a working group of the Scientific Institute of Dutch Pharmacists
developed and maintains the G-Standaard, an evidence-based professional
guideline for the management of pDDIs, described in detail elsewhere15. In this
professional guideline, pDDIs are classified into either ‘relevant’ pDDIs requiring
an interaction alert (358 pDDIs) or ‘less/not relevant’ pDDIs that do require an alert.
Based on the G-Standaard’s textual information on background, mechanism, and
advice, the pDDIs were classified into ‘potential clinical outcome’ categories were
described in detail elsewhere7. One pDDI could potentially have multiple outcomes
or management advice.
In the ePDMS, medication is not registered as a medication record with a start and
stop date; each administration is registered separately. For this study, medication
records were reconstructed retrospectively. Administrations for a specific drug were
attributed to one medication record if the time gap did not exceed 12 hours for
continuously administered medications (e.g., dobutamine 2.5 mg/kg/min) and 36
hours for discontinuously administered medications (e.g. erythromycin two times/
day 250 mg).
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Data Analysis and Statistical AnalysisThe frequency of pDDIs was calculated as the number of pDDIs per patient; the
number of patients that experienced at least one pDDI; and the number of ICU
days with at least one pDDI. The frequencies were calculated separately for baseline
patient characteristics such as length of ICU stay, predicted hospital mortality,
observed hospital mortality, mechanical ventilation upon admission, admitting
service, number of comorbidities, and the number of different drugs used. Predicted
hospital mortality was calculated during the first 24 hours of ICU admission, using
the Acute Physiologic and Chronic Health Evaluation IV model16.
The type of pDDIs was described by listing the 20 most frequently occurring pDDIs.
The percentage of pDDIs in each potential clinical outcome category and in the
management advice category was listed.
Data were analyzed using descriptive statistics from the SPSS software v. 17.0 for
Windows (SPSS, Chicago, IL).
Results
During the study period, 3489 patients 18 years of age or older were hospitalized
in the ICU; 1793 were admitted to the ICU for less than 24 hours and 37 patients
did not receive any medication during their stay in the ICU. This resulted in a total
of 1659 patients, accounting for 10252 ICU days and a median length of stay in the
ICU of 2.9 (Interquartile range 1.7-6.8) days. Among the 1659 patients, 84% were
mechanically ventilated; the observed hospital mortality was 20% (Table 1).
Frequency of pDDIsIn total, 35,784 medication records were identified, resulting in 2,887 pDDIs (8.1%).
The total study population was 1,659 patients, for a mean frequency of 1.7 pDDIs
per patient (95% confidence interval, [CI] 1.6-1.9) (Table 2). During ICU admission,
900 patients (54%) experienced at least one pDDI. Within this subgroup of patients,
the frequency was 3.2 pDDIs per patient (95% CI, 3.0-3.4). One or more pDDIs were
present during 27% of all ICU admission days. Table 2 lists these three outcomes for
the total study population stratified for various baseline characteristics such as age,
gender, predicted mortality upon ICU admission and admitting service.
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Table 1. Characteristics of the study population
Population characteristics (n=1659)
Male gender; %, n 60% (n=994)
Age; median, interquartile range 62 years (50-72)
Observed ICU Length of stay; median, interquartile range 2.9 days (1.7-6.8)
Observed hospital mortality; %, n 20% (n=324)
APACHE IV predicted mortalitya; mean %, 95% CI 23.0% 22.7-25.0%
Mechanical ventilation at admission; %, n 84% (n=1340)
Elective admission to ICU; %, n 36% (n=581)
Admitting medical specialism; %, n
Internal medicine 18% (n=293)
General surgery 26% (n=431)
Cardiology or cardiothoracic surgery 34% (n=569)
Neurology or neurosurgery 22% (n=366)
Comorbidities; %, n
Cardiovascular 9.9% (n=164)
Diabetes mellitus 13% (n=212)
Renal disease b 6.0% (n=99)
Hepatic disease c 1.0% (n=16)
Respiratory disease 14% (n=230)
Number of different drugsd;; %, n
1-12 31% (n=507)
13-20 38% (n=623)
≥21 32% (n=529)
pDDI = potential drug-drug interaction; CI = confidence interval; ICU = intensive care unit; APACHE = Acute Physiologic and Chronic Health Evaluation Three patients were prescribed only one medicationa calculated within the first 24 hours of ICU admission, using the APACHE IV model;b Renal diseases: serum creatinine > 177micromol/L combined with a medical history of chronic renal disease or renal dialysis c Hepatic disease: including portal hypertension combined with a positive liver biopsy or bleeding in the upper part of the gastro-intestinal tract, hepatic encephalopathyd Sum of unique drugs per patient (active ingredient and dosage form) during the entire ICU stay
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Table 2. Main Outcome Parameters
pDDIs per patient Patients with ICU days with
(mean; 95%CI a) ≥ 1 pDDI ≥ 1 pDDI % (n)
total DDIs total patients total ICU days
n = 2,887 n = 1,659 n = 10,252
Total 1.7 (1.6-1.9) 54% (n=900) 27% (2,779/10,252)
Gender
Male 1.8 (1.6-2.0) 56% (n=557/994) 27% (n=1,774/6,471)
Female 1.6 (1.4-1.9) 52% (n=343/665) 27% (n=1,005/3,781)
Age category, yrs
18-39 1.2 (0.9-1.6) 42% (n=95/228) 18% (n=284/1,590)
40-59 1.6 (1.4-1.9) 51% (n=251/497) 25% (n=772/3,129)
- 60-79 1.9 (1.7-2.1) 59% (n=472/804) 29% (1,399/4,742)
≥ 80 2.4 (1.7-3.0) 63% (n=82/130) 41% (324/792)
Length of stay ICU, days
1-2 0.6 (0.5-0.7) 39% (n=332/853) 10% (138/1,436)
3-4 1.5 (1.2-1.7) 58% (n=148/256) 22% (223/1016)
5-6 2.4 (2.0-2.8) 68% (n=113/165) 29% (293/998)
≥ 7 4.2 (3.7-4.6) 80% (n=307/385) 31% (2,126/6,802)
Apache IV predicted mortality, %
0-33 1.3 (1.2-1.5) 51% (n=578/1,145) 24% (1,395/5,803)
34-66 3.0 (2.5-3.4) 65% (n=192/294) 32% (887/2,796)
67-100 2.7 (2.0-3.4) 62% (n=76/123) 35% (335/967)
Unknown 1.7 (1.1-2.3) 56% (n=54/97) 23% (161/686)
Observed hospital mortality
Yes 2.5 (2.1-2.9) 57% (n=185/324) 32% (874/2,694)
No 1.6 (1.4-1.7) 54% (n=694/1,288) 25% (1,799/7,093)
Unknown 1.8 (0.9-2.72) 45% (n=21/47) 23% (106/465)
Mechanical ventillation at admission
Yes 1.8 (1.6-1.9) 56% (n=744/1,340) 27% (2,198/8,169)
No 1.9 (1.5-2.3) 51% (n=127/251) 29% (563/1,969)
Unknown 1.0 (0.37-1.54) 43% (n=29/68) 16% (18/114)
Elective admission at ICU
Yes 1.3 (1.2-1.5) 55% (n=319/581) 25% (577/2,300)
No 2.0 (1.8-2.2) 54% (n=556/1,026) 28% (2,187/7,860)
Unknown 1.2 (0.4-1.9) 48% (n=25/52) 16% (15/92)
Admitting medical specialty
Internal medicine 2.2 (1.8-2.7) 51% (n=150/293) 31% (845/2,747)
General surgery 1.6 (1.3-1.8) 43% (n=186/431) 23% (711/3,132)
Cardiology or cardiothoracis surgery 2.0 (1.7-2.2) 70% (n=396/569) 33% (822/2,504)
Neurology or neurosurgery 1.3 (1.0-1.5) 46% (n=168/366) 21% (398/1,863)
Number of comorbidities
0 1.5 (1.4-1.7) 50% (n=522/1,045) 24% (1,545/6,367)
1 2.0 (1.7-2.3) 61% (n=253/415) 29% (760/2,628)
≥ 2 2.8 (2.1-3.5) 69% (n=97/141) 39% (452/1,149)
Unknown 1.12 (0.43-1.81) 48% (n=28/58) 19% (21/109)
Number of different drugs usedb
1-12 0.3 (0.3-0.4) 25% (n=129/507) 8% (81/989)
13-20 0.9 (0.8-1.0) 51% (n=315/623) 15% (344/2,229)
≥ 21 4.0 (3.7-4.4) 86% (n=456/529) 33% (2,353/7,034)
pDDI = potential drug-drug interaction; CI = confidence interval; ICU = intensive care unit; APACHE = Acute Physiologic and Chronic Health Evaluationa 95%CI = 95% Confidence Intervalb sum of unique drugs per patient (active ingredient and dosage form) during the entire ICU stay
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Type of pDDIsOnly 81 (23%) of the 358 relevant unique pDDI-pairs described in the G-Standaard
occurred in our study population. Of the 81 different pDDI pairs observed, 20
pDDI pairs accounted for 93% of all observed pDDIs (Table 3). The most frequently
occurring potential clinical consequence of the pDDIs was an increased risk of side
effects or toxicity (91% of all pDDIs); masking of hypoglycemia (21%) and electrolyte
disturbances (20%) were the most common (Table 4). Another potential clinical
consequence is decreased effectiveness of the medication. This was potentially
the case in 10% of all encountered pDDIs, mainly due to interactions with
antihypertensive drugs (4.6%).
Table 3. Twenty most frequently occurring pDDIs in ICU patients
pDDI Frequency n
β-blockers selective + insulin 15.9% 459
Midazolam/alprazolam + CYP3A4- inhibitors 11.0% 317
QT-prolongators + QT-prolongators 10.7% 310
RAS inhibitors + potassium salts/potassium saving agents 9.7% 280
ACE-inhibitors + diuretics 8.5% 245
Acetazolamide + diuretics (potassium losing) 5.7% 164
β-blockers nonselective + insulin 5.0% 143
Coumarins + antibiotics 4.3% 125
Potassium salts + potassium saving agents 4.2% 120
β-blockers nonselective + β-mimetics 4.0% 116
NSAIDs (excl.COXIBs) + corticosteroids 3.7% 107
Statins + CYP3A4 inhibitors 2.1% 60
Midazolam/Alprazolam + enzyme inductors 1.5% 42
Corticosteroids + enzyme inductors 1.4% 40
Tacrolimus + CYP3A4 inhibitors 1.2% 34
Coumarins + Salicylates 1.0% 30
Haloperidol + enzyme inductors 0.9% 26
Calcium antagonists + CYP3A4 inhibitors 0.8% 22
Digoxine + Amiodarone 0.6% 18
Ciclosporine + CYP3A4 inhibitors 0.5% 15
Total 20 most frequently occurring pDDIs 92.6% 2673
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Table 4. Type of pDDIs: Possible Clinical Outcome Categories and Advised Management Strategy
Potential outcome category pDDIs % (n); total n=2,887
Increased risk of side-effects/toxicity 90.6a(2,617)
Masking hypoglycemia 20.9 (604)
Electrolyte disturbance 19.6 (565)
Cardiac arrhythmias (including QT-prolongation) 11.7 (338)
Increased effect benzodiazepines 11.0 (317)
Bleeding risk (incl gastrointestinal ulcer risk) 10.3 (296)
Hypotension 8.8 (253)
Nephrotoxicity 2.4 (70)
Myopathy 2.1 (60)
Other 3.1 (104)
Risk of decreased efficacy 10.7a(310)
Antihypertensive drugs 4.6 (134)
Immunomodulators 1.5 (43)
Benzodiazepines/opioids 1.5 (42)
Antipsychotic drugs (haloperidol) 0.9 (26)
Other 2.3 (65)
Advised Management strategy pDDIs % (n); total n=2,887
Monitoring 80.8b (2,330)
Monitoring of laboratory values 51.6c (1489)
Glucose 20.9 (602)
Potassium 19.6 (566)
Drugs (therapeutic drug monitoring) 5.0 (144)
Blood clotting time (international normalized ratio) 4.6 (134)
Kidney function (serum creatinine) 1.2 (36)
Liver function 0.2 (5)
Sodium 0.1 (2)
Clinical monitoring of toxicity/effectiveness 48.4c (1,398)
ECG monitoring 10.7c (310)
Blood pressure monitoring 0.6c (16)
Avoid combination 40.3b (1,163)
Adjust dose/titrate dose slowly 25.0b (722)
Risk-modifying strategy 19.3b (558)
Taking medication when sitting or laying down 8.8 (253)
Potassium or potassium sparing diuretic 5.7 (166)
Add gastric protection (proton pump inhibitor) 4.6 (134)
Separate moments of oral administration 0.2 (5)
Other 1.9b (54)
pDDI = potential drug-drug interaction; ECG = electrocardiogram.a Percentages add up to more than 100% because one alert could potentially have multiple outcomesb Percentages add up to more than 100% because one alert could potentially have multiple management advicec Percentages add up to more than 100% because one alert could potentially have multiple advice for monitoring
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With respect to the management of pDDIs, monitoring was the most important
advised management strategy (81%) (Table 4) and included the monitoring of
laboratory values (52%), toxicity or effectiveness (48%) and physical parameters
such as electrocardiogram and blood pressure (11%). Other frequently advised
strategies were avoiding combination (40%), adjusting or titrating dose (25%) and
modifying risk strategy (19%). Comparable results for potential clinical outcome
and management strategies were obtained when the data were analyzed for the
number of patients instead of the number of pDDIs (data not shown).
Discussion
At least one relevant pDDI occurs in 54% of all ICU patients. Only a limited set of
drug-drug pairs (n=20) is responsible for more than90% of all pDDIs. Most pDDIs
can be managed by monitoring either clinical symptoms or laboratory values.
The percentage of patients on the ICU experiencing at least one pDDI in our study
is comparable with the percentages found in other ICU studies (40-73%) 11-14. The
percentage is twice as high as the percentage on the general wards of the same
hospital as previously reported (28%; n=5,909) 7. However, when the number of
pDDIs is considered in relation to the number of medication records, the difference
between the ICU and general hospital departments disappears (9.6% vs. 8.1%). The
average number of pDDIs calculated for ICU-patients with at least one pDDI (3.2
pDDIs per patient experiencing at least one pDDI), is also comparable to the average
number of pDDIs found on general wards (3.4 pDDIs). As such, the average number
of medications per patient is higher for ICU patients than for other hospitalized
patients, resulting in a higher percentage of patients experiencing at least one
pDDI; the percentage of pDDIs per medication prescription is about equal.
Only 20 pDDI pairs make up to more than 90% of all pDDI alerts. The 10 most common
pDDI pairs in ICU patients differs substantially from the most common pairs found
in the general wards7. This difference can be explained by the differences in the
medical needs of ICU patients compared with general ward patients. For example,
the pDDI between midazolam/alprazolam and CYP3A4 inhibitors seldom occurs in
a general ward. On the ICU however, this is the second most frequently occurring
pDDI, because midazolam is frequently prescribed to ICU patients for sedation.
The pDDI between nonsteroidal antiinflammatory drugs and corticosteroids or
bisphosphonates and antacids are among the top 10 pDDIs on general wards, but
they seldom occurred in our ICU study population.
The limited set of 20 pDDI pairs being responsible for more than 90% of all pDDIs
enables caregivers to establish local guidelines for the handling of these pDDIs.
For example, local caregivers may agree to discontinue alerts for some of these
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frequently occurring combinations because they are familiar with these pDDIs and
feel that the potential benefit of these combinations outweighs the potential risk
in their patient population. This is the case in our ICU setting for the combination
of insulin and selective β-blockers. Moreover, most (81%) of the pDDIs occurring at
the ICU can be managed by monitoring. Because ICU patients are, by definition,
intensively monitored, deviations from normal values may be noticed quickly,
even without a pDDI alert. As such, it may not be very useful to generate standard
pDDI alerts in the electronic prescribing system for these 20 well-known and easy
to monitor pDDIs. Instead, advanced clinical decision support systems linking
laboratory data to prescription data, and signaling only when values fall out of
normal ranges, may be much more valuable. Furthermore, when laboratory data
are linked to prescription data, changes may also be detected when one of the
interacting drugs is discontinued. For other, less frequently occurring pDDIs, pDDI
alerts at the moment of prescribing can still be considered.
In this study, the G-Standaard was used to reconstruct the pDDIs. Because the
G-Standaard was developed primarily for community pharmacies, some advice (
e.g. taking medication when sitting upright) are not applicable to ICU patients.
Moreover, pDDIs involving ICU specific medications, such as sedatives or inotropics,
might be missing. For example, both the Lexi-Interact (Wolter Kluwer Health,
Baltimore, Maryland)17 and Micromedex (Truven Health Analytics Inc., Greenwood
Village, Colorado) 18 databases mention several relevant pDDIs with norepinephrine
(Table 5), while no pDDIs were mentioned in the G-Standaard. However, the
G-Standaard may include pDDIs that do not occur in the Lexi-Interact17 and
Micromedex18 databases, such as the major pDDI between sotalol and telaprevir. It
is well known that there are major differences between different DDI databases11,12.
Ideally, we would have used a pDDI database especially designed for ICU patients.
Because such a database does not exist, we used the G-Standaard, the national
professional guideline used in all Dutch hospitals. For further research it would be
worthwhile to develop a specific ICU pDDI database.
The key strength of this study is that it was performed in a general ICU of an
academic hospital with a large and diverse patient population, which increases
the generalizability of the results to other ICUs. Moreover, potential outcomes
and management strategies were analyzed. A limitation of this study may be
that potential adverse drug events (ADEs) resulting from pDDIs have not been
studied. Although theoretically it would be worthwhile to measure the clinical
consequences of the pDDIs, in practice it is almost impossible to attribute clinical
outcomes to pDDIs in complex and severely ill ICU patients.
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Table 5. Drug-Drug Interactions Involving Five Specific Intensive Care Unit Medicationsa Missing in The G-Standaard
Drug-drug pair EffectPatients% (n)
Propofol + midazolam Serum concentration of propofol/midazolam may increase
16% (273)
Dopamine + sympathicomimetics
Sympathicomimetic may increase the effect of other sympathicomimetics
12% (202)
propofol + CYP2D6 inhibitors
Decreased metabolism of propofol 7.2% (120)
Epinephrine + sympathicomimetics
Sympathicomimetic may increase effect of other sympathicomimetics
4.0% (67)
Dobutamine + calcium salts Effect of dobutamine may decrease 3.5% (58)
Norepinephrine + β-adrenergic blockers
Effect norepinephrine may increase 3.0% (50)
(nor)epinephrine + spironolacton
Vascular effect of (nor)epinephrine may decrease
2.3% (38)
Dobutamine + β-adrenergic blockers
Effect of dobutamine may decrease 2.1% (35)
(nor)epinephrine + Carbonic anhydrase inhibitors
Excretion of (nor)epinephrine may decrease
1.9% (32)
Epinephrine + antacids Excretion of epinephrine may decrease 1.3% (21)
Norepinephrine + antidepressant, tricyclic
Effect norepinephrine may increase 0.8% (13)
Epinephrine + rocuronium Increased risk on postoperatieve paralysis
0.8% (13)
Epinephrine + haloperidol Decreased effect of epinephrine 0.1% (2)
afive specific ICU medications: propofol, dopamine, dobutamine, epinephrine, norepinephrine
Conclusion
Potential drug-drug interactions occur in 54% of all ICU patients. A limited set of
20 pDDI-pairs is responsible for more than 90% of all pDDIs occurring on the ICU.
Therefore it is very worthwhile to develop guidelines for the management of these
specific pDDIs. Because the vast majority of the interactions can be managed by
monitoring, advanced clinical decision support systems linking laboratory data
to prescription data may be an important tool for effective management of risks
associated with pDDIs.
Acknowledgment
The authors are grateful to Hanneke den Breeijen for the data analysis.
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References
1 Leape LL, Brennan TA, Laird N et al. The nature of adverse events in hospitalized patients: results of
the Harvard Medical Practice Study II. N Engl J Med 1991;324:377-384
2 Nebeker JR, Hoffman JM, Weir CR, Bennett CL, Hurdle JF. High rates of adverse drug events in a highly
computerized hospital. Arch Intern Med 2005;165:1111-1116
3 Krahenbuhl-Melcher A, Schlienger R, Lampert M, Haschke M, Drewe J, Krahenbuhl S. Drug-related
problems in hospitals: a review of the recent literature. Drug Saf 2007;30:379–407
4 BJ Kopp, Erstad BL, Allen ME, Theodorou AA, Priestley G. Medication errors and adverse drug events
in an intensive care unit: direct observation approach for detection. Crit Care Med 2006;34:415-425
5 Reis AMM, de Bortoli Cassiani SH. Adverse drug events in an intensive care unit of a university
hospital. Eur J Clin Pharmacol 2011;67: 625-632
6 Bertsche T, Pfaff J, Schiller P et al. Prevention of adverse drug reactions in intensive care patients by
personal intervention based on an electronic clinical decision support system. Intensive Care Med
2010;36:665–672
7 Zwart-van Rijkom JE, Uijtendaal EV, ten Berg MJ, van Solinge WW, Egberts ACG. Frequency and nature
of drug-drug interactions in a Dutch university hospital. Br J Clin Pharmacol 2009;68:187-193
8 Reimche L, Forster AJ, van Walraven C. Incidence and contributors to potential drug-drug interactions
in hospitalized patients. J Clin Pharmacol 2011;51:1043-1050
9 Institute of Safe Medication Practices: ISMP’s list of high alert medications 2008. http://www.ismp.
org/Tools/highalertmedications.pdf. (Accessed at March 1th, 2013)
10 Spriet I, Meersseman W, de Hoon J et al. Mini series: II-clinical aspects: clinically relevant CYPP450-
mediated drug interactions in the ICU. Intensive Care med 2009;35:603-612
11 Smithburger PL, Kane-Gill SL, Seybert AL. Drug-drug interactions in cardiac and cardiothoracic
intensive care units. Drug Saf 2010;33:879-888
12 Smithburger PL, Kane-Gill SL, Seybert AL. Drug-drug interactions in the medical intensive care unit:
an assessment of frequency, severity and the medications involved. Int J Pharm Pract 2012;20:402-408
13 Lima REF, Cassiani, SHB. Potential drug interactions in intensive care patients at a teaching hospital.
Rev LatAm Enfermagem 2009;17:222-227
14 Askari M, Eslami S, Louws M, et al. Frequency and nature of drug-drug interactions in the intensive
care unit. Pharmacoepidem Drug Safe 2013;22:430–437
15 van Roon EN, Flikweert S, le Comte M. Clinical relevance of drug-drug interactions : a structured
assessment procedure. Drug Saf 2005;28:1131-1139
16 Cerner, Kansas City: The APACHE IV equations: benchmarks for mortality and resource use. https://
apachefoundations.cernerworks.com/apachefoundations/resources/APACHE%20IV%20White%20
Paper%20Version%201.0.pdf (Accessed at July 1th, 2013)
17 Up-to-Date, Inc.: Lexi-interact online. http://www.uptodate.com/crlsql/interact/frameset.jsp (Accessed
at July 1th, 2013)
18 Thomson Reuters (Healthcare) Inc.: Micromedex healthcare series. Interactions. Updated periodically.
Available from http://micromedexsolutions.com/micromedex2/librarian. (Accessed at March 1th,
2013)
3Monitoring drug therapy in
hospitalized patients
3.1Frequency and determinants of
laboratory measurements for serum
potassium, sodium and creatinine in
hospitalized patients
Esther V. Uijtendaal
Jeannette E.F. Zwart-van Rijkom
Wouter W. van Solinge
Toine C.G. Egberts
Maarten J. ten Berg
Chapter 3.1 | Frequency and determinants of laboratory measurements for serum potassium, sodium and serum creatinine in hospitalized patients
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Abstract
BackgroundAdequate laboratory monitoring is considered important in the prevention of adverse drug events (ADEs). Information on laboratory markers is also essential in medication reconciliation at discharge. There is limited knowledge, however, on how often and when general biomarkers that are relevant to pharmacotherapy, such as serum potassium, sodium and creatinine are measured in daily clinical hospital practice.
ObjectiveThis study aimed to estimate the frequency and timing of measurement of serum creatinine, potassium and sodium in hospitalized patients and determinants thereof. Determinants of special interest were the use of potassium- and sodium influencing medications and the use of drugs in which renal function needs to be known (DRF drugs).
MethodsA retrospective observational study was conducted using the Utrecht Patient Oriented Database. All patients aged 18 years or older hospitalised for ≥24 hours were included. The proportion of patients with at least one measurement of potassium, sodium or creatinine respectively was calculated. The stratified estimates per medication-group were expressed as percentage and as odds ratios (ORs) with 95% confidence intervals. Also the time between the last measurement and discharge was determined. Potassium- and sodium influencing medications and DRF drugs were determined in accordance with the Dutch professional medication guideline on clinical risk management.
ResultsPotassium, sodium and creatinine were measured at least once during hospital admission in about 50% of the patients. The percentages of patients with a measurement was associated with patient- and admission related factors, i.e. percentages varied largely among different age groups and medical specialties, but percentages were also associated with the use of specific medications, i.e. potassium levels were measured in 99% of the patients using a potassium supplement. The use of a DRF drug did not influence laboratory monitoring of serum creatinine. Relevant laboratory markers were not measured within the last 24 hours before discharge in the majority of the patients using medication that requires laboratory monitoring, i.e. ranging from 72% of the patients using a potassium decreasing drug to 88% of the patients using a DRF drug.
ConclusionThis study provided insight into the frequency and determinants of monitoring electrolytes and creatinine at our institution. The results suggest that required laboratory monitoring in pharmacotherapy for detecting ADEs is low. In addition, we observed a low frequency of laboratory monitoring at discharge. Effort should be made to improve monitoring for ADEs.
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Introduction
The prevention of adverse drug events (ADE) has become a major public health
issue since it was shown that these occur frequently, are to a substantial part
potentially preventable and contribute majorly to health care costs1,2. It has been
estimated that 6.5 ADEs and 5.5 potential ADEs occur per 100 admissions and that
approximately 30-50% could have been prevented1,3.
Part of the ADEs may be prevented by adequately monitoring the clinical effects
during the course of drug therapy4. This monitoring may consist of careful direct
patient observation (e.g. skin reactions), but monitoring may also concern the
measurement of physical parameters like blood pressure and biochemical markers
like serum creatinine. Adequate monitoring of drug therapy is considered one of
the most important strategies to prevent ADEs; 61% of potentially preventable ADEs
occurred due to inadequate monitoring of drug therapy or a delayed response or
failure to respond to signs and symptoms or laboratory evidence of drug toxicity3.
Inadequate monitoring was also the most frequently reported category (22-45%)
of drug therapy problems leading to preventable hospital admissions5,6. The most
frequently involved drug-related problems leading to hospital admission were
bleeding, disturbances of serum electrolyte levels and kidney insufficiency5,7.
Monitoring is also the most frequently advised mitigation strategy in clinical
guidelines for risks related to drug-drug interactions8. A limited set of laboratory
markers, i.e. international normalized ratio (INR), serum creatinine and serum
electrolytes are important to monitor for the majority of these problems7,9-11. This
knowledge has led to an amendment of the Dutch Drug Law, obligating the exchange
of information between health care professionals on a limited set of laboratory
markers namely drug serum levels, INR, serum creatinine, potassium, sodium
levels and pharmacogenetic markers12. Due to practical implementation problems
this law was amended again in July 2013, now only demanding to communicate
laboratory values on kidney function when these deviate from normal limits13.
Whether health care professionals adhere to this obligation, however, is not known.
The exchange of these laboratory data between health care providers aims to
contribute to a continuity of care14. Discontinuity of care has been found to lead
to significant harm to patients, including harm due to ADEs7,15. Because laboratory
facilities are easily accessible and intensely used in hospitals, values for these markers
will be generally available for hospitalized patients. Information on these markers
can than also be provided upon discharge. However, there is limited knowledge on
how often these biomarkers relevant to pharmacotherapy are measured in daily
clinical practice. A recent study showed that serum creatinine levels were measured
in 59% of the hospitalized elderly patients16, but this frequency is not known for other
Chapter 3.1 | Frequency and determinants of laboratory measurements for serum potassium, sodium and serum creatinine in hospitalized patients
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relevant drug-monitoring-biomarkers or for other groups of hospitalized patients. It
is also not known whether the decision to measure these laboratory values depends
on patient, medication and hospitalization characteristics. Moreover, it is not
known when this measurement is taken, shortly after admission and/or just before
leaving the hospital. If a measurement is performed shortly after admission, values
may represent the patients’ illness rather than the patients’ condition at discharge.
This may be relevant when laboratory data are communicated to the next health
care provider after discharge. This study therefore aims to estimate the frequency of
measurement of three general laboratory markers, serum potassium, sodium and
creatinine, in hospitalized patients and determinants thereof. Of special interest is
the association between the use of potassium- and sodium influencing medication
and drugs needing dose adjustment in case of renal impairment and measurement
of these biomarkers. Finally, this study also aims to determine the timing of these
laboratory measurements during hospitalization.
Methods
Design and setting This hospital-based retrospective observational study was performed in the
University Medical Centre Utrecht (UMCU), a 1042-bed academic medical centre
located in the Netherlands. At the UMCU all medications are prescribed using a
computerized physician order entry system. For research purposes all prescriptions
are routinely exported in the Utrecht Patient Oriented Database (UPOD). UPOD is
an infrastructure of relational databases comprising data on patient demographics,
hospital discharge diagnoses, medical procedures, medication orders and laboratory
tests for all patients treated at the UMCU since 2004, and has been described in
detail elsewhere17.
Study populationAll patients aged 18 years or older hospitalized in the UMCU between October 1st
2011 and October 1st 2013 for ≥24 hours were included. Per patient only the first
episode of hospital admission during the study period was included and only the
time spent on the nursing wards was included, the days that patients stayed at the
intensive care unit (ICU) were excluded from the analysis.
OutcomesPer patient all laboratory values of serum potassium, sodium and creatinine
measured during admission were collected. The proportion of patients with at least
one measurement during hospitalization was assessed for potassium, sodium and
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creatinine. In addition, the percentage of patients in which all three biomarkers
were measured at least once was determined.
DeterminantsPatient-, admission, and medication related determinants for laboratory marker
measurement were studied. Patient-related determinants evaluated were gender,
age, treatment specialism at discharge, urgent admission (defined as admitted
through the emergency department). Admission related determinants were ICU
stay during admission, length of stay, and number of medication orders during
hospital stay. Medication-related determinants were the use of a serum potassium
increasing drug, a serum potassium decreasing drug, a serum sodium decreasing
drug or the use of a drug that is renally cleared and requires dose modification in
case of decreased renal function (DRF-drug). A drug was classified as a potassium
increasing, potassium decreasing and sodium decreasing drug as the drug was
known to affect serum potassium or serum sodium levels respectively18-21 and if
monitoring was advised according to the Dutch professional medication guideline
on clinical risk management22. The drugs that are classified into one of the potassium
increasing, potassium decreasing or sodium decreasing subgroups are represented
in Table 1. DRF-drugs were defined following the classification in the Z-Index of
October 2013: dose reduction for decreased renal function was determined relevant
for 250 of the 606 (41%) medications that are renally cleared.
Table 1: Drugs defined as potassium increasing, serum potassium decreasing and serum sodium decreasing drugs
Serum potassium increasing drugs
Serum potassium decreasing drugs
Serum sodium decreasing drugs
Potassium supplements diuretics (lis- and thiazides) diuretics (lis- and thiazides)
RAAS-inhibitors acetazolamide (ox)carbamazepine
potassium saving diuretics mineralocorticosteroids SSRI’s
potassium-binders venlafaxine and duloxetine
Data analysisThe proportions of patients with at least 1 measurement during hospitalization were
stratified per biomarker for patient characteristics, hospitalization characteristics
and medication-related factors. The stratified estimates per medication-group
were also expressed as odds ratios (ORs) with 95% confidence intervals. ORs were
adjusted for gender, age, treatment specialty at discharge, IC stay during admission,
admission due to an emergency, length of hospital stay and the number of
medication orders during hospital stay.
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For the three biomarkers the number of hours between the time of discharge and
the time of last measurement was calculated per patient. In addition, the proportion
of patient with at least one measurement within the last 24 hours of admission was
calculated per biomarker. These proportions were stratified for medication-groups.
Data analyses were performed using SPSS release 20 (SPSS release 20 (SPSS, Inc.,
Chicago, Illinois).
Results
During the study period, 31,982 patients ≥= 18 years were hospitalized ≥ 24 hours in
the UMCU. Mean age was 52.7 years (95%CI = 52.5-52.9 years) and median length of
stay was 3.2 days (interquartile range = 1.9-7.1 days). Approximately one third of the
patients were hospitalized for surgical specialties and 10% of the patients stayed
one or more days at the ICU during hospital admission (Table 2).
In total, 48% of the patients had at least one potassium measurement, 48% had at
least one sodium measurement and 49% had at least one creatinine measurement
during hospitalization. Most patients with at least one measurement had
measurement for all three laboratory markers during hospital stay, i.e. all three
biomarkers were measured in 45% of the patients (Figure 1a).
The proportion of patients with at least one measurement of the biomarkers stratified
for patient- and admission-related factors is presented in Table 2. Proportions
increased with increasing age, length of stay, and number of medication orders, e.g.
potassium was measured in 25% of the patients of 35 years and younger and this
percentage increased to 70% for patients older than 70 years. The percentages of
measurements were also higher in males, for patients who had been admitted due
to an emergency and for patients who stayed at the ICU during hospital stay. With
regard to the treatment specialism, percentages of measurements were highest
for cardio-pulmonary patients (73-78%) and lowest for gynecology patients (7-15%).
Figure 1 shows which measurements were made in patients using a medication
from one of the medication groups.
59
3.1
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40Tab
le 2
: Pe
rcen
tage
of
pat
ien
ts w
ith
at
leas
t on
e m
easu
rem
ent
for
seru
m p
otas
siu
m,
sod
ium
an
d c
reat
inin
e p
er c
ateg
ory
of p
atie
nt
char
acte
rist
ics
n
um
ber
(to
tal)
K+
mea
sure
men
t (%
)N
a+ m
easu
rem
ent
(%)
Cre
at m
easu
rem
ent
(%)
Gen
der
M15
480
55.4
55.2
55.4
F
1650
241
.040
.842
.4
age
cate
gory
(yr)
≤35
7735
24.9
24.8
28.1
35-5
588
6741
.441
.241
.8
55-7
087
1759
.158
.958
.2
>7
066
6369
.168
.869
.3
trea
tmen
t sp
ecia
lty
at d
isch
arge
gyn
ecol
ogy
4872
6.5
6.4
15.3
surg
ery
1049
432
.132
.030
,0
onco
logy
1886
59.3
59.0
56.5
inte
rnal
3951
69.9
69.0
72.9
brai
n53
2671
.371
.264
.9
ca
rdio
-pu
lmon
ary
5453
73.3
73.2
78.3
IC a
dm
issi
on d
uri
ng
hos
pit
aliz
atio
nN
o28
738
43.3
43.1
44.3
Ye
s32
4489
.589
.387
.5
urg
ent
adm
issi
onN
o23
608
36,0
35.8
37.2
Ye
s83
7481
.881
.781
.2
len
gth
of
stay
, tot
al (d
ays)
1-2
8931
20.5
20.3
24.0
3-4
1130
337
.136
.937
.6
>=
511
748
79.4
79.2
78.1
nu
mbe
r of
med
icat
ion
ord
ers
(du
rin
g h
osp
ital
sta
y)<=
510
310
24.3
24.2
28.6
6-13
1089
941
.241
.141
.5
>=
1410
773
77.5
77.1
75.2
Chapter 3.1 | Frequency and determinants of laboratory measurements for serum potassium, sodium and serum creatinine in hospitalized patients
60
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
Figure 1: Distribution of serum potassium, sodium and creatinine measurements per stratum of medication group
Figure 1: distribution of serum potassium, sodium and creatinine measurements per stratum of medication group
1.a: distribution for all patients
1.b: Patients using a potassium increasing drug (n=8739 (25%)
1.c: Patients using potassium decreasing drug n=5881 (18%)
1.d: Patients using sodium decreasing drug n=6670 (21%)
1.e: Patients using a using a drug that is renal cleared or affects renal function n=24542 (77%)
Figure 1: distribution of serum potassium, sodium and creatinine measurements per stratum of medication group
1.a: distribution for all patients
1.b: Patients using a potassium increasing drug (n=8739 (25%)
1.c: Patients using potassium decreasing drug n=5881 (18%)
1.d: Patients using sodium decreasing drug n=6670 (21%)
1.e: Patients using a using a drug that is renal cleared or affects renal function n=24542 (77%)
1.a: distribution for all patients 1.d: Patients using sodium decreasing drug n=6670 (21%)
Figure 1: distribution of serum potassium, sodium and creatinine measurements per stratum of medication group
1.a: distribution for all patients
1.b: Patients using a potassium increasing drug (n=8739 (25%)
1.c: Patients using potassium decreasing drug n=5881 (18%)
1.d: Patients using sodium decreasing drug n=6670 (21%)
1.e: Patients using a using a drug that is renal cleared or affects renal function n=24542 (77%)
Figure 1: distribution of serum potassium, sodium and creatinine measurements per stratum of medication group
1.a: distribution for all patients
1.b: Patients using a potassium increasing drug (n=8739 (25%)
1.c: Patients using potassium decreasing drug n=5881 (18%)
1.d: Patients using sodium decreasing drug n=6670 (21%)
1.e: Patients using a using a drug that is renal cleared or affects renal function n=24542 (77%)
1.b: Patients using a potassium 1.e: Patients using a using a drug that isincreasing drug (n=8739 (25%) renal cleared or affects renal function n=24542 (77%)
Figure 1: distribution of serum potassium, sodium and creatinine measurements per stratum of medication group
1.a: distribution for all patients
1.b: Patients using a potassium increasing drug (n=8739 (25%)
1.c: Patients using potassium decreasing drug n=5881 (18%)
1.d: Patients using sodium decreasing drug n=6670 (21%)
1.e: Patients using a using a drug that is renal cleared or affects renal function n=24542 (77%)
Figure 1: distribution of serum potassium, sodium and creatinine measurements per stratum of medication group
1.a: distribution for all patients
1.b: Patients using a potassium increasing drug (n=8739 (25%)
1.c: Patients using potassium decreasing drug n=5881 (18%)
1.d: Patients using sodium decreasing drug n=6670 (21%)
1.e: Patients using a using a drug that is renal cleared or affects renal function n=24542 (77%)
1.c: Patients using potassium decreasing drug n=5881 (18%)
61
3.1
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
Table 3 shows the percentage of patients with at least one measurement for serum
potassium, sodium or creatinine per stratum of medication group. Serum potassium
was measured more often in patients using a serum potassium increasing (82% vs
35%, ORadjusted = 2.4 (95%CI 2.2-2.6)) or a potassium decreasing drug (82% vs 40%,
ORadjusted = 1.8 (95%CI 1.6-2.0)) compared to patients not using such a drug. The
highest percentage of patients with at least one potassium measurement was seen
in patients using potassium supplements, this percentage was 99% (ORadjusted = 52
(95%CI 35-77)), numbers not mentioned in tables) or in patients using a potassium
binding drug (99%, ORadjusted = 18 (95%CI 5.4-60).
At least one serum sodium level was measured in 79% of the patients using a
serum sodium decreasing drug and in 40% of the patients not using such a drug
(ORadjusted=1.7 (95%CI 1.5-1.8)). The use of a selective serotonin reuptake inhibitor
(SSRI) did not contribute to the percentage of patients with at least one serum
sodium measurement. Serum sodium levels were measured in 64% of the SSRI
users 47% of the non-users (ORadjusted = 1.11 (95%CI 0.93-1.32)). Within the group
of diuretics, only the use of a lisdiuretic contributed to the measurement of at least
one serum sodium level (ORadjusted = 2.75 (95%CI 2.43-3.12) for users of lisdiuretics
and 1.06 (95%CI 0.93-1.21) for users of thiazide diuretics). The majority of the patients
(77%) used a drug that is renally cleared and requires dose modification in case
of decreased renal function (DRF drug) during hospitalization. Serum creatinine
was measured in 55% of these patients and in 30% of the patients not using such
drugs, but the use of a DRF-drug did not contribute to the percentage of patients
with at least one serum creatinine measurement. After correction for patient and
admission related factors, the percentage of patients with at least serum creatinine
measurement was even slightly decreased compared to patients not using a DRF-
drug (ORadjusted = 0.89 (0.81-0.97)).
62
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
Tab
le 3
: N
um
ber
of p
atie
nts
wit
h a
mea
sure
men
t fo
r se
rum
pot
assi
um
, ser
um
sod
ium
an
d s
eru
m c
reat
inin
e p
er m
edic
atio
n c
ateg
ory
nu
mb
er
tota
l (u
sers
)
nu
mb
er
tota
l (n
on
u
sers
)
% K
+ m
easu
red
w
hen
usi
ng
% K
+ m
easu
red
w
hen
no
t u
sin
gO
R a
dj
e
% N
a+
mea
sure
d
wh
en u
sin
g
% N
a+
gem
eten
w
hen
no
t u
sin
gO
R a
dj
e
%
Cre
atin
ine
mea
sure
d
wh
en u
sin
g
%
Cre
atin
ine
mea
sure
d
wh
en
no
t u
sin
gO
R a
dj
e
K i
ncr
easi
ng
dru
g a
8739
2324
381
.835
.32.
37 (2
.18-
2.57
)81
.435
.12.
33 (2
.15-
2.52
)80
.336
.82.
14 (1
.98-
2.32
)
K d
ecre
asin
g d
rug
b58
8126
101
81.7
40.4
1.81
(1.6
4-1.
98)
81.3
40.2
1.78
(1.6
2-1.
96)
80.3
41.6
1.66
(1.5
1-1.
82)
Na
dec
read
ing
c66
7025
312
79.4
39.7
1.66
(1.5
2-1.
81)
79.1
39.5
1.67
(1.5
3-1.
82)
77.9
41.0
1.57
(1.4
5-1.
71)
DR
F d
rug
d
2454
274
4055
.523
.21.
31 (1
.19-
1.45
)55
.223
.31.
27 (1
.15-
1.40
)54
.529
.50.
89 (0
.81-
0.97
)
all
pat
ien
ts (t
otal
)
48
.0
47
.8
4
8.7
a pot
assi
um
in
crea
sin
g d
rugs
: pot
assi
um
su
pp
lem
ents
, Ren
ine
An
giot
ensi
ne
Ald
oste
ron
in
hib
itor
s, p
otas
siu
m s
avin
g d
iure
tics
b pot
assi
um
dec
reas
ing
dru
gs: d
iure
tics
(lis
an
d t
hia
zid
es),
acet
azol
amid
e, m
iner
aloc
orti
cost
eroi
ds,
pot
assi
um
-bin
din
g d
rugs
c sod
ium
dec
reas
ing
dru
gs: d
iure
tics
(lis
an
d t
hia
zid
es),
(ox)
carb
amaz
epin
e, S
SRI’
s, v
enla
faxi
ne,
du
loxe
tin
ed D
RF:
dru
gs t
hat
are
ren
ally
cle
ared
an
d r
equ
ire
dos
e m
odifi
cati
on i
n c
ase
of a
dec
reas
ed r
enal
fu
nct
ion
e ad
just
ed f
or s
exe,
age
, sp
ecia
lity
at
dis
char
ge, I
CU
sta
y d
uri
ng
hos
pit
al s
tay,
urg
ent
adm
issi
on, l
engt
h o
f st
ay a
nd
nu
mbe
r of
med
icat
ion
ord
ers
du
rin
g h
osp
ital
sta
y
63
3.1
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
The timing of the last serum potassium, sodium and creatinine measurement prior
to discharge is presented in Figure 2 and Table 4. Of all patients, 11-12% had at
least one measurement of potassium, sodium or creatinine within the last 24 hours
before discharge. At least one serum potassium level, was obtained for 26%-28% of
the patients using a serum potassium influencing drug within 24 hours before
discharge. These percentages increased to 46-48% for the time window of 48 hours
prior to discharge. Twenty three percent of the hospitalized patients using a serum
sodium decreasing drug had at least one measurement of serum sodium within the
24 hours prior to discharge. This percentage increased to 42% when a time window
of 48 hours prior to discharge was taken. Twelve percent of the patients using a DRF
drug had a serum creatinine measurement within 24 hours prior to discharge. This
percentage increased to 25% for the time window of 48 hours prior to discharge.
Perc
ent o
f pat
ient
s
14
12
10
8
6
4
2
0
Time of last serum potassium measurement (hours before discharge)936864792720648576504432360288216144720
Page 1
Figure 2a: Time of last serum potassium measurement before discharge (all patients)
Chapter 3.1 | Frequency and determinants of laboratory measurements for serum potassium, sodium and serum creatinine in hospitalized patients
64
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
Time of last serum sodium measurement (hours before discharge)936864792720648576504432360288216144720
Perc
ent o
f pat
ient
s
14
12
10
8
6
4
2
0
Page 1
Figure 2b: Time of last serum sodium measurement before discharge (all patients)
Time of last serum creatinine measurement (hours before discharge)936864792720648576504432360288216144720
Perc
ent o
f pat
ient
s
14
12
10
8
6
4
2
0
Page 1
Figure 2c: Time of last serum creatinine measurement before discharge (all patients)
65
3.1
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40Tab
le 4
: Pe
rcen
tage
of p
atie
nts
wit
h a
t le
ast
one
mea
sure
men
t fo
r se
rum
pot
assi
um
, sod
ium
or
crea
tin
ine
per
tim
e fr
ame
of 2
4, 4
8 an
d
72 h
ours
res
pec
tive
ly b
efor
e d
isch
arge
Pota
ssiu
m m
easu
rem
ent
Sod
ium
mea
sure
men
tC
reat
inin
e m
easu
rem
ent
Dru
g u
seN
um
ber
to
tal
wit
hin
24
ho
ur
bef
ore
d
isch
arge
(%)
wit
hin
48
ho
ur
bef
ore
d
isch
arge
(%)
wit
hin
72
ho
ur
bef
ore
d
isch
arge
(%)
wit
hin
24
ho
ur
bef
ore
d
isch
arge
(%)
wit
hin
48
ho
ur
bef
ore
d
isch
arge
(%)
wit
hin
72
ho
ur
bef
ore
d
isch
arge
(%)
wit
hin
24
ho
ur
bef
ore
d
isch
arge
(%)
wit
hin
48
ho
ur
bef
ore
d
isch
arge
(%)
wit
hin
72
ho
ur
bef
ore
d
isch
arge
(%)
K i
ncr
easi
ng
dru
g87
3925
.846
.158
.923
.343
.156
.321
.240
.252
.9
no
K i
ncr
easi
ng
dru
g23
243
6.6
15.2
20.6
6.5
14.9
20.3
8.0
17.1
22.5
K d
ecre
asin
g d
rug
5881
27.7
48.2
60.4
25.5
45.3
57.8
24.3
44.0
56.2
no
K d
ecre
asin
g d
rug
2610
18.
318
.124
.47.
817
.523
.98.
718
.825
.1
Na
dec
reas
ing
dru
g66
7024
.944
.456
.123
.242
.254
.121
.840
.652
.3
no
Na
dec
reas
ing
dru
g25
312
8.4
18.1
24.4
7.9
17.5
23.8
8.9
18.9
25.1
dru
g w
ith
dos
age
dep
end
ent
on k
idn
ey f
un
ctio
n24
542
13.8
26.8
35.6
12.8
25.5
34.5
12.3
24.8
33.5
no
dru
g w
ith
dos
age
dep
end
ent
on k
idn
ey f
un
ctio
n74
405.
313
.015
.85.
213
.015
.89.
318
.721
.8
tota
l31
982
11.9
23.6
31.0
11.1
22.6
30.1
11.6
23.4
30.8
Chapter 3.1 | Frequency and determinants of laboratory measurements for serum potassium, sodium and serum creatinine in hospitalized patients
66
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
Discussion
In this study we observed that at our institution potassium, sodium and creatinine,
are measured at least once during hospital admission in about half of the patients.
In addition, we observed that monitoring of these biomarkers, which are relevant
biomarkers in clinical risk management of ADEs, is associated with the use of
specific medication. Finally, we observed that in the majority of the patients there is
no laboratory measurement for these markers in the last 24 hours before discharge
(88-89%), however in patients treated with medication that require laboratory
monitoring, this percentage is lower (72-88%).
Patients with at least one measurement, most often had laboratory measurements
for all three biomarkers during hospitalization (45% of all patients). The fact that
not one but all three biomarkers are measured during hospitalization may indicate
that patient characteristics are more important determinants for monitoring than
the use of drugs. Percentages of patients with a measurement indeed varied strongly
depending on patient and admission related factors, e.g. 7% for patients treated for
gynecology specialties to 90% for patients with an ICU stay during hospitalization.
The association between laboratory monitoring and the use of specific medications
was of special interest in this study. The percentage of patients with at least
one measurement was higher when patients were using a serum potassium
increasing, serum potassium decreasing or sodium decreasing drug, but these
percentages varied less compared to the percentages found for the different
patient characteristics. However, when the medication was intended to influence
serum levels, these levels were almost always monitored (e.g. potassium levels
were measured in 99% of the patients using potassium supplements or potassium
binding drugs). When the influence on serum levels was a side effect of drug
treatment, these levels were measured less frequently (e.g. potassium levels were
measured in 75% of the patients using renin-angiotensin system (RAS) inhibitors).
From this observation we can conclude that required laboratory monitoring is
suboptimal in our institution, putting patients at risk for ADEs.
The use of the considered medications was found be related to certain medical
specialties, i.e. potassium increasing drugs like RAS inhibitors and spironolactone
are used more often in patients hospitalized for cardiac specialties than in patients
hospitalized for other medical specialties. After adjustment for patient factors
such as medical specialty, however, the adjusted ORs for the association between
drug use and laboratory monitoring was still significant, suggesting that the use of
medication influences monitoring as well.
The use of a DRF-drug did not contribute to the percentage of patients with at
least one serum creatinine measurement. The OR for at least one serum creatinine
67
3.1
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
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measurement was even lower for patients using a DRF-drug than for patients not
using such a drug (OR=0.89; 95%CI = 0.81-0.97). The CPOE, however, did not signal to
verify kidney function when a DFR-drug was prescribed, signaling would probably
have improved the adherence to serum creatinine monitoring. Since the majority
of the patients (77%) were using a DRF drug, signaling only would have led to a high
burden of signals, causing alert fatigue and the risk that physicians override both
important and unimportant signals23,24. The specificity and clinical relevance of
these signals should therefore be high, ie, only be generated when kidney function
drops below the threshold value for a specific DRF drug. An advanced clinical
decision support system in which prescription data are linked to laboratory data is
necessary for this25,26. Additionally, if serum creatinine values are not measured in
advance, a corollary order for serum creatinine measurement may further improve
adherence to monitoring protocols27. The extent to which corollary orders may
increase monitoring frequency and may prevent ADEs, however, is not known and
needs further research.
As exchange of information on medication between health care providers may
contribute to continuity of care, and laboratory values are often involved in the
monitoring of pharmacotherapy, these values should also be transferred at
discharge. Values measured during hospitalization, however, are only suitable
when they represent the patients’ health status at the moment of discharge. Of all
hospitalized patients, only 11-12% had at least one potassium, sodium or creatinine
measurement within the last 24 hours before discharge. For patients using a serum
potassium influencing drugs, 26-28% had at least one potassium measurement
within 24 hours before discharge. This percentage increased to 46-48% when the
last 48 hours before discharge was considered. Comparable percentages were found
for serum sodium measurements in patients using serum sodium decreasing
drugs. Percentages were considerably lower for creatinine measurements in
patients a DRF drug. Only 12% of the patients using a DRF drug had at least one
serum creatinine value within the last 24 hours before discharge. This percentage
increased to 25% when the last 48 hours before discharge was considered,
although, a serum creatinine measurement within 48 hours prior to discharge
was missing in 75% of the patients using a DRF drug. As the clinical relevance of
many DDIs can only be assessed when information on serum electrolytes and/or
serum creatinine is available, these percentages indicate that there is still room
for improvement. E.g. the DDI between RAS inhibitors and spironolactone is one
of the most frequently occurring DDIs in hospitals and patients using these drugs
concomitantly are especially at risk developing hyperkalemia when their kidney
function is decreased. Moreover, a decreased kidney function has also shown one of
the most frequently involved drug-related problems leading to hospital admission7.
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Transition of information on laboratory levels should therefore be part of the
medication reconciliation at discharge. The time frame, however, which must be
held to consider a laboratory value valid for transfer at discharge may be discussed.
Guidelines on transfer of medication data also lacks clarity on which laboratory
data can be considered valid28-30. As clinical pharmacists and clinical chemist
share responsibility with physicians in the transition of care, measurement of
laboratory markers should not be reserved to physicians only. When pharmacists,
for example, notice that a valid value is missing at discharge, they should have
the possibility to order or perform a measurement. Even point of care testing has
shown feasible in this31. Finally, when information on pharmacy and laboratory
data are transferred together with a monitoring advise at discharge, unnecessarily
repeated measurements may be prevented in primary care
Conclusion
Measurement of the three biomarkers potassium, sodium and creatinine, occurs
on average in 50% of the patients, but varies strongly across patient-, admission-
and medication-related factors. The use of electrolyte influencing medication
seems to influence laboratory monitoring, the use of a DRF drug did not seem to
influence laboratory monitoring of serum creatinine. In addition, the percentage
of patients in which these biomarkers are measured at the end of hospitalization
is low. As information on these laboratory markers is essential in medication
reconciliation at discharge, efforts should be given to improve the availability of
relevant laboratory monitoring data.
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References
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23 Glassman PA, Simon B, Belperio P, Lanto A. Improving recognition of drug interactions: benefits and
barriers to using automated drug alerts. Med Care 2002;40:1161-1171
24 Sijs H, Mulder A, van Gelder T, Aarts J, Berg M, Vulto A. Drug safety alert generation and overriding in
a large Dutch university medical centre. Pharmacoepidem Drug Saf 2009;18:941–947
25 Schiff GD, Klass D, Peterson J, Shah G, Bates DW. Linking Laboratory and Pharmacy Opportunities for
Reducing Errors and Improving Care. Arch Intern Med 2003;163:893-900
26 Seger AC, Jha AK, Bates DW. Adverse drug event detection in a community hospital utilizing
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29 The American Medical Association. The physician’s role in medication reconciliation: issues, strategies
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31 Geerts AF, De Koning FH, Vooght KM, Egberts TC, De Smet PA, Van Solinge WW. Feasibility of point-of-
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3.2Frequency of laboratory measurement
and hyperkalaemia in hospitalised
patients using serum potassium
concentration increasing drugs
Esther V. Uijtendaal
Jeannette E.F. Zwart-van Rijkom,
Wouter W. van Solinge
Toine C.G. Egberts
Eur J Clin Pharmacol 2011;67:933–940
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Abstract
Purpose
Although, drug-drug interactions (DDIs) between potassium increasing drugs
(PIDs) are known risk factors for developing hyperkalaemia, not much is known
about their risk and management strategies during hospitalisation. This study
examines the frequency of serum potassium measurements and hyperkalaemia in
hospitalised patients, based on the use of one or more PIDs, and the determinants
thereof.
Methods
Adult patients hospitalised in the University Medical Centre Utrecht between 2006-
2008 were included in this cross-sectional study. The frequency of serum potassium
measurement and of hyperkalaemia were compared between patients using only
one PID at a time (monotherapy group) and patients using two or more PIDs
concomitantly (interaction group). The determinants studied were renal failure,
diabetes mellitus, use of diuretics, type of DDI, start of the PIDs within the hospital
versus continued home medication, and medical speciality.
Results
Serum potassium was measured more frequently in the interaction group than in
the monotherapy group [67% vs. 56%; relative risk (RR) 1.19, 95% confidence interval
(CI) (1.14-1.24)] and the risk of hyperkalaemia was also increased in the interaction
group (9.9% vs. 5.9%, RR=1.7, 95% CI 1.3-2.1). The combination of potassium-sparing
diuretics plus a potassium supplement, start of the PID within the hospital and
hospitalisation in non-internal medicine departments was associated with higher
relative risk estimates for hyperkalaemia.
Conclusions
Among our patient cohort, even when physicians received a direct pop-up to
monitor serum potassium levels when prescribing two PIDs concomitantly, serum
potassium levels were not measured in 33% of patients and 10% of patients developed
hyperkalaemia. Improved management strategies and/or clinical decision-support
systems are needed to decrease the frequency of hyperkalaemia following DDIs.
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Introduction
Drug-drug interactions (DDI) often lead to preventable adverse drug events and
contribute to the burden of drug-related health damage1,2. The interaction between
serum potassium concentration increasing drugs (PIDs) is one of the most frequently
occurring DDI and has been reported to occur in 8-10% of all hospitalized and non-
hospitalized patients3,4.
Hyperkalaemia is a serious and potentially life threatening electrolyte disorder that
follows from an imbalance in potassium homeostasis5. The magnitude of the risk
of hyperkalaemia caused by the combined use of two or more PIDs was highlighted
after the findings of the Randomized Aldactone Evaluation Study (RALES) study
were implemented in regular patient care. In 1999, the RALES demonstrated
significantly improved outcomes for congestive heart failure patients treated with
spironolactone in addition to treatment with diuretics and angiotensin-converting
enzyme (ACE)-inhibitors6. Five years later, in 2004, it was found that this publication
had resulted in a clear increase in the prescription rates for spironolactone as
well as a marked increase in hyperkalaemia-associated morbidity and mortality7.
The risk does not only exist for renin-angiotensin system (RAS) inhibitors and
spironolactone, but also for other combinations of PIDs including potassium
supplements8-11.
Many studies have investigated the risk of developing hyperkalaemia from DDIs
between PID’s, but most studies have been performed in an outpatient setting,
upon hospital admission, in a specific patient group or with a specific combination
of PID’s. Moreover, in these studies, serum potassium levels were often measured
per protocol and were closely monitored. No published data are available about the
risk and management strategies of combining PIDs during hospitalisation where
the average stay is only a few days and the access to laboratory measurement is
more easy. Therefore, the objective of this study is to examine the frequency of
serum potassium measurement and of hyperkalaemia for hospitalised patients
using one or more PIDs, and determinants there of.
Methods
Setting This cross-sectional study was performed in the University Medical Centre
Utrecht (UMCU), a 1042-bed academic medical centre located in the centre of the
Netherlands. Between 2006-2008, approximately 30,000 clinical hospitalisations
took place annually. All medications for hospitalised patients are prescribed
using a computerized physician order entry (CPOE) system. In the Netherlands,
Chapter 3.2 | Frequency of laboratory measurement and hyperkalaemia in hospitalised patients using serum potassium concentration increasing drugs
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a working group of the Scientific Institute of Dutch Pharmacists developed and
maintains an evidence based and professional guideline for the management of
DDIs, the G-standard, which is described in detail elsewhere12. For clinical risk
management purposes, the G-standard is incorporated in the CPOE system13. All
drugs in the G-standard are coded according to the Anatomical Therapeutical
Chemical Classification (ATC) index. The ATC code system is an international
standard for drug utilization studies determined by a World Health Organisation
(WHO) International Working Group14. Each DDI in this guideline was assessed by
four core parameters: (1) evidence on the interaction; (2) clinical relevance of the
potential adverse reaction resulting from the interaction; (3) risk factors identifying
patient, medication or disease characteristics for which the interaction is of special
importance; (4) incidence of the adverse reaction. Based on the information in
the G-standard, the CPOE generates an alert when a combination of two or more
interacting drugs are prescribed concomitantly. In the case the concomitant
prescribing of two PIDs, the physician receives a direct pop-up that there is a risk of
hyperkalaemia and that the serum potassium levels should be closely monitored.
For research purposes all prescriptions are routinely exported in the Utrecht Patient
Oriented Database (UPOD), which is an infrastructure of relational databases
comprising data on patient demographics, hospital discharge diagnoses, medical
procedures, medication orders and laboratory tests for all patients treated at the
UMCU since 2004. This database is described in detail elsewhere15.
Study populationAll patients aged >18 years who were hospitalised in the UMCU in 2006-2008 for ≥24
h and who were prescribed at least one PID were enrolled in the study. If a patient
was admitted during the study period to the hospital more than once, only the
first admission was included. Patients admitted to the dialysis department were
excluded because patients at end-stage renal disease lack the renal compensation
mechanism for potassium homeostasis and large variations in serum potassium
levels may occur due to dialysis. Patients admitted to the intensive care (IC) units
were also excluded because a different CPOE system is used in the IC units and the
prescription data are not (yet) stored in the UPOD database.
ExposurePatients who were prescribed one PID (monotherapy group) were compared
to patients who were prescribed two or more PIDs concomitantly (interaction
group). In this study, PIDs were defined as: (1) RAS inhibitors, (2) potassium
sparing diuretics or (3) potassium supplements. The professional DDI clinical risk
management guideline has defined a combination of these drugs as a risk for
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developing hyperkalaemia. Other combinations of potentially PIDs mentioned in
literature such as nonsteroidal anti-inflammatory drugs, β-adrenergic blocking
drugs, heparin and trimethoprim are not considered to be sufficiently relevant by
the professional DDI clinical risk management guideline and were therefore not
included.
OutcomeTwo different outcome measures were studied: (1) frequency of serum potassium
measurement and (2) frequency of hyperkalaemia. The frequency of serum
potassium measurement was defined as the percentage of patients for which
a serum potassium level was measured at least once during the hospitalisation
period. The frequency of hyperkalaemia was defined as the percentage of patients
with any serum potassium measurement ≥ 5.5 mEq/L during the hospitalisation
period.
DeterminantsIn addition to age and gender, the following determinants were studied:
- Renal function: glomerular filtration rates were estimated (eGFR) with the
Modification of Diet in Renal Disease (MDRD) equation16,17 using the highest
serum creatinine level measured during the hospitalisation period. A
decreased renal function is a known risk factor for hyperkalaemia.
- Diabetes mellitus: a patient was considered to have diabetes mellitus when
one or more medication orders for any anti-diabetic medication had been
prescribed during hospitalisation. Anti-diabetic medication was defined
as medication with an ATC code “A10”. Diabetes mellitus is also a known
risk factor for hyperkalaemia.
- Diuretics: any use of diuretics. This was defined as the use of lis- or thiazide
diuretica or a combination of drugs containing thiazide diuretics during
hospitalisation (ATC C03C, C03A). This determinant was studied, because
both the use of thiazide- and lisdiuretics have been associated with a
protective effect against the development of hyperkalaemia18
- Type of combination: in the interaction group three combinations are
possible, namely (1) RAS-inhibitor + potassium-sparing diuretic, (2) RAS-
inhibitor + potassium supplement, (3) potassium sparing diuretic +
potassium supplement.
- Start at home or start during hospitalisation: in the monotherapy group
start at home was defined as the first PID prescribed in the CPOE system
within 24 h of arrival in the hospital. In the interaction group start at
home was defined as the second PID prescribed in the CPOE system within
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24 h of arrival in the hospital. PIDs prescribed >24 h after admission were
considered to be newly started within the hospital.
- Department: internal medicine departments versus non-internal medicine
departments. Internal medicine departments were defined as general
internal medicine departments, (namely Heart & Lung departments and
the departments of Geriatrics, Nephrology and Oncology). Non-internal
medicine departments were mainly the Surgery, Neurology, Psychiatry
and Gynaecology departments.
Data analysisRelative risks (RR) with corresponding 95% confidence intervals (CI) were calculated
for both outcome measures. The SPSS Package for Social Sciences software (SPSS)
version 15.1 for windows (SPSS., Chicago, IL) was used to analyse the collected data.
Results
A total of 9,441 patients using at least one PID were included in this study of whom
1,396 patients (14.7%) used two or more PIDs concomitantly at any time during
their hospitalisation (interaction group). On average, patients using ≥2 PIDs were
older, had a worse kidney function, and used anti-diabetic drugs and diuretics
more often than patients using only one PID (monotherapy group) (Table 1). Of
the patients in the monotherapy group, 35% started PID therapy during their stay
in the hospital; of those using > 2 PIDs, 50.4% were first prescribed the second PID
during their hospital stay.
Serum potassium was measured more frequently in the interaction group than
in the monotherapy group (67 vs. 56%, respectively RR 1.19, 95% CI 1.14-1.24), and
the risk of hyperkalaemia was also higher in the interaction group (9.9 vs. 5.9%,
respectively, RR 1.7, 95% CI 1.3-2.1) (Table 2). For patients whose potassium was
measured at least once, a median of 0.67 measurements per hospital admission
day were performed in the interaction group and 0.50 in the monotherapy group.
There were no no differences in the relative risk estimates between the monotherapy
and interaction groups when age, gender, renal function, diabetes and use of
diuretics were stratified. The absolute risk of hyperkalaemia, however, was higher in
patients with an eGFR ≤ 50 mL/min (p<0.01 for both monotherapy and interaction
groups).
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Table 1. Baseline characteristics for monotherapy group (users of 1 PID) versus interaction group (users of ≥2 PIDs)
Monotherapy:1 PIDN=8,045
Interaction:≥2 PIDsN=1,396
p valuea
Median length of stay, days (interquartile range)
6.18 (3.0-12.0) 8.9 (4.2-16.1) <0.001
Mean age (95% CI) 62.2 (61.8-62.5) 67.3 (66.6-68.1) <0.001
Male gender (%) 3,981 (49.5%) 751 (53.8%) <0.01
Renal functioneGFR ≥80 mL/mineGFR 50-80 mL/mineGFR ≤50 mL/minUnknown
1,479 (18.4%)1,876 (23.3%)1,150 (14.3%)3,540 (44.0%)
123 (8.8%)375 (26.9%)422 (30.2%)476 (34.1%)
<0.001
Diabetes mellitus 1,582 (19.7%) 340 (24.4%) <0.001
Diuretics 3,106 (38.6%) 1,097 (78.6%) <0.001
Drug type (first monotherapy)RAS inhibitorPotassium sparing diureticPotassium supplement
4,819 (59.9%)622 (7.7%)2,604 (32.4%)
n.a.
Interaction typeRAS-inhibitor + potassium sparing
diureticRAS-inhibitor + potassium
supplementPotassium sparing diuretic +
potassium supplement
707 (50.6%)
501 (35.9%)188 (13.5%)
n.a.
Start drug/interactionAt homeDuring hospitalisation
5,192 (64.5%)2,853 (35.5%)
692 (49.6%)704 (50.4%)
<0.001
DepartmentInternal medicine specialitiesNon-internal medical specialities
3,694 (45.9%)4,351 (54.1%)
977 (70.0%)419 (30.0%)
<0.001
Data are given as the number (n) of patients, with the percentage in parenthesis, unless indicated otherwise
PID, Potassium-increasing drug; CI, confidence interval; eGFR, estimated glomerular filtration rate; RAS,
renin-angiotensin system, n.a., not applicable
a) Chi square test (categorical variables) or t-test (continuous variables) as appropriate.
Comparison of the risk of hyperkalaemia for the different interaction types,
revealed that the risk was higher for the combination of a potassium supplement
plus a potassium-sparing diuretic (RR 3.0, 95% CI 2.0-4.4) than for the two other
combinations: a potassium-sparing diuretic plus RAS-inhibitor (RR 1.5, 95% CI 1.1-
2.1) and a potassium supplement plus RAS-inhibitor (RR 1.3, 95% CI 0.9-2.0).
Chapter 3.2 | Frequency of laboratory measurement and hyperkalaemia in hospitalised patients using serum potassium concentration increasing drugs
80
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
Tab
le 2
. Pe
rcen
tage
of
pat
ien
ts i
n w
hom
ser
um
pot
assi
um
was
mea
sure
d a
nd
per
cen
tage
of
pat
ien
ts w
ith
hyp
erka
laem
ia (
defi
ned
as
seru
m p
otas
siu
m l
evel
≥ 5
.5 m
Eql/
L) f
or u
sers
of
1 PI
D v
ersu
s u
sers
of
≥2 P
IDs
Pota
ssiu
m m
easu
red
Hyp
erk
alae
mia
Mon
oth
erap
y1
PID
(n=8
,045
)
Inte
ract
ion
≥PID
s (n
=1,3
96)
Rel
ativ
e ri
sk
(95%
CI)
Mon
oth
erap
y1
PID
(n
=4,5
20)
Inte
ract
ion
≥2 P
IDs
(n=9
30)
Rel
ativ
e ri
sk
(95%
CI)
Ove
rall
56.2
% (4
,520
/8,0
45)
66.6
% (9
30/1
,396
)1.
19 (1
.14-
1.24
)5.
9% (2
67/4
,520
)9.
9% (9
2/93
0)1.
68 (1
.34-
2.10
)
Age
18
-50
50-7
0 70
-80
>= 8
0
56.8
% (9
89/1
,742
)55
.3%
(1,9
21/3
,473
)54
.7%
(964
/1,7
61)
60.4
% (6
46/1
,069
)
70.8
% (1
14/1
61)
64.3
% (3
69/5
74)
68.5
% (2
63/3
84)
66.4
% (1
84/2
77)
1.25
(1.1
2-1.
39)
1.16
(1.0
9-1.
24)
1.25
(1.1
6-1.
36)
1.10
(1.0
-1.2
1)
6.2%
(61/
989)
5.6%
(107
/1,9
21)
5.2%
(50/
964)
7.6%
(49/
646)
12.3
% (1
4/11
4)8.
4% (3
1/36
9)9.
5% (2
5/26
3)12
.0%
(22/
184)
1.99
(1.1
5-3.
44)
1.51
(1.0
3-2.
21)
1.83
(1.1
6-2.
90)
1.58
(0.9
8-2.
54)
Gen
der
Mal
eFe
mal
e58
.4%
(2,3
26/3
,981
))54
.0%
(2,1
94/4
,064
)67
.4%
(506
/751
)65
.7%
(424
/645
)1.
15 (1
.09-
1.22
)1.
22 (1
.14-
1.30
)6.
3% (1
47/2
,326
)5.
5% (1
20/2
,194
)9.
5% (4
8/50
6)10
.4%
(44/
424)
1.50
(1.1
0-2.
05)
1.90
(1.3
7-2.
64)
Ren
al f
un
ctio
neG
FR ≥
80 m
L/m
ineG
FR 5
0-80
mL/
min
eG
FR ≤
50 m
L/m
inU
nkn
own
97.2
% (1
,438
/1,4
79)
97.5
% (1
,829
/1,8
76)
99.3
% (1
,142
/1,1
50)
3.1%
(111
/3,5
40)
99.2
% (1
22/1
23)
99.7
% (3
74/3
75)
99.8
% (4
21/4
22)
2.7%
(13/
476)
1.02
(1.0
0-1.
04)
1.02
(1.0
1-1.
03)
1.01
(1.0
0-1.
01)
0.87
(0.4
9-1.
54)
2.5%
(36/
1,43
8)2.
6% (4
8/1,
829)
15.8
% (1
81/1
,142
)1.
8% (2
/111
)
4.1%
(5/1
22)
4.0%
(15/
374)
16.9
% (7
1/42
1)7.
7% (1
/13)
1.64
(0.6
5-4.
10)
1.53
(0.8
7-2.
70)
1.06
(0.8
3-1.
37)
4.27
(0.4
2-43
.91)
Dia
bete
s N
on-d
iabe
tes
54.0
% (8
55/1
,582
)56
.7%
(3,6
65/6
,463
)69
.1%
(235
/340
)65
.8%
(695
/1,0
56)
1.28
(1.1
8-1.
39)
1.16
(1.1
1-1.
22)
10.1
% (8
6/85
5)4.
9% (1
81/3
,665
)12
.8%
(30/
235)
8.9%
(62/
695)
1.27
(0.8
6-1.
87)
1.81
(1.3
7-2.
38)
Diu
reti
cs
Thia
zid
eLo
opTh
iazi
de
+ lo
opN
o d
iure
tics
55.5
% (1
,723
/3,1
06)
45.0
% (5
97/1
328)
62.9
% (1
,036
/1,6
48)
69.2
% (9
0/13
0)56
.6%
(2,7
97/4
,939
)
66.5
% (7
30/1
,097
)63
.4%
(92/
145)
67.3
% (5
88/8
74)
64.1
% (5
0/78
)66
.9%
(200
/299
)
1.20
(1.1
4-1.
26)
1.41
(1.2
3-1.
62)
1.07
(1.0
1-1.
14)
0.93
(0.7
6-1.
13)
1.18
(1.0
9-1.
28)
7.7%
(133
/1,7
23)
1.5%
(9/5
97)
10.5
% (1
09/1
036)
16.7
% (1
5/90
)4.
8% (1
34/2
,797
)
10.8
% (7
9/73
0)4.
3% (4
/92)
11.2
% (6
6/58
8)18
.0%
(9/5
0)6.
5% (1
3/20
0)
1.40
(1.0
8-1.
83)
2.88
(0.9
1-9.
17)
1.07
(0.8
0-1.
42)
1.08
(0.5
1-2.
29)
1.36
(0.7
8-2.
35)
Inte
ract
ion
*T
ype1
Typ
e 2
Typ
e 3
56.2
% (4
,520
/8,0
45)
56.2
% (4
,520
/8,0
45)
56.2
% (4
,520
/8,0
45)
65.6
% (4
64/7
07)
65.9
% (3
30/5
01)
72.3
% (1
36/1
88)
1.17
(1.1
0-1.
24)
1.17
(1.1
0-1.
25)
1.29
(1.1
8-1.
41)
5.9%
(267
/4,5
20)
5.9%
(267
/4,5
20)
5.9%
(267
/4,5
20)
9.1%
(42/
464)
7.9%
(26/
330)
17.6
% (2
4/13
6)
1.53
(1.1
2-2.
10)
1.33
(0.9
1-1.
96)
2.99
(2.0
4-4.
37)
81
3.2
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
Star
t d
rug
or i
nte
ract
ion
At
hom
eD
uri
ng
hos
pit
alis
atio
n52
.1%
(2,7
06/5
,192
)63
.6%
(1,8
14/2
,853
)63
.0%
(436
/692
)70
.2%
(494
/704
)1.
21 (1
.14-
1.29
)1.
10 (1
.04-
1.17
)5.
9% (1
59/2
,706
)6.
0% (1
08/1
,814
)6.
7% (2
9/43
6)12
.8%
(63/
494)
1.13
(0.7
7-1.
66)
2.14
(1.6
0-2.
88)
Dep
artm
ent
Inte
rnal
med
icin
e sp
ecia
liti
esN
on-in
tern
al m
edic
al
spec
iali
ties
66.4
% (2
,453
/3,6
94)
47.5
% (2
,067
/4,3
51)
68.6
% (6
70/9
77)
62.1
% (2
60/4
19)
1.03
(0.9
8-1.
08)
1.31
(1.2
0-1.
42)
8.2%
(202
/2,4
53)
3.1%
(65/
2,06
3)10
.4%
(70/
670)
8.5%
(22/
260)
1.27
(0.9
8-1.
64)
2.69
(1.6
9-4.
29)
* T
ype
1, R
AS-
inh
ibit
or +
pot
assi
um
sp
arin
g d
iure
tic,
typ
e 2,
RA
S-in
hib
itor
+ p
otas
siu
m s
up
ple
men
t, t
ype
3, p
otas
siu
m s
par
ing
diu
reti
c +
pot
assi
um
su
pp
lem
ent
Chapter 3.2 | Frequency of laboratory measurement and hyperkalaemia in hospitalised patients using serum potassium concentration increasing drugs
82
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
The relative risk estimate was also found to be higher when the PIDs were first
prescribed during hospitalisation (RR 2.14, 95%CI 1.60-2.88) than when they were
already started at home (RR 1.13, 95% CI 0.77-1.66). Serum potassium levels were
measured more frequently when PIDs were started during hospitalisation in both
the interaction (63.0 vs 70.2%, p<0.01) and the monotherapy group (52.1 vs 63.6%,
p<0.01).
When stratifying for type of department, the relative risk estimate for developing
hyperkalaemia was found to be higher for patients hospitalised in non-internal
medicine departments (RR 2.7, 95% CI 1.7-4.3) than in those hospitalised in internal
medicine departments (RR 1.3, 95% CI 1.0-1.6). Serum potassium measurements
were made more frequently for patients of both groups hospitalised in internal
medicine departments, than in non-internal medicine departments (monotherapy:
66.4 vs. 47.5%, p<0.01; interaction groups 68.6 vs. 62.1%, p=0.018).
Discussion
In the patients of our hospital who participated in this study, serum potassium levels
were measured in only 56-67% of patients using one or more PIDs. Hyperkalaemia,
defined as a serum potassium concentration ≥ 5.5 mEq/L, occurred in 6-10% of
patients. The absolute risk for developing hyperkalaemia was highest for patients
with a eGFR≤ 50 and for patients with diabetes mellitus. However, the relative risk
estimates for comparing interaction to monotherapy were not increased for age,
gender, kidney function and diabetes. When using two or more PIDs concomitantly,
the risk of hyperkalaemia was elevated in patients using a combination of a
potassium-sparing diuretic with a potassium supplement, in patients starting their
PID therapy during their hospitalisation period and in patients hospitalised in non-
internal medicine departments.
Serum potassium measurements were made more frequently for patients in the
interaction group than for those in the monotherapy group. This may suggest
that the interaction alerts, namely, direct pop-ups advising physicians to monitor
potassium levels, were indeed (partly) effective. However, the higher frequency
may also be due to the longer hospital stay of patients in the interaction group
and differences in patients characteristics between the interaction group and
monotherapy group. To check our assumption that the higher frequency of serum
potassium measurement was indeed caused by a drug interaction signal, we
compared the interaction and the monotherapy groups for the measurement of
two ‘neutral markers’, that is leukocyte count and haemoglobin level. We found
that both markers were measured more frequently in the interaction group than
in the monotherapy group (66 versus 58%, p < 0.01 for haemoglobin level, and 63
83
3.2
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
versus 51%, p < 0.01 for leukocyte count, respectively, results not shown). This result
indicates that the higher frequency of potassium measurements in the interaction
group may not be (entirely) due to warnings about the drug-drug interaction, but
may also be explained by differences in patient characteristics between patients
using only one PID and patients using ≥2 PIDs. Apparently, more laboratory
measurements were required in the interaction group, which may suggest that
these patients were more severely ill or that they had more complex diseases or
more co-morbidity.
As shown in Table 2, serum creatinine and serum potassium were always almost
measured simultaneously. When renal function was known, serum potassium was
measured in 97-99% of patients, while if it was unknown serum potassium was
measured in only about 3% of patients. It is likely that the same holds true for the
measurement of, for example, sodium. As such, serum potassium measurements
may not always be carried out with the intention to monitor potassium, but simply
as an adjunct.
As expected, the frequency of hyperkalaemia was much higher in patients with
an estimated eGFR ≤ 50 mL/min than in patients with an estimated eGFR > 50 mL/
min. Patients with chronic renal failure are known to bet at risk of developing
hyperkalaemia19. It is also known that the use of a PID may contribute to the risk
in patients with renal failure20. In our patient cohort, however, this risk did not
further increase for patients using ≥ 2 PIDs (15.8 and 16.9% for the monotherapy
and interaction group, respectively). It is possible that monotherapy already
requires the maximum capacity of renal autoregulation. If so, this may not leave
much space for a further increase in risk when ≥ 2 PIDs are used concomitantly.
A comparable result was seen in diabetic patients: diabetes is a known risk factor
for hyperkalaemia10,19,21. In our study however, the introduction of a second PID did
not increase the risk, suggesting that risk factors for developing hyperkalaemia due
to a PID are not the same as those for developing hyperkalaemia after a DDI.
The frequency of hyperkalaemia in patients using a thiazide diuretic seemed to be
lower than in patients without diuretics, which is suggestive for a protective effect
in both monotherapy and interaction groups. This protective effect was not seen
for the use of loop diuretics or for the combination of a thiazide and a loop diuretic
in the co-medication group. The use of diuretics is a known protective factor for
the development of hyperkalaemia18. Weinberg reported a 59% reduction in the
probability of hyperkalaemia with the use of diuretics; however, thiazide diuretics,
not loop diuretics, were found to contribute the most to this effect18.
A comparison of the different combinations of PIDs, revealed that hospitalised
patients using potassium-sparing diuretics combined with potassium supplements
had the highest risk of hyperkalaemia. Of course, the risk of hyperkalaemia
Chapter 3.2 | Frequency of laboratory measurement and hyperkalaemia in hospitalised patients using serum potassium concentration increasing drugs
84
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
is obvious for this combination and some authors have even argued that an
alert is superfluous in this situation22. Indermitte et al. found that the use of
potassium supplements contributed most strongly to the velocity of developing
hyperkalaemia23, while others found that a combination of PID with RAS- inhibitors
was the strongest predictor to develop hyperkalaemia in hospitalised patients10.
Patients who were started on interacting PIDs during hospitalisation were at
higher risk developing hyperkalaemia than patients who were started taking
their interacting PID therapy at home. One possible explanation may be when
the interacting medication was started at home, prior to hospitalisation, serum
potassium levels may have been monitored before the patient was hospitalised.
Adjustments may have been made, such as by decreasing the dose of the PIDs or
adding medicine to the therapeutic regimen for decreasing the potassium serum
level24. It may also be possible that the PIDs caused only a temporary rise of serum
potassium levels. Moreover, one might expect that physicians are more inclined
to monitor the effects of newly added medication than of continued home-
medication25. Indeed, the serum potassium levels were measured somewhat more
often in the group of patients who started the PIDs in the hospital than in the
group of patients who continued their PID home-medication. This was seen in both
the monotherapy and interaction group (63.6 vs. 52.1%, p< 0.01 and 70.2 vs. 63.0%,
p< 0.01 respectively).
Finally, the relative risk estimate for developing hyperkalaemia was higher for
patients hospitalised in non-internal medicine departments (RR 2.7, 95% CI 1.7-
4.3) than in patients hospitalised in internal medicine departments (RR 1.3, 95%
CI 1.0-1.6). The frequency of serum potassium measurements was also increased for
patients with an interaction (47.5 vs. 62.1% RR 1.3, 95% CI 1.2-1.4), but the absolute
frequency of serum potassium measurements was remarkably low for patients
with monotherapy in the non-internal medicine departments. This may once again
suggest that warning signals were followed. The frequency of the measurement of
the two neutral markers, however, were also different (54.2 vs. 40.2%, p< 0.01 for
leucocytes and 62.3 vs. 50.3%, p< 0.01 for haemoglobin), which means that differences
in patient characteristics may have played a role as well. Moreover, the frequency
of serum potassium measurements in the monotherapy group of the non-internal
departments is relatively low, which may reflect the fact that physicians working
in non-internal medicine departments are less preoccupied with serum electrolyte
levels than their counterparts working in internal medicine departments. As
such, they may be less aware of the risk for developing of hyperkalaemia when
prescribing a PID. Therefore, special attention is needed for patients using PIDs at
non-internal medicine departments.
85
3.2
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
There are a number of limitations to our study. First, one potential limitation
may be that nonsteroidal anti-inflammatory drugs, β-adrenergic blocking drugs,
heparin and trimethoprim were not included in our definition of a PID. The
exclusion of these drugs was deliberate as they increase serum potassium levels to
a far lesser extent than RAS-inhibitors, potassium-sparing diuretics and potassium
supplements. As such, the former are not included in the G-standard as DDIs and
the CPOE does not generate a pop-up to warn for hyperkalaemia.
Second, the local hospital setting may limit the generalisability of the results.
Finally, as this is an observational study, patients were not randomised to either
the monotherapy or the interaction group and serum potassium levels were not
measured per protocol. The differences in baseline characteristics make it very
clear that the patients in the monotherapy and the interaction group are not the
same. In addition, serum potassium levels were not measured systematically in
all patients, but were known for only 56-66% of patients, meaning that there is
a testing bias26. As such, this study does not aim to answer the etiologic question
about the contribution of DDIs to the risk of developing hyperkalaemia. The
objective was to study the consequences of a frequently encountered DDI in a daily-
hospital practice and to gain insight in the determinants thereof. Consequently,
the adjusted relative risk estimates have consciously been omitted from Table 2, and
only uni-variate analysis were performed. The message is not so much that these
DDIs cause hyperkalaemia but that, in daily practice, patients in the interaction
group deserve extra attention and serum potassium measurements should be made
more frequently.
At our hospital, when two PIDs are prescribed concomitantly, the physicians
directly receive a pop-up that there is a risk of hyperkalaemia and that the serum
potassium levels should be monitored closely. However, we found that this advice
was not followed in 33% of patients. One of the reasons for this may be that the
warnings are not always appropriate. The DDI signal always appears when a DDI
occurs, even if the serum potassium level is actually low which may cause alert
fatigue27,28.
Another reason may be that the alert to monitor potassium serum levels only
occurs when the medication is started. There are no subsequent reminders, while
in fact it will take at least a few days before a rise in serum potassium levels can
actually be determined.
More advanced methods of clinical decision support may be suitable to improve
the management of this DDI and to reduce the frequency of hyperkalaemia. For
example, it may be worthwhile to generate a reminder every 3-4 days to monitor
serum potassium for all hospitalised patients using two or more PIDs. Another
possibility would be to link laboratory and medication data and to generate a
Chapter 3.2 | Frequency of laboratory measurement and hyperkalaemia in hospitalised patients using serum potassium concentration increasing drugs
86
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
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warning when serum potassium levels increase above a certain limit for patients
using one or more PIDs.
Acknowledgement
The authors are grateful to Hanneke den Breeijen for the data analysis and to their colleagues
at the Utrecht Institute for Pharmaceutical Sciences and the UMC Utrecht for their support in
establishing and maintaining UPOD.
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References
1 Leendertse AJ, Egberts AC, Stoker LJ, van den Bemt PM. Frequency of and risk factors for preventable
medication-related hospital admissions in the Netherlands. Arch Intern Med 2008;168:1890-1896
2 Juurlink DN, Mamdani M, Kopp A, Laupacis A, Redelmeier DA. Drug-drug interactions among elderly
patients hospitalized for drug toxicity. JAMA 2003;289:1652-1658
3 Buurma H, De Smet PA, Egberts AC. Clinical risk management in Dutch community pharmacies: the
case of drug-drug interactions. Drug Saf 2006;29:723-732
4 Zwart-van Rijkom JE, Uijtendaal EV, ten Berg MJ, van Solinge WW, Egberts AC. Frequency and nature
of drug-drug interactions in a Dutch university hospital. Br J Clin Pharmacol 2009;68:187-193
5 Perazella MA, Mahnensmith RL. Hyperkalaemia in the elderly: drugs exacerbate impaired potassium
homeostasis. J Gen Intern Med 1997;12:646-656
6 Pitt B, RALES study group. Effectiveness of spironolactone added to an angiotensin-converting enzyme
inhibitor and a loop diuretic for severe chronic congestive heart failure (the Randomized Aldactone
Evaluation Study [RALES]). Am J Cardiol 1996;78:902-907
7 Juurlink DN, Mamdani MM, Lee DS, Kopp A, Austin PC, Laupacis A, Redelmeier DA. Rates of
hyperkalaemia after publication of the Randomized Aldactone Evaluation Study. N Engl J Med
2004;351:543-551
8 Cruz CS, Cruz AA, Marcilio de Souza CA. Hyperkalaemia in congestive heart failure patients using
ACE inhibitors and spironolactone. Nephrol Dial Transplant 2003;18:1814-1819
9 Desai A. Hyperkalaemia associated with inhibitors of the renin-angiotensin-aldosterone system:
balancing risk and benefit. Circulation 2008;118:1609-1611
10 Henz S, Maeder MT, Huber S, Schmid M, Loher M, Fehr T. Influence of drugs and comorbidity on
serum potassium in 15 000 consecutive hospital admissions. Nephrol Dial Transplant 2008;23:3939-
3945
11 Johnson ES, Weinstein JR, Thorp ML, Platt RW, Petrik AF, Yang X, Anderson S, Smith DH. Predicting the
risk of hyperkalaemia in patients with chronic kidney disease starting lisinopril. Pharmacoepidemiol
Drug Saf 2010;19:266-272
12 van Roon EN, Flikweert S, le Comte M. Langendijk PN, Kwee-Zuiderwijk WJ, Smits P, Brouwers
JR. Clinical relevance of drug-drug interactions : a structured assessment procedure. Drug Saf
2005;28:1131-1139
13 Z-index (2008). Z-index .http://www.z-index.nl/ (Accessed at Jan, 2009)
14 WHO Collaborating Center for Drug Statistics Methodology (2009) Anatomical Therapeutic Chemical
(ATC) Classification Index. http://www.whocc.no/ (Accessed at Jan, 2009)
15 ten Berg MJ, Huisman A, van den Bemt PM, Schobben AF, Egberts AC, van Solinge WW. Linking
laboratory and medication data: new opportunities for pharmacoepidemiological research. Clin
Chem Lab Med 2007;45:13-19
16 Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate
glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in
Renal Disease Study Group. Ann Intern Med 1999;130:461-470
17 Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, Kusek JW, van Lente LF. Using
standardized serum creatinine values in the modification of diet in renal disease study equation for
estimating glomerular filtration rate. Ann Intern Med 2006;145:247-254
18 Weinberg JM, Appel LJ, Bakris G, Gassman JJ, Greene T, Kendrick CA, Wang X, Lash J, Lewis JA, Pogue
V, Thornley-Brown D, Philips RA. Risk of hyperkalaemia in nondiabetic patients with chronic kidney
disease receiving antihypertensive therapy. Arch Intern Med 2009;169:1587-1594
19 Einhorn LM, Zhan M, Hsu VD, Walker LD, Moen MF, Seliger SL, Weir MR, Fink JC. The frequency of
hyperkalaemia and its significance in chronic kidney disease. Arch Intern Med 2009;169:1156-1162
20 Bakris GL, Siomos M, Richardson D, Janssen I, Bolton WK, Hebert L, Agarwal R, Catanzaro D. ACE
inhibition or angiotensin receptor blockade: impact on potassium in renal failure. VAL-K Study
Group. Kidney Int 2000;58:2084-2092
21 Stevens MS, Dunlay RW. Hyperkalaemia in hospitalized patients. Int Urol Nephrol 2000;32:177-180
Chapter 3.2 | Frequency of laboratory measurement and hyperkalaemia in hospitalised patients using serum potassium concentration increasing drugs
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22 Ponce SP, Jennings AE, Madias NE, Harrington JT. Drug-induced hyperkalaemia. Medicine 1985;64:357-
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23 Indermitte J, Burkolter S, Drewe J, Krähenbühl S, Hersberger KE. Risk factors associated with a high
velocity of the development of hyperkalaemia in hospitalised patients. Drug Safety 2007; 30:71-80
24 Vereijken TL, Bellersen L, Groenewoud JM, Knubben L, Baltussen L, Kramers C. Risk calculation for
hyperkalaemia in heart failure patients. Neth J Med 2007;65:208-211
25 van der Sijs H, Mulder A, van GT, Aarts J, Berg M, Vulto A. Drug safety alert generation and overriding
in a large Dutch university medical centre. Pharmacoepidemiol Drug Saf 2009;18:941-947
26 Velthove KJ, Leufkens HG, Souverein PC, Schweizer RC, van Solinge WW. Testing bias in clinical
databases: methodological considerations. Emerg Themes Epidemiol 2010;7:2-9
27 van der Sijs H, Aarts J, Vulto A, Berg M Overriding of drug safety alerts in computerized physician
order entry. J Am Med Inform Assoc 2006;13:138-147
28 Weingart SN, Toth M, Sands DZ, Aronson MD, Davis RB, Phillips RS. Physicians’ decisions to override
computerized drug alerts in primary care. Arch Intern Med 2003;163:2625-2631
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3.3Serum potassium influencing
interacting drugs: Risk modifying
strategies also needed at
discontinuation
Esther V. Uijtendaal
Jeannette E.F. Zwart-van Rijkom
Wouter W. van Solinge
Toine C.G. Egberts
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Abstract
Background
Although the discontinuation of a medication may have important clinical
consequences, there is generally much less attention given to medication
surveillance when a prescription is stopped than when it is started.
Objective
To investigate the consequences on serum potassium levels of discontinuing a drug
that increases the serum potassium level (PID↑) and drug that decreases the serum
potassium level (PLD↓) in patients taking both
Methods
Patients who were hospitalized in the University Medical Centre Utrecht (UMCU) in
2004-2009 and were using both a PID↑ and a PLD↓ were included when one of these
drugs was discontinued during hospitalization. Serum potassium levels measured
before (potassium1) and after (potassium
2) discontinuation were compared in
patients who stopped the PLD↓ and in patients who stopped the PID↑.
Results
In the group of patients who stopped the PLD↓, (i.e., continued the PID↑),
mean serum potassium levels increased 0.19 mEq/L (range -0.9–1.8 mEq/L). After
discontinuation of the PLD↓, serum potassium levels increased in 91 (59%) patients.
Five patients (3.2%) developed hyperkalemia (potassium2>5.5 mEq/L).
In the group of patients who stopped the PID↑, (i.e. continued the PLD↓), mean
serum potassium levels decreased 0.40 mEq/L (range -2.6-0.7 mEq/L). Serum
potassium levels decreased in 61 (70%) patients after discontinuation of the PID↑.
Fifteen patients (17%) developed hypokalemia (potassium2 <3.5 mEq/L).
The results were not influenced by length of stay, age, sex, renal function and type
of medication discontinued.
Conclusions
The effects of serum potassium-influencing drugs need to be monitored not only
after starting the medication but also after stopping the medication. The same may
hold true for the effects of other drugs. Clinical risk management should therefore
focus on the risks not only when a new medication is prescribed, but also when
medication is stopped.
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Introduction
Medications can be an important source of unintended patient harm, which may be
caused by non-preventable adverse effects or medication errors.1 When considering
initiation of a new medication, it is important to determine the appropriateness
and fit of the patient’s drug therapy, including checking for potential drug-
related problems such as correct dosing, monitoring necessities and drug-drug
interactions. To support this evaluation, some hospitals have incorporated a
basic clinical decision support system in the computerized physician order entry
(CPOE) system. The discontinuation of a medication, however, may have important
consequences as well. A tapering scheme may be necessary, the loss of enzyme
induction or inhibition may require dosage adjustment of other medications, or
electrolyte disturbances may occur. Ignoring these consequences may lead to an
adverse drug withdrawal event.2 To our knowledge, there is hardly any literature
about medication surveillance when a prescription is stopped, even when the
prescription is involved in a drug-drug interaction.
Literature has now shown that the risk on hyperkalemia is one of the most
frequently occurring problems with drug therapy.3,4 After publication of the RALES
(Randomized Aldactone Evaluation Study) results in 1999, it has been increasingly
recognized that the use of serum potassium increasing drugs (PIDs↑) such as renin-
angiotensin system (RAS) inhibitors, potassium-sparing diuretics, or potassium
supplements may heighten a patient’s risk for developing hyperkalemia.5-7
Recommendations from the RALES report not only resulted in an increased
prescription rate of spironolactone, from 34 per 1000 patients to 149 per 1000
patients, but also led to an increased rate of hospitalizations for hyperkalemia from
2.4 to 11 per 1000 patients, and an associated rise in mortality from 0.3 to 2.0 per
1000 patients.6
On the other hand, the use of serum potassium lowering drugs (PLDs↓), such
as loop diuretics or thiazide diuretics, is associated with an increased risk for
developing hypokalemia.8 As with hyperkalemia, hypokalemia is a risk factor for
developing cardiac arrhythmias, a potentially lethal adverse effect.9 Monitoring of
serum potassium levels after the start of a PID↑ or PLD↓ is recommended in drug
information leaflets and guidelines; however, recommendations to monitor serum
potassium levels after discontinuation of these agents are lacking.10
Many patients use both a PID↑ and PLD↓, (eg, an RAS-inhibitor and a diuretic),
which is often seen in patients with heart failure or hypertension and diabetes or
renal failure. Although not recognized by all drug-drug interactions databases, the
combination of a PID↑ with a PLD↓ is an example of a pharmacologic interaction. As
such, extra attention is warranted when one of the drugs is discontinued. Therefore,
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the purpose of this study was to investigate the consequences of discontinuation of
one of these agents on serum potassium levels in patients using both a PID↑ and
a PLD↓.
Methods
Setting This retrospective follow-up study was performed in the University Medical Centre
Utrecht (UMCU), a 1042-bed academic medical centre located in the center of the
Netherlands. In 2004-2009, approximately 30,000 clinical hospitalizations took
place annually. All medications for hospitalized patients were prescribed using
a CPOE system with a basic clinical decision support system incorporated. For
research purposes all prescriptions are routinely exported in the Utrecht Patient
Oriented Database (UPOD). UPOD is an infrastructure of relational databases
comprising data on patient demographics, hospital discharge diagnoses, medical
procedures, medication orders and laboratory tests for all patients treated at the
UMCU since 2004, and has been described in detail elsewhere.11 The utilization of
UPOD is in accordance with guidance of the Institutional Review Board.
Study design and populationPatients were eligible for inclusion if they had been hospitalized in the UMCU in
2004-2009, used both a PID↑ and a PLD↓, and had one of these drugs discontinued
during the hospitalization period. Serum potassium levels before (potassium1)
and after (potassium2) discontinuation were compared in patients who stopped
the PLD↓, (i.e. continued the PID↑), and in patients who stopped the PID↑, (i.e.
continued the PLD↓) (Figure 1). Patients were included in our study if both the PID↑
and the PLD↓ were prescribed within 12 hours after hospital admission, assuming
that this represents continuation of medication in use before admission. If a patient
was admitted to the hospital more than once during the study period, only the first
admission was included in the analysis.
PIDs↑ were defined as RAS-inhibitors, potassium-sparing diuretics or potassium
supplements. PLDs↓ were defined as loop diuretics or thiazide diuretics. Potassium1
was defined as the serum potassium level measured closest to the moment
of discontinuation, within a window of 48 hours before and 12 hours after
discontinuation. The 12 hour period after discontinuation was chosen because the
potassium influencing drug is still present in the body for several hours, depending
on the elimination half-life after the last dosage. Potassium2 was defined as the
first serum potassium level measured within 48 -144 hours after the moment of
discontinuation (Figure 1).
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Patients were excluded if they were younger than 18 years, on hemodialysis,
using a potassium or phosphate binding drug or if the potassium-influencing
medication was changed (another PID↑/PLD↓ stopped or added) between the time
of discontinuation and the measurement of potassium2. Patients were also excluded
if potassium1 or
potassium
2 were not available.
Figure 1: Schematic representation of the inclusion criteria
Figure 1: Schematic representation of the inclusion criteria
OutcomeTwo primary outcome variables were studied: the change in serum potassium
levels after the moment of discontinuation (change in serum potassium levels =
potassium2
– potassium1) and the serum potassium level after discontinuation
(potassium2). Also, the percentage of patients developing either hypokalemia or
hyperkalemia was calculated. Hypokalemia was defined as potassium2 < 3.5 mEq/L
and hyperkalemia as potassium2 > 5.5 mEq/L.
Data analysisBoth outcome measures were computed separately for patients who discontinued
the use of the PLD↓ (i.e. continued the PID↑) and for patients who discontinued
the use of the PID↑ (i.e. continued the PLD↓). Univariate linear regression analysis
(continuous variables) and 1-way analysis of variance (categorical variables) were
used to study the influence of length of stay, age, sex, renal function and type of
medication discontinued. Estimated glomerular filtration rate was estimated using
the Modification of Diet in Renal Disease equation12 and the serum creatinine
level measured closest to time of discontinuation during the hospitalization. SPSS
Package for Social Sciences software version 17.0 for windows (SPSS Inc., Chicago,
IL) was used to analyze the collected data.
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Results
The selection profile of the study is depicted in Figure 2. Of the 420 patients who
met the inclusion and exclusion criteria, 156 (37%) were subsequently excluded
as no serum potassium was measured 48-144 hours after discontinuation of 1 of
both drugs. In the end, a total of 241 patients were included in the study, 154 of
whom discontinued the PLD↓ and 87 of whom discontinued the PID↑. Typical for
both groups was the relatively long length of stay (Table 1). Kidney function was
comparable in both groups.
Table 1: Baseline characteristics
Characteristic
PLD↓ Discontinued,
PID↑ Continued (n=154)
PID↑ Discontinued,
PLD↓ Continued (n=87)
Length of stay, days, median
(interquartile range)
13.2 (8.6-21.0) 16.7 (10.8-28.9)
Age, y, mean (95% CI) 70.4 (68.1-72.8) 67.8 (64.7-71.0)Male, n (%) 73 (47.4%) 48 (55.2%)eGFR, ml/min, a n (%)
≥80 77 (50.0%) 47 (51.5%)50-80 50 (32.5%) 24 (27.6%) ≤50 27 (17.5%) 16 (18.4%)
Type of drug stopped, n (%)potassium supplementb 31 (36%)potassium sparing diureticc 14 (16%)RAS-inhibitord 42 (48%)loop diuretice 108 (70%)thiazide diureticf 46 (30%)
Type of drug continued, n (%)potassium supplementb 16 (10%)potassium sparing diureticc 25 (16%)RAS-inhibitord 113 (73%)loop diuretice 75 (86%)thiazide diureticf 12 (14%)
eGFR: estimated glomerular filtration rate;
PID↑= potassium-increasing drug; PLD↓= potassium-lowering drug aGlomerular filtration rate estimated by the Modification of Diet in Renal Disease equation.b Oral en intravenous potassium chloride, potassium phosphate.c Spironolactone, triamterened Candesartan, captopril, enalapril, fosinopril, irbesartan, lisinopril, losartan, perindopril, quinapril,
ramipril, valsartan,e Bumetanide; furosemidef hydrochlorothiazide, chlorthalidone, indapamide
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Figure 2: In- and exclusion profile
292,594 admissions 126,444 patients
6,724 admissions 4,976 patients
1,072 admissions 1,006 patients
exclusion criteria (total): 640 admissions 586 patients
no follow up for more than 48hours after discontinuation 289 admissions 263 patientsdialysis or potassium/phospate resorber 80 admissions 74 patientschanges in potassium influencing drug use after discontinuation 271 admissions 249 patients
432 admissions 420 patients
potassium1or potassium2 not available a 187 admissions 179 patientspotassium1 not available 83 admissions 79 patientspotassium2 not available 164 admissions 156 patients
245 admissions 241 patientsa numbers do not add up as patients may have both serum potassium levels not available
both potassium1 and potassium2 available
admissions UMCU >18 year
using 1 PID and 1PLD upon admission
PID or PLD stops
evaluable admissions/patients
Figure 2: In- and exclusion profile
Patients who discontinued the PLD↓, and continued the PID↑Overall, in the group of patients who discontinued the PLD↓, mean change in
serum potassium levels was 0.19 mEq/L (range -0.9–1.8 mEq/L). After the time of
discontinuation, serum potassium levels increased in 91 (59%) of the patients
(Figure 3). Surprisingly, 51 (33%) patients who discontinued the PLD↓ showed a
decrease in serum potassium levels.
Five patients (3.2%) developed hyperkalemia after discontinuation. Five patients
(3.2%) developed hypokalemia after discontinuation, and another 11 (7.1%) patients
already had hypokalemia before discontinuation (Figure 4).
Patients who discontinued the PID↑, and continued the PLD↓Overall, in the group of patients who discontinued the PID↑, mean change in
serum potassium levels was -0.40 mEq/L (range -2.6-0.7 mEq/L). After the time of
discontinuation, serum potassium levels decreased in 61 (70%) patients (Figure 3).
Surprisingly, 20 (23%) patients who discontinued the PID↑ showed an increase in
serum potassium levels.
Fifteen (17%) patients developed hypokalemia after discontinuation. Two patients
(2.3%) developed hyperkalemia after discontinuation (Figure 4).
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In both groups of patients, results were not influenced by length of stay, age, sex,
renal function or type of medication discontinued.
Figure 3: Changes in serum potassium levels after discontinuation (potassium2-potassium1),
Figure 3: Changes in serum potassium levels after discontinuation (potassium
2-potassium
1),
Figure 4: Serum potassium levels after discontinuation (potassium2)
Figure 4: Serum potassium levels after discontinuation (potassium2)
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Discussion
Serum potassium levels decreased in 70% of the patients who discontinued the PID↑
but continued the PLD↓, and increased in 59% of the patients who discontinued
the PLD↓ but continued the PID↑. Of the patients who discontinued the PID↑, 17%
developed hypokalemia and 3.2% of those who discontinued the PLD↓ developed
hyperkalemia. The strongest effect was found in the group of patients who
discontinued the PID↑ and continued to take a PLD↓.
Notably, in 23-33% of patients, serum potassium levels shifted in the opposite
direction from the one expected. This may have been caused by minor changes
in serum potassium levels that fell within the range of normal physiological
fluctuations and the variation of the assay method (2%). The magnitude of the
shift in the expected direction was larger than the shift in the opposite direction.
In the group of patients who discontinued the PID↑, the mean change in serum
potassium levels in the patients whose serum potassium levels decreased was
-0.66 mEq/L, while the mean change in serum potassium levels in patients whose
serum potassium levels increased was only 0.30 mEq/L. In the group of patients
who discontinued the PLD↓, the mean change in serum potassium levels in
patients whose levels increased was 0.51 mEq/L, while the mean change in serum
potassium levels in patients whose levels decreased was only -0.33 mEq/L. However,
as the variation of the assay was only 2%, this is not a complete explanation for
the data. It is likely that pathophysiologic factors such as morbidity (eg, nausea,
diarrhea, organ failure), nutritional status and concomitant (eg, laxatives) may
have influenced the results. Lack of data on these issues is a limitation of this study.
Another limitation is the fact that strict inclusion and exclusion criteria have been
applied. This resulted in a selected group of patients with an average length of
stay within the hospital of 19.3 days (median 14.9 days). Between 2004 and 2009,
the average length of stay within the UMCU was only 7.8 days, meaning that the
population in our study probably was more severely ill than the average hospital
population. Furthermore, this study included patients using a combination of a
PID↑ and PLD↓. Results may therefore not be extrapolated to patients only one of
these drugs. This may compromise the generalizability of the results.
Finally, a limitation of this study is that the doses of the PIDs↑ and PLDs↓ were not
accounted for, while it is known that these drugs have a dose-dependent effect on
the serum potassium levels. However, it was not our objective to determine to what
extent the discontinuation of drugs that interfere with potassium homeostasis
contributes to changes in serum potassium levels. Rather, it was our intention
to create awareness that attention is needed not only when a new medication
is started, but also when a medication is discontinued. We have demonstrated
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this need with regard to discontinuation of drugs that interfere with potassium
homeostasis, but the need may hold true for many others drugs, such as those
involved in pharmacokinetic interactions.13,14 From the in- and exclusion profile of
our study it can be deducted that serum potassium levels were not monitored after
discontinuation of one of the 2 types of serum potassium-influencing drugs in 37%
of the patients.
In conclusion, this study demonstrated that the effects of serum potassium-
influencing drugs need to be monitored, not only after starting the medication,
but also after stopping it. The same may hold true for the effects of other drugs.
Clinical risk management should therefore be focused not only on risks when a
new medication is prescribed, but also on those that may occur when medication
is stopped.
Acknowledgment
The authors are grateful to Hanneke den Breeijen for the data analysis and to their colleagues
at the Utrecht Institute for Pharmaceutical Sciences and the UMC Utrecht for their support in
establishing and maintaining UPOD.
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References
1. Leendertse AJ, Egberts AC, Stoker LJ, et al. Frequency of and risk factors for preventable medication-
related hospital admissions in the Netherlands. Arch Intern Med 2008;168:1890-6.
2. Bain KT, Holmes HM, Beers MH, et al. Discontinuing medications: a novel approach for revising the
prescribing stage of the medication-use process. J Am Geriatr Soc 2008;56:1946-1952
3. Zwart-van Rijkom JE, Uijtendaal EV, ten Berg MJ, et al. Frequency and nature of drug-drug interactions
in a Dutch university hospital. Br J Clin Pharmacol 2009;68:187-193
4. Buurma H, De Smet PA, Egberts AC. Clinical risk management in Dutch community pharmacies: the
case of drug-drug interactions. Drug Saf 2006;29:723-732
5. Henz S, Maeder MT, Huber S, et al. Influence of drugs and comorbidity on serum potassium in 15 000
consecutive hospital admissions. Nephrol Dial Transplant 2008;23:3939-45.
6. Juurlink DN, Mamdani MM, Lee DS, et al. Rates of hyperkalemia after publication of the Randomized
Aldactone Evaluation Study. N Engl J Med 2004;351:543-551
7. Pitt B, Zannad F, Remme WJ, et al. The effect of spironolactone on morbidity and mortality in patients
with severe heart failure. Randomized Aldactone Evaluation Study Investigators. N Engl J Med
1999;341:709-717
8. Liamis G, Milionis H, Elisaf M. Blood pressure drug therapy and electrolyte disturbances. Int J Clin
Pract 2008;62:1572-1580
9. Cohen HW, Madhavan S, Alderman MH. High and low serum potassium associated with cardiovascular
events in diuretic-treated patients. J Hypertens 2001;19:1315-1323
10. Bootsma JE, Warle-van Herwaarden MF, Verbeek AL, et al. Adherence to biochemical monitoring
recommendations in patients starting with Renin Angiotensin system inhibitors: a retrospective
cohort study in the Netherlands. Drug Saf 2011;34:605-614
11. ten Berg MJ, Huisman A, van den Bemt PM, et al. Linking laboratory and medication data: new
opportunities for pharmacoepidemiological research. Clin Chem Lab Med 2007;45:13-19
12. Levey AS, Coresh J, Greene T, et al. Using standardized serum creatinine values in the modification
of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med
2006;145:247-254
13. Shullo MA, Schonder K, Teuteberg JJ. Elevated tacrolimus levels associated with intravenous
azithromycin and ceftriaxone: a case report. Transplant Proc 2010;42:1870-1872
14. Castberg I, Helle J, Aamo TO. Prolonged pharmacokinetic drug interaction between terbinafine and
amitriptyline. Ther Drug Monit 2005;27:680-682
3.4Influence of a strict glucose protocol
on serum potassium levels in intensive
care patients
Esther V. Uijtendaal
Jeannette E.F. Zwart-van Rijkom
Dylan W. de Lange
Arief Lalmohamed
Wouter W. van Solinge
Toine C.G. Egberts
Chapter 3.4 | Influence of a strict glucose protocol on serum potassium levels in intensive care patients
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Abstract
Objective
Tight glucose control therapy (TGC) has been implemented to control hyperglycemia
in intensive care (ICU) patients. TGC, however, may also influence serum potassium
levels. We therefore investigated the influence of TGC on both serum glucose and
serum potassium levels and associated mortality.
Design
Retrospective analysis
Setting
Mixed ICU of a tertiary hospital
Patients
Patients admitted to the ICU for 24 hours or more and with at least three serum
glucose and serum potassium levels between 1999-2001 (conventional period),
2002-2006 (implementation period) or 2007-2009 (TGC period).
Measurements and main results
Means and standard deviations (SDs) of serum glucose and serum potassium
levels, and rate of hypoglycemia (≤2.2mmol/L) and hypokalemia (≤3mmol/L) levels,
were compared between the TGC and conventional period using time series
analysis. Although mean serum glucose levels dropped 2.1 mmol/L (95%CI =-1.8
to -2.3mmol/L, p<0.002), mean serum potassium levels did not change (absolute
increase 0.02mmol/L; 95%CI = -0.06 to 0.09mmol/L, p=0.64). The rate of hypoglycemia
increased with 5.9% (95%CI=-3.0 to -8.9, p<0.002), but the rate of hypokalemia stayed
equal (absolute reduction 4.8%; 95%CI = -11.1% to 1.5%, p=0.13). The SD of serum
glucose levels within a patient did not change, while the SD of serum potassium
levels even decreased 0.04 mmol/L (95%CI = -0.01 to -0.07, p=0.01).
Mean serum glucose levels, mean serum potassium levels and SDs of both serum
glucose and serum potassium levels were all independently associated with ICU
mortality. Highest mortality rates were seen at both the lowest and highest mean
values (U/J-shaped association) and mortality rates increased with increasing
variability (SDs) for both serum glucose and serum potassium levels.
Conclusion
Our study shows that a TGC was not associated with an increased risk of serum
potassium related events. Low and high mean values and high variability of both
serum glucose and serum potassium levels should be prevented.
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Introduction
Hyperglycemia in response to critical illness has been associated with increased
morbidity and mortality1. The mechanism suggested for this increased risk is that
elevated glucose concentrations increase the level of several toxic intracellular
derivatives that are generated as by-products of the glycolytic pathway2,3. Especially
during severe illness, the expression of insulin-independent glucose transporters
on the membranes of several cell types is up-regulated, which may allow high
circulating glucose levels to overload and damage these cells4-7. Based upon this line
of reasoning, vd Berge et al investigated in surgical intensive care unit (ICU) patients
whether a tight glycemic control (TGC) protocol (target serum glucose level 4.4-6.1
mmol/L (80-110 mg/dL)) would reduce mortality8. In this landmark clinical trial, an
absolute mortality reduction of 3.4% was found. This led to the implementation of
TGC protocols on many ICUs worldwide.
Subsequent studies performed in medical ICU patients, however, failed to
reproduce the reduction in mortality9-11. The NICE-sugar investigators even reported
an increased mortality when TGC was compared to conventional treatment12. Since
then, TGC has become a major area of debate among medical specialties involved in
the care of acutely ill patients. Several hypotheses have been postulated to explain
the contradictory results.
First, the characteristics of study populations differed between the different clinical
trials that were carried out. TGC seems to benefit surgical ICU patients more than
medical ICU patients13. In the setting where hyperglycemia is triggered by surgery,
the delay between onset of hyperglycemia and the start of glycemic control is short.
Medical ICU patients may have suffered from chronic diseases and hyperglycemia
before ICU admission and time from the onset of symptoms to the start of TGC
may be longer. As such the TGC protocol may be more beneficial in surgical
ICU patients. Second, a TGC protocol can be expected to increase the incidence
of hypoglycemia, which in itself gives a higher risk on mortality. An increased
incidence of hypoglycemia (serum glucose levels ≤ 2.2 mmol/L (40 mg/dL)) was
seen in all studies that applied a TGC protocol14. The highest rates of hypoglycemia
were generally found in studies that applied the lowest serum glucose targets,
varying, however, with the complexity of the TGC protocols15. An increased rate of
hypoglycemia may also reflect a large fluctuation of serum glucose levels implying
that these vulnerable patients may also be exposed to high glucose levels, although
mean serum glucose levels are relatively low16. In vitro studies showed that a large
variability in glucose levels may enhance cell apoptosis17,18 and some clinical studies
confirmed an independent association between large variations in serum glucose
levels and mortality 19-22. Third, in addition to serum glucose, serum potassium
Chapter 3.4 | Influence of a strict glucose protocol on serum potassium levels in intensive care patients
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may also be a significant factor influencing patients’ outcomes. It is well known
that insulin induces a shift of potassium from the extracellular to the intracellular
compartment. As a result, the implementation of a TGC protocol may also induce
larger variations in serum potassium levels and more frequently hypokalemia
and associated complications such as arrhythmia. In the NICE-sugar study there
was an increased risk of cardiovascular deaths in the TGC-group, but its cause
remained unclear12. Some studies reported an increased incidence of hypokalemia,
and emphasized the necessity of combined glucose and potassium monitoring to
prevent hypokalemia-induced arrhythmia23,24. Other studies did not mention the
measurement of serum potassium levels as a part of the TGC protocol.
Because of the ongoing discussion, we investigated within our own academic ICU
what the influence was of the implementation of a TGC protocol on the rate of
hypoglycemia and hypokalemia and the variation in serum glucose and potassium
levels. Furthermore, the association between mortality and both serum glucose
and potassium levels and variability was studied during the TGC period.
Methods
Setting and study populationThis retrospective observational study was carried out at the 32-bed Intensive Care
Unit (ICU) of the University Medical Centre Utrecht (UMCU), which is a tertiary care
teaching hospital in the Netherlands. Surgical, internal, neurological and cardio-
thoracic ICU patients are treated on this ICU.
A TGC policy was consecutively implemented on all specialties of the ICU during
the years 2002-2006. These years were defined as the implementation period. The
years before implementation, 1999-2001, were defined as the conventional period
and the years 2007-2009 were defined as the TGC period.
During the conventional period, conventional serum glucose control was applied
to all patients, which implied that insulin therapy was started if the serum glucose
levels were above 12 mmol/L (215 mg/dL) and that serum glucose levels were
maintained between 10-11 mmol/L (180 and 200 mg/dL). During the TGC period,
a TGC protocol was followed, aiming to maintain serum glucose levels between
4.4-6.1 mmol/L (80-110 mg/dL). In the conventional period, the policy was to start
potassium supplementation if serum potassium dropped below 4 mmol/L; this
policy did not change in the TGC period.
Adult patients (≥18 years) who were admitted to the ICU for more than 24 hours in
the years 1999-2009 were eligible for inclusion. To be able to calculate serum glucose
and potassium variability, patients with less than three valid serum glucose or
less than three valid serum potassium measurements during ICU admission were
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excluded. Moreover, if a patient was admitted to the ICU more than once, only the
first admission was included (Figure 1).
For research purposes all laboratory values are routinely exported to the Utrecht
Patient Oriented Database (UPOD). UPOD is an infrastructure of relational databases
comprising data on patient demographics, hospital discharge diagnoses, medical
procedures, medication orders and laboratory tests for all patients treated at the
UMCU since 2004, and has been described in detail elsewhere25. As only routinely
documented patient data were used in this study, the ethics board waived the need
for informed consent.
Figure 1: Inclusion of study patients
Figure 1: Inclusion of study patients
OutcomesFour outcome parameters were determined for each individual patient:
1) mean serum glucose and potassium levels: means of serum glucose and serum
potassium levels were calculated for each patient during ICU stay
2) hypoglycemia and hypokalemia: proportion of patients with at least one
hypoglycemic episode and proportion of patients with at least one hypokalemic
episode. Hypoglycemia was defined as a serum glucose measurement ≤ 2.2 mmol/L
and hypokalemia was defined as a serum potassium measurement ≤ 3.0 mmol/L
3) variability of serum glucose and serum potassium levels: mean standard
deviation (SD) of individually calculated standard deviations of serum glucose and
serum potassium.
4) in the TGC period: ICU mortality.
Chapter 3.4 | Influence of a strict glucose protocol on serum potassium levels in intensive care patients
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Data analysisFirst, interrupted time series analysis was performed to compare the TGC period
with the conventional period. Patient characteristics were compared using t-test
(mean values), the chi-square test (proportions) and Mann-Whitney U test (median
values).
Segmented regression analysis was used to estimate changes in outcomes that
occurred after the intervention controlling for pre-intervention trends26. The
intervention was the implementation of a TGC protocol between January 2002 and
January 2007. Outcome values during the implementation period were modelled
as a separate segment. Each time point in the time series data represents a three
month period resulting in the recommended minimum of 12 time points before
and 12 time points after the intervention26. The following linear regression model
was used:
Yt = β
0 + β
1* time
1 + β
2 * time
2 + β
3 * time
3 (1)
(Yt = mean serum glucose level at time = t; β
0= intercept = mean serum glucose
level at time = 0; β1= baseline trend = change in serum glucose level during the
conventional period; time1 = time after start of the conventional period(Jan 1, 1999);
β2= trend change during the implementation period, adjusted for the baseline
trend; time2 = time after start of the implementation period (Jan 1, 2002); β
3 = trend
change during the TGC Period, adjusted for the baseline trend and trend change
during the implementation period; time3 = time after start of the TGC Period (Jan
1, 2007). The change in mean serum glucose values between the end of the control
period and the start of the TGC period are calculated from:
Change = (β1 +
β2) * duration of implementation (2)
The same regression model was used for the proportion of patients with a
hypoglycemia, the mean SDs for serum glucose levels, mean serum potassium
levels, proportion of patients with hypokalemia and mean SDs for serum potassium
levels.
Second, the relationship between mean serum glucose levels, mean serum
potassium levels and ICU mortality was studied. This relationship was also studied
for the SD of serum glucose, SD of serum potassium levels and mortality. For this
analysis, only patients who either survived or who died on the ICU during the TGC
period were included. Logistic regression analysis was performed to determine the
association between mean glucose levels and risk on ICU mortality. Adjustments
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were made for mean serum potassium levels, age, gender and length of ICU stay.
A similar procedure was performed to determine the association between mean
serum potassium levels (with adjustments for mean serum glucose levels, age,
gender and length of stay), SD of serum glucose levels and SD of serum potassium
levels and ICU mortality. Moreover, to study the relationship between the combined
mean serum glucose and mean serum potassium levels on ICU mortality, patients
were divided in eight equal subgroups for their mean serum glucose and eight
equal subgroups for their mean serum potassium values. This resulted in 64
possible mean glucose-mean potassium subgroup-combinations. ICU mortality was
calculated for each combination.
Time series analysis was performed using SAS 9.2, SAS Institute Inc, Cary, USA using
PROC REG procedure. All other data analyses were performed using SPSS release 20
(SPSS, Inc., Chicago, Illinois).
Results
During the study period, a total of 21,662 admissions were available for selection.
After application of the inclusion criteria, 2538 patients could be included in the
conventional period, 4925 in the implementation period and 2730 in the TGC
Period (Figure 1). Baseline characteristics of the patients are shown per period in
Table 1. Compared to patients included in the conventional period, patients in the
TGC period were older (59.6 vs 57.6 years), mean serum creatinine levels were lower
(112 vs 121 mmol/L), the length ICU and hospital stay was shorter (3.1 vs 4.5 and 16
vs 18 days respectively) and the number of patients admitted due to an emergency
was lower (45 vs 54%). ICU mortality was also lower in the TGC period (14.4 vs 18.6%).
The median number of serum glucose measurements increased approximately 3.4
times (p<0.002) after implementation of the TGC protocol. The median number of
serum potassium measurements per patient, decreased from 10 measurements to
9 (p<0.002).
Chapter 3.4 | Influence of a strict glucose protocol on serum potassium levels in intensive care patients
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le 1
: B
asel
ine
char
acte
rist
ics
Con
ven
tion
al
per
iod
1999
-200
0-20
01(n
=253
8)
Imp
lem
enta
tion
Pe
riod
20
02-2
003-
2004
-200
5-20
06
(n=4
925)
Tigh
t G
luco
se
Con
trol
Per
iod
20
07-2
008-
2009
(n=2
730)
p
Mal
e ge
nd
er (%
)60
.363
.160
.70.
782a
Age
(yrs
) mea
n (9
5% C
I)57
.6 (5
7.0-
58.3
)59
.9 (5
9.4-
60.3
)59
.6 (5
9.0-
60.2
)<0
.002
b
Len
gth
of
stay
IC
U (d
ays)
med
ian
(IQ
R)
4.5
(2.0
-11.
3)2.
8 (1
.1-8
.2)
3.1
(1.8
-8.3
)<0
.002
c
Len
gth
of
stay
hos
pit
al (d
ays)
med
ian
(IQ
R)
18.0
(9.0
-38.
7)14
.0 (7
.9-3
0.5)
16.0
(8.1
-32.
8)<0
.002
c
Hos
pit
alis
atio
n d
ue
to e
mer
gen
cy (%
)54
.041
.845
.1<0
.002
a
Die
d o
n I
CU
n (%
)47
2 (1
8.6)
659
(13.
4)39
3 (1
4.4)
<0.0
02a
Die
d i
n h
osp
ital
(%)
135
(5.3
)27
5 (5
.6)
147
(5.4
)0.
741a
Nu
mbe
r of
glu
cose
mea
sure
men
ts, t
otal
4354
218
3393
1846
59n
.a.
Nu
mbe
r of
glu
cose
mea
sure
men
ts p
er p
atie
nt,
med
ian
(IQ
R)
8 (5
-18)
12 (6
-37)
27 (1
3-76
)<0
.002
a
Nu
mbe
r of
pot
assi
um
mea
sure
men
ts, t
otal
4495
477
407
4895
9n
.a.
Nu
mbe
r of
pot
assi
um
mea
sure
men
ts p
er p
atie
nt,
med
ian
(IQ
R)
10 (6
-21)
9 (6
-17)
9 (6
-20)
<0.0
02a
pH
, mea
n (9
5% C
I)7.
42 (7
.38-
7.45
)†7.
39 (7
.39-
7.39
)†††
††7.
39 (7
.39-
7.39
)††
0.12
5b
Seru
m c
reat
inin
e (m
mol
/L) m
ean
(95%
CI)
120.
6 (1
15.7
-125
.4)†
††10
8.5
(105
.6-1
11.4
) †††
†††
111.
5 (1
07.5
-115
.5)†
†††
<0.0
02b
p-v
alu
es r
epre
sen
t d
iffe
ren
ces
betw
een
Con
ven
tion
al p
erio
d a
nd
TG
C p
erio
da =
Ch
i sq
uar
e te
stb
= in
dep
end
ent
sam
ple
s t-
test
c = M
ann
Wh
itn
ey U
tes
t95
% C
I =
95%
con
fid
ence
in
terv
al; I
QR
= i
nte
rqu
arti
le r
ange
n.a
. = n
ot a
pp
lica
ble
† 0.
6% m
issi
ng
†† 8
.6%
mis
sin
g††
† 0.
1% m
issi
ng
††††
0.7
% m
issi
ng
††††
† 0.
6% m
issi
ng
††††
†† 0
.5%
mis
sin
g
111
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Comparison of the TGC period with the conventional periodThe mean serum glucose levels, proportion of patients with a hypoglycemia and SD
values for serum glucose levels over time are displayed in the figures 2a, 2b and 2c.
After implementation of the TGC protocol, mean serum glucose levels dropped from
8.7 to 6.6 mmol/L, an absolute reduction of 2.1 mmol/L (95%CI = -1.8 to -2.3, p<0.002,
Figure 2a). The percentage of patients with a hypoglycemia increased from 1.7% to
7.6%, an absolute increase of 5.9% (95%CI = 3.0% to 8.9%, p<0.002). The variability
of serum glucose levels within a patient did not change after implementation of
the TGC protocol (absolute difference -0.11 mmol/L; 95%CI = -0.26 to -0.03, p=0.13).
Similarly to serum glucose, values over time for serum potassium are displayed
in figure 2d, e, and f. Mean serum potassium levels stayed the same (absolute
increase 0.02 mmol/L, 95%CI = -0.06 to 0.09 mmol/L, p=0.64). The percentage of
patients with a hypokalemia dropped from 23.8 to 19.0%, an absolute reduction of
4.8% (95%CI = -11.1% to 1.5%, p=0.13), however this reduction was not statistically
significant. The variability of serum potassium levels within a patient decreased
after implementation of the TGC; SD values decreased by 0.04 mmol/L (95%CI =
-0.01 to -0.07, p=0.01).
Relationship between serum glucose and potassium levels and ICU mortality during TGCDuring TGC, overall ICU mortality was 15.2%. Multivariate logistic regression
analysis revealed a U-shaped relationship between mean serum glucose levels and
mortality (figure 3a). A J-shaped relationship was found between serum potassium
level and mortality (figure 3b). Both trends were statistically significant and
independent of age, gender, length of ICU stay, and mean serum potassium or
mean serum glucose levels respectively.
Mortality increased with increasing variability (SDs) of both serum glucose and
serum potassium levels (figure 3b and 3d). These trends were also independent of
age, gender, length of stay and SD for serum potassium or SD for serum glucose
levels respectively.
The percentages of patients who died at the ICU for each combined mean serum
glucose and mean serum potassium level subgroup is displayed in Table 2b. When
stratified for both mean serum glucose and mean serum potassium levels, extremes
in mortalities were seen in both the combined highest mean serum potassium
and lowest serum glucose subgroup (45.2%) and the combined highest mean
serum glucose and lowest mean serum potassium subgroup (40.5%). ICU mortality
increased with increasing variability of both serum glucose and serum potassium
levels with extremes at the highest SD’s of the combination (table 2b).
Chapter 3.4 | Influence of a strict glucose protocol on serum potassium levels in intensive care patients
112
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R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
Figure 2a: Mean serum glucose levels during the study period y=8.720875 - 0.00002*time
1 - 0.0011*time
2 + 0.001605*time
3
Figure 2b: Percentage of patients with a period of hypoglycemia (≤2.2 mmol/L) during the study period
y=2.268667 - 0.00056*time1 + 0.003807*time
2 - 0.00659*time
3
Figure 2c: Means of individual standard deviations for serum glucose levels during the study period
y=2.085461 - 0.00019*time1 + 0.000133*time
2 + 1.342228*time
3
113
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R2
R3
R4
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R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
Figure 2d: Mean serum potassium levels during the study period y=4.117654 - 0.0000025*time
1 + 0.0000114*time
2 + 0.0000374*time
3
Figure 2e: Percentage of patients with a period of hypokalemia (≤3 mmol/L) during the study period
y = 24.14247 - 0.00029 * time1 - 0.00236 * time
2 - 0.004526 * time
3
Figure 2f: Means of individual standard deviations for serum potassium levels during the study period
y=0.493879 - 0.000021*time1 - 0.000033*time
2 - 0.0000311*time
3
Chapter 3.4 | Influence of a strict glucose protocol on serum potassium levels in intensive care patients
114
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R4
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R7
R8
R9
R10
R11
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R13
R14
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R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
Figure 3a: Relationship between mean serum glucose levels and adjusted* ODDs ratio on ICU mortality.
* adjusted for age, gender, length of ICU stay and mean serum potassium levels
Figure 3b: Relationship between mean serum potassium levels and adjusted* ODDs ratio on ICU mortality.
* adjusted for age, gender, length of ICU stay and mean serum glucose levels.
115
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R3
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R7
R8
R9
R10
R11
R12
R13
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R16
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R18
R19
R20
R21
R22
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R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
Figure 3c: Relationship between SD for serum glucose levels and adjusted* ODDs ratio on ICU mortality.
* adjusted for age, gender, length of ICU stay and SD for serum potassium levels
Figure 3d: Relationship between SD for serum potassium levels and adjusted* ODDs ratio on ICU mortality.
* adjusted for age, gender, length of ICU stay and SD for serum glucose levels
116
R1
R2
R3
R4
R5
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R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
Tab
le 2
a:
ICU
mor
tali
ty (%
an
d n
) per
str
atu
m o
f m
ean
ser
um
glu
cose
an
d m
ean
ser
um
pot
assi
um
lev
els
du
rin
g th
e TG
C p
erio
d
aver
age
1 (2
.78-
3.77
)2
(3.7
8-3.
91)
3 (3
.92-
4.00
)4
(4.0
1-4.
10)
5 (4
.11-
4.21
)6
(4.2
2-4.
34)
7 (4
.35-
4.57
)8
(4.5
8-6.
04)
pot
assi
um
n=3
17n
=339
n=3
07n
=334
n=3
20n
=311
n=3
36n
=319
tota
l
aver
age
glu
cose
1 (3
.18-
6.00
)n
=319
4/41
(9.8
%)
6/40
(15.
0%)
8/40
(20.
0%)
5/51
(9.8
%)
9/41
(22.
0%)
9/36
(25.
0%)
5/28
(17.
9%)
19/4
2 (4
5.2%
)65
/319
(20.
4%)
2 (6
.01-
6.25
)n
=322
2/35
(5.7
%)
2/43
(4.7
%)
4/43
(9.3
%)
1/40
(2.5
%)
6/48
(12.
5%)
6/34
(17.
6%)
8/45
(17.
8%)
15/3
4 (4
4.1%
)44
/322
(13.
7%)
3 (6
.26-
6.46
)n
=338
1/44
(2.3
%)
2/49
(4.1
%)
3/47
(6.4
%)
4/41
(9.8
%)
8/41
(19.
5%)
6/37
(16.
2%)
7/35
(20.
0%)
14/4
4 (3
1.8%
)45
/338
(13.
3%)
4 (6
.47-
6.67
)n
=304
2/26
(7.7
%)
3/37
(8.1
%)
4/40
(10.
0%)
5/51
(9.8
%)
0/34
(0%
)4/
50 (8
.0%
)11
/47
(23.
4%)
7/19
(36.
8%)
36/3
04 (1
1.8%
)
5 (6
.68-
6.93
)n
=331
3/43
(7.0
%)
2/54
(3.7
%)
4/35
(11.
4%)
4/49
(8.2
%)
3/37
(8.1
%)
11/3
9 (2
8.2%
)9/
38 (2
3.7%
)9/
36 (2
5.0%
)45
/331
(13.
6%)
6 (6
.94-
7.29
)n
=332
9/42
(21.
4%)
3/47
(6.4
%)
1/35
(2.9
%)
2/32
(6.3
%)
7/41
(17.
1%)
5/44
(11.
4%)
6/44
(13.
6%)
12/3
7 (3
2.4%
)45
/322
(14.
0%)
7 (7
.30-
7.85
)n
=325
6/44
(13.
6%)
5/37
(13.
5%)
7/40
(17.
5%)
1/38
(2.6
%)
3/36
(8.3
%)
4/31
(12.
9%)
5/53
(9.4
%)
10/4
6 (2
1.7%
)41
/325
(12.
6%)
8 (7
.86-
29.9
8)n
=322
17/4
2 (4
0.5%
)6/
32 (1
8.8%
)3/
27 (1
1.1%
)4/
32 (1
2.5%
)8/
42 (1
9.0%
)3/
40 (7
.5%
)10
/46
(21.
7%)
21/6
1 (3
4.4%
)72
/322
(22.
4%)
tota
ln
=258
344
/317
(13.
9%)
29/3
39 (8
.6%
)34
/307
(11.
1%)
26/3
34 (7
.8%
)44
/320
(13.
8%)
48/3
11 (1
5.4%
)61
/336
(18.
2%)
107/
319
(33.
5%)
393/
2583
(15.
2%)
Tab
le 2
b:
ICU
mor
tali
ty (%
an
d n
) per
str
atu
m o
f st
and
ard
dev
iati
on o
f se
rum
glu
cose
an
d s
eru
m p
otas
siu
m l
evel
s d
uri
ng
the
TGC
per
iod
SD p
otas
siu
m1
(0.0
0-0.
22)
2 (0
.23-
0.28
)3
(0.2
9-0.
34)
4 (0
.35-
0.39
)5
(0.4
0-0.
45)
6 (0
.46-
0.53
)7
(0.5
4-0.
66)
8 (0
.67-
2.11
)
n=3
23n
=291
n=3
70n
=298
n=3
30n
=312
n=3
37n
=322
tota
l
SD g
luco
se1
(0.0
0-0.
99)
n=3
183/
68 (4
.4%
)3/
67 (4
.5%
)0/
59 (0
.0%
)1/
47 (2
.1%
)0/
28 (0
.0%
)1/
16 (6
.3%
)2/
15 (1
3.3%
)2/
18 (1
1.1%
)12
/318
(3.8
%)
2 (1
.00-
1.23
)n
=330
0/58
(0.0
%)
6/41
(14.
6%)
1/47
(2.1
%)
2/49
(4.1
%)
5/49
(10.
2%)
2/39
(5.1
%)
4/24
(16.
7%)
14/2
3 (6
0.9%
)34
/330
(10.
3%)
3 (1
.24-
1.42
)n
=318
0/39
(0.0
%)
3/44
(6.8
%)
3/54
(5.6
%)
1/39
(2.6
%)
6/35
(17.
1%)
7/41
(17.
1%)
8/42
(19.
0%)
5/24
(20.
8%)
33/3
18 (1
0.4%
)
4 (1
.43-
1.61
)n
=336
1/41
(2.4
%)
1/30
(3.3
%)
4/53
(7.5
%)
1/53
(1.9
%)
5/44
(11.
4%)
4/35
(11.
4%)
8/46
(17.
4%)
8/34
(23.
5%)
32/3
36 (9
.5%
)
5 (1
.62-
1.81
)n
=311
1/32
(3.1
%)
1/26
(3.8
%)
3/36
(8.3
%)
5/32
(15.
5%)
5/48
(10.
4%)
13/5
1 (2
5.5%
)10
/42
(23.
8%)
19/4
4 (4
3.2%
)57
/311
(18.
3%)
6 (1
.82-
2.12
)n
=322
5/29
(17.
2%)
3/22
(13.
6%)
5/43
(11.
6%)
1/28
(3.6
%)
5/43
(11.
6%)
10/4
4 (2
2.7%
)20
/67
(29.
9%)
19/4
6 (4
1.3%
)68
/322
(21.
1%)
7 (2
.13-
2.60
)n
=323
0/27
(0.0
%)
1/37
(2.7
%)
2/42
(4.8
%)
2/30
(6.7
%)
4/38
(10.
5%)
15/4
7 (3
1.9%
)20
/46
(43.
5%)
22/5
6 (3
9.3%
)66
/323
(20.
4%)
8 (2
.61-
24.4
5)n
=325
2/29
(6.9
%)
2/24
(8.3
%)
2/36
(5.6
%)
6/20
(30.
0%)
3/45
(6.7
%)
15/3
9 (3
8.5%
)17
/55
(30.
9%)
44/7
7 (5
7.1%
)91
/325
(28.
0%)
tota
ln
=258
312
/323
(3.7
%)
20/2
91 (6
.9%
)20
/370
(5.4
%)
19/2
98 (6
.4%
)33
/330
(10.
0%)
67/3
12 (2
1.5%
)89
/337
(26.
4%)
133/
322
(41.
3%)
393/
2583
(15.
2%)
117
3.4
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
Discussion
Although implementation of TGC resulted in a significant absolute reduction of
mean serum glucose levels of 2.1 mmol/L, mean serum potassium levels did not
change significantly. The proportion of patients with a hypoglycemia increased
from 1.7 to 7.6% while the proportion of patients with a hypokalemia did not
change. The variability of serum glucose levels within patients did not change
while the variability of serum potassium values decreased. The highest mortality
was seen in patients with low serum glucose levels combined with high serum
potassium levels and in patients with high serum glucose levels combined with
low serum potassium levels. ICU mortality increased with increasing variability for
both serum glucose and serum potassium levels.
Implementation of a TGC led to 3.4 times more frequent measurement of serum
glucose levels (27 vs 8), lower mean glucose levels and a higher incidence of
hypoglycemia. As both moderate and severe hypoglycemia are associated with an
increased risk of death, an elevated rate of hypoglycemia should be prevented27,28.
In our setting, after implementation of the TGC protocol, the incidence of
hypoglycemia was 7.6%. This percentage compares favorably to the percentages
5.1 – 28.6% found in other studies8, 11, 14. It is hard, however, to unravel whether the
increased incidence of hypoglycemia is caused by the lower mean glucose levels or
whether it is caused by the more frequent measuring of serum glucose levels, as
more frequent monitoring may also increase the chance to detect a hypoglycemia.
As insulin therapy induces a shift of potassium from the extracellular to the
intracellular space, we expected that the proportion of patients with a hypokalemia
would increase. However, in our study mean serum potassium levels and the
proportion of patients with a hypokalemia did not change significantly after
implementation of the TGC protocol. One study did find an increased incidence
of hypokalemia after implementation of a TGC, however, this study used a higher
threshold to define hypokalemia12, 29. Another study reported an increase of patient
days with hypokalemia just after application of TGC, but this increase did not
persist throughout the study period23. We believe that a serum potassium level ≤
3 mmol/L represents a more appropriate threshold as this comprises a serious risk
on arrhythmia30. Apparently, the unchanged policy in our setting to keep serum
potassium levels above 4 mmol/L was successful in preventing severe hypokalemia.
Some studies showed that TGC increased the variability of serum glucose levels.
This is of clinical importance as an increased variability is associated with a rise
in mortality. In our study, after implementation of TGC, serum glucose SD values
stayed about equal. This means that TGC can be implemented in the general practice
Chapter 3.4 | Influence of a strict glucose protocol on serum potassium levels in intensive care patients
118
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R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
without an increase of serum glucose SD values. SD of serum potassium levels even
decreased, while the target levels and monitoring frequency remained unchanged.
Different parameters have been reported to express glucose variability, among them
the Mean Absolute Glucose per hour (MAG) and SD. The MAG sums the absolute
differences between two consecutive serum glucose levels and averages these over
the time. Consequently, the MAG is influenced by the number of measurement31.
As in our study the number of measurements was 3.4 times higher in the TGC
period than in the conventional period, the MAG could not be used to compare the
variability. As the SD is the most frequently used parameter in literature22, the SD
was chosen to report the variability.
During TGC, the highest ICU mortality was seen at both low and high mean serum
glucose levels. A similar relationship between mean glucose levels and mortality
was recently described for cardiac patients admitted to the ICU32. Mortality, however,
was also high at both low and high serum potassium levels. This means that both
mean serum glucose and serum potassium levels were independently associated
with ICU mortality. Moreover, mortality for low and high mean serum potassium
levels was higher than for low and high mean serum glucose levels. Apparently, the
association between mean serum potassium levels and ICU mortality was stronger
than the association between serum glucose levels and ICU mortality.
The combined mean serum glucose – mean serum potassium levels table reveals
highest mortality rates in both the combined lowest serum glucose and highest
serum potassium subgroup (45.2%) and the combined highest serum glucose and
lowest serum potassium subgroup (40.5%). Patients in the first subgroup, may have
been severely ill, which is often accompanied with high serum potassium levels.
When these high serum potassium levels were treated with insulin, hypoglycemia
may have resulted. In the latter subgroup, the TGC protocol might have attributed
to the high mortality as correction of the high serum glucose level in combination
with the relatively low serum potassium level may have resulted in an unintended
hypokalemia.
ICU mortality also increased with increasing SD values for both serum glucose and
serum potassium levels and highest mortality rates were seen in the combined
highest serum glucose SD and serum potassium SD subgroups. Sakr et al recently
also showed a relationship between variability of serum sodium levels and
mortality, suggesting that high variability is unfavorable for a patient’s outcome33.
High variability may have been caused by the TGC protocol but may also be the
result of the severity of disease. As no information was available about the severity
of illness, it was not possible to unravel the association between variability, severity
of illness and mortality, which limits the causal interpretation.
119
3.4
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
Another limitation is that no information was available about the individual use of
insulin, parenteral feeding or co-medication. The use of co-medication might have
been relevant as it is known that many medications may influence both serum
glucose and/or serum potassium levels34-37. Beta2-sympathicomimetics, for example,
may induce hypokalemia and beta2-sympathicomimetics are widely used in the
ICU38. The use of co-medications, however, is not supposed to vary between the
conventional and TGC period to an extent that would have influenced the outcome
measures.
In conclusion, our study shows that TGC can be implemented was not associated
with an increased risk of serum potassium related events. During TGC, both low
and high serum glucose and low and high mean serum potassium levels should be
prevented, as these laboratory findings were associated with an increased mortality
rate. The same applies for a high variability of both serum glucose and serum
potassium levels.
Acknowledgement
The authors are grateful to Hanneke den Breeijen for the data analysis and to their colleagues
at the Utrecht Institute for Pharmaceutical Sciences and the UMC Utrecht for their support in
establishing and maintaining UPOD.
Chapter 3.4 | Influence of a strict glucose protocol on serum potassium levels in intensive care patients
120
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R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
R24
R25
R26
R27
R28
R29
R30
R31
R32
R33
R34
R35
R36
R37
R38
R39
R40
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20. Krinsley JS: Glycemic variability: A strong independent predictor of mortality in critically ill patients.
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21. Ali NA, O’Brien JM, Dungan K, et al: Glucose variability and mortality in patients with sepsis. Crit Care
Med 2008;36:2316-21
22. Eslami S, Taherzadeh Z, Schultz M, Abu-Hanna A. Glucose variability measures and their effect on
mortality: a systematic review. Int Care Med 2011;37:583-593
23. Onyenweni AJ, Winterstein AG. Rates of hypokalaemia after implementation of aggressive insulin
dosing in critical care patients. Pharmacoepidemiol Drug Saf 2007;16:S011
24. Berghe van den G, Schetz M, Vlasselaers D, et al: Intensive insulin therapy in critically ill patients:
NICE-SUGAR or Leuven blood glucose target? J Clin Endocrin Metab 2009;94:3163-3170
25. Berg ten MJ, Huisman A, van den Bemt PM, et al: Linking laboratory and medication data: new
opportunities for pharmacoepidemiological research. Clin Chem Lab Med 2007;45:13-19
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26. Wagner AK, Soumerai SB, Zhang F, et al: Segmented regression analysis of interrupted time series
studies in medical use research. J Clin Pharm Therapeut 2002;27:299-309
27. Hermanides J, Bosman RJ, Vriesendorp TM, et al: Hypoglycemia is associated with intensive care unit
mortality. Crit Care Med 2010;38:1430-1434
28. The NICE-SUGAR Study Investigators: Hypoglycemia and risk of death in critically ill patients. N Engl
J Med 2012;367:1108-1118
29. Vlasselaers D, Milants I, Desmet L, et al: Intensive insulin therapy for patients in paediatric intensive
care: a prospective, randomised controlled study. Lancet 2009;373:547–556
30. Paltiel O, Salakhov E, Ronen I, et al: Management of severe hypokalaemia in hospitalized patients.
Arch Int Med 2001;161:1089-1095
31. Harmsen RE, Spronk PE, Schultz MJ, Abu-Hanna A: May frequency of blood glucose measurement be
blurring the association between MAG and mortality? Crit Care Med 2011;39:224
32. Lipton JA, Barendse RJ, Van Domburg RT et al: Hyperglycemia at admission and during hospital stay
are independent risk factors for mortality in high risk cardiac patients admitted to an intensive
cardiac care unit. Eur Heart J Acute Cardiovasc Care 2013:2:306-313
33. Sakr Y, Rother S, Ferreira AM, Ewald C, et al: Fluctuations in serum sodium level are associated with
an increased risk of death in surgical ICU patients. Crit Care Med 2013;41:133-142
34. Rimmer JM, Horn JF, Gennari J: Hyperkalaemia as a complication of drug therapy. Arch Intern Med
1987;147:867-869
35. Paice BJ, Paterson KR, Onyanga-Omara F et al: Record linkage study of hypokalaemia in hospitalized
patients. Postgrad Med J 1986;62:187-191
36. Salem CB, Fathallah N, Hmouda H and Bouraoui K: Drug-Induced Hypoglycaemia, an update. Drug
Saf 2011;34 :21-45
37. Luna B, MN Feinglos: Drug-induced hyperglycemia. JAMA 2001; 286: 1945-1948
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Tijdschr Geneeskd. 1990;134:688-689
4General discussion and summary
Chapter 4 | General discussion and summary
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General discussion
Pharmaceutical care aims to optimize the benefit/harm balance of drug treatment
for the individual patient. Approved medicines have, on a population level, a by
regulatory authorities judged positive benefit / harm balance given appropriate use
by health care professionals and patients. On the level of the individual patient
there is in clinical practice, however, a large variability in treatment response:
for the individual patient the benefit/harm balance may substantially differ from
the population average. In a way, each time a medicine is prescribed, it can be
considered an n=1 experiment and adverse effects may occur. Medication errors are
an important cause of, often preventable, adverse drug events (ADEs)1. Medication
errors most frequently result from flaws in the complex medication management
iterative process consisting of prescribing, transcription and verification, pharmacy
dispensing, nurse administration to the patient or use by the patient himself,
monitoring the effects of, and evaluation of the pharmacotherapy2. Especially the
prescribing and monitoring stages are prone for errors3. Pharmaceutical care aims
to prevent such errors and should therefore especially focus on these two stages.
Drug-drug interactions (DDIs) have shown to contribute significantly to the
negative consequences of drug treatment4-6. There are many guidelines to support
healthcare professionals to mitigate the potential clinical consequences of DDIs
(pDDIs) for the individual patient7-10. These guidelines advise physicians and
pharmacists how to handle the pDDI for the individual patient at the moment
of either prescribing, for example by suggesting alternative medication, and/
or during the course of drug use by adequate monitoring. This monitoring may
consist of clinical monitoring, such as the clinical observation of desired and
unintended effects (e.g. skin reactions), but also by measurement of biomarkers,
which may consist of physical parameters like blood pressure and ECG or of
laboratory markers such as serum drug concentrations, serum creatinine or serum
electrolytes. Analysis of the Dutch clinical guidelines on pDDI management showed
that 33% of the risk mitigation strategies advise to monitor one or more laboratory
values11. As a limited set of laboratory markers is particularly involved in the risk
mitigation strategies of ADEs12,13, exchange between health care professionals of
information on drug serum levels, INR, serum creatinine, potassium, sodium levels
and pharmacogenetic markers became obliged by the Dutch law in 201214. Studies
on risk mitigation strategies should therefore especially focus on these markers.
The medication management process is in most hospitals nowadays supported by
information technology applications such as computerized physician order entry
systems (CPOE)15,16. A CPOE usually includes a basic form of clinical decision support
(CDS) for example for to alert health care professionals for the risk of pDDIs at
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the moment of drug prescribing: each new prescription is checked for drug-drug
interactions given the other active prescriptions for that patient. The moment
of prescribing is indeed an important moment to reflect on the appropriateness
and fit of the patient’s drug therapy, including the risk of the potential negative
effects of a pDDI. At this stage the physician can only decide to either continue or
discontinue the medication while many combinations of drugs are not necessarily
harmful when these are adequately monitored during the course of drug taking.
This means that at the moment of prescribing, the physician has to decide which
monitoring, when and how often has to be performed in order to minimize the risk
of actual occurrence of an ADE. Clinical guidelines of pDDI management, however,
often lack this clarity17,18.
It has been shown that in ambulatory care the risk of pDDIs is often not
monitored19-21. It may be expected that within the hospital setting patients are
monitored more adequately, because of the easy access to laboratory facilities and
the availability of other monitoring facilities in hospitals as well as the exacerbated
disease state of the patient. However, hospital stay is only short and ADEs that result
from pDDIs may therefore also become manifest after discharge22. This means that
monitoring should continue after discharge and that information on both drug use
and monitoring should accompany the patient when he moves from hospital care
to primary care. Additional to differences related to setting (hospital or at home),
risk mitigation strategies may also depend on patient characteristics. Geriatric
patients differ from children and ICU patients are different again. ICU patients are
in general more severely ill, which may interfere with their drug metabolism and
their capacity to counteract and deal with physiological disturbances. Although
these patients are monitored intensively, treatment is complex and risk mitigation
strategies therefore deserve extra attention.
The objective of this thesis was to describe the frequency and potential clinical
relevance of drug therapy monitoring with laboratory markers in hospitalized
patients with a special focus on pDDIs, potassium and ICU patients.
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Summary
A high burden of nonspecific computer alerts is known to undermine the safety
purposes of an alerting system. As basic pDDI alerting systems do not take relevant
patient characteristics into account, current systems have a high sensitivity but
a low specificity. Chapter 2 showed to what extent this problem may apply for
hospitalized patients. Indeed pDDIs occurred frequently in hospitalized patients.
Of all hospitalized patients, 25% encountered at least one pDDI, and mainly due to
the higher number of medication orders per patient, this percentage was twice as
high for the ICU population (54%). pDDIs occurring in ICU patients were of special
interest as medications used at the ICU are often categorized as “high risk” and
patients may be, due to their severity of illness, more vulnerable to ADEs resulting
from these pDDIs23,24. Chapter 2.1 and chapter 2.2 showed that both in the general
hospital setting and the ICU, a limited number of drug pairs were responsible for
the majority of the pDDI alerts. The top ten was responsible for 53% of the pDDIs at
general hospital departments and for 79% of the pDDIs occurring at the ICU. The
most frequently occurring pDDIs on the ICU was a reflection of the specific high
risk medications frequently used at the ICU and the top-10 therefore differed from
the top-10 pDDIs observed in patients hospitalized at general hospital departments.
The most frequently occurring possible outcome of the pDDIs was an increased
risk of side effects. Laboratory monitoring was the most frequently advised risk
mitigation strategy, namely in approximately 50% of all pDDIs (48.6% for patients
hospitalized at general hospital departments and 51.6% for patients hospitalized at
the ICU). This means that an advanced clinical decision support system which links
laboratory data to prescription data may be an important tool for a more efficient
and more effective management of risks associated with these pDDIs.
Chapter 3 showed that measurement of serum potassium, sodium and/or creatinine
was performed in about 50% of the patients somewhere during hospitalization.
These three markers were frequently involved in clinical risk management of drug
therapy12. If one of these three biomarkers was measured, the other two were
measured as well in over 90% of the patients, suggesting that these three markers
are often monitored simultaneously. Overall, patient- and admission related factors
such as age and medical specialism appeared to be the stronger predictors of
monitoring than the use of specific medications. The use of specific medication
requiring the monitoring of one of these three markers did not, or only modestly,
increase the frequency of monitoring. For example, the monitoring of serum
creatinine, was not more frequent in patients using medication that was renally
cleared and required dose modification in case of decreased renal function (DRF-
drugs). The computerized physician order entry system (CPOE), however, did not
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signal when a DRF drug was prescribed. As 77% of the patients were using such
a DRF drug, signaling would have led to a high burden of alerts. An exception of
medication leading to an increased percentage of patients with a measurement
is the use of potassium supplements, for which serum potassium measurement
increased from 41 to 99% of the patients.
To assess whether the laboratory values measured during hospitalization are
suitable for medication verification at discharge, the time of the last measurement
was also determined for each of these markers. The percentage of patients with
a measurement within the last 48 hours before discharge was less than 25%,
suggesting that there is still room for improvement to communicate a relevant test
result at transition of care.
As serum potassium is involved in the risk mitigation strategy of one of the most
frequently occurring pDDIs, e.g. those between serum potassium increasing drugs
(PIDs) and this pDDI has potentially serious consequences25, monitoring of this
electrolyte was studied in more detail. Chapter 3.2 showed that in the presence of
a CPOE with pDDI alerting, serum potassium levels were measured slightly more
frequently in patients with a pDDI between two potassium increasing drugs than
when the patients were using monotherapy of potassium increasing drugs (67% vs
58%). Whether this difference can be attributed to pDDI alerting, however, is hard
to say as baseline characteristics of patients in the pDDI group were different from
those in the monotherapy group. Even though physicians received a direct pop-up
to monitor serum potassium levels in case of prescribing two PIDs concomitantly,
serum potassium levels were not measured in 33% of patients and 10% of the
patients developed hyperkalemia
Although monitoring is often advised when medication is started17, there is less
attention for the role of monitoring when medication is discontinued, even when
this medication is involved in a pDDI. Inhibitors of the renin-angiotensin-aldosteron
system (RAS-i) and spironolactone are often combined with diuretics in patients
with cardiac failure. As these drugs have an opposite effect on serum potassium
levels, stopping one of these drugs may have consequences on serum potassium
levels and when these levels fall outside of normal ranges, even lead to a potassium
related cardiac event in these vulnerable patients. After discontinuation of the
potassium lowering drug, serum potassium levels increased in 59% of the patients
and 3.2% of the patients developed hyperkalemia (potassium>5.5 mmol/L) (chapter
3.3). When the potassium increasing drug was stopped, serum potassium levels
decreased in 70% of the patients and 17% developed hypokalemia (potassium <3.5
mmol/L). These results show that monitoring is not only important after starting
therapy, but also after discontinuation. As this need may also be true for patients
using other drugs, clinical risk management should not only be focused on risks
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that occur when a new medication is prescribed, but also on those that may occur
when the medication is stopped.
Serum potassium levels may also be influenced by insulin which is involved in
tight glucose protocols (TGC). TGC aims to lower glucose levels and the effect on
serum glucose levels has therefore been extensively studied. To what extent TGC
also influences serum potassium levels and possibly may result in potassium
related adverse events, is barely studied. TGC protocols therefore often lack clarity
on when and how often to monitor serum potassium levels as well. Results of
our study showed that TGC was not associated with an increased risk of serum
hypokalemia, although both low and high mean values and high variability of both
serum glucose and serum potassium levels were associated with an increased ICU
mortality and should therefore be prevented (chapter 3.4).
All studies that are presented in this thesis show the importance of monitoring
laboratory markers as a risk mitigation strategy in drug therapy for hospitalized
patients. Individual studies focused on the role of monitoring for patients with
pDDIs and vulnerable patients admitted to the ICU. In this chapter, the studies in
this thesis will be discussed in a broader perspective of three themes. In chapter
2 it is shown that a CDS based on basic pDDI alerting is leading to a high burden
of non-specific alerts (pDDI alerts generated for 8-10% of the prescriptions). The
result that only a limited number of drug-drug combinations was responsible for
the high burden of alerts and that the most frequently advised risk mitigation
strategy consisted of monitoring, offer possibilities to enhance these CDS systems
with monitoring data and improve the specificity for pDDI alerting. Moreover,
as in chapter 3 is shown that adherence to monitoring guidelines is hampered,
advanced CDS should also support adherence to monitoring guidelines. The next
part therefore discusses the restrictions of current CDS systems on mitigating
risks of pDDIs and options to improve CDS systems with a focus on improving the
specificity of alerting and adherence to monitoring guidelines. The second part
addresses the role of advanced clinical decision support on the ICU and in the final
part, the role of the pharmacist or clinical pharmacologist at the ICU, additional
to CDS is discussed.
Clinical decision supportGuidelines on medical and pharmaceutical care are continuously developing given
the dynamics in research and evidence, and health care professionals are expected
to practice in accordance with these guidelines. Health care professionals, however,
not always adhere to clinical guidelines including drug therapy guidelines. For
example, adherence of pharmacists in ambulatory care to pDDI guidelines was
overall only 69%, but varied largely depending on gender, age and number of drugs
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used26. The degree of adherence also varied with the nature of the recommended
risk mitigation strategy. Low adherence was found for alerts where the only
proposed mitigation strategy was drug substitution. Adherence was high when
the pharmacist could communicate the risk mitigation strategy directly with
the patient. Physicians may be hindered to adhere to guidelines for a variety of
reasons27,28. Lack of awareness and familiarity to therapy guidelines contribute to
decision making based on clinical experience rather than evidence or guidelines
which may in turn create risks for adverse events. Other frequently mentioned
reasons for a hampered adherence are lack on agreement on the content of the
guideline, lack of believe that the guideline recommendation can be performed,
lack of outcome expectancy, lack of ability to overcome the inertia of previous
practice, unclear guideline recommendations, and external barriers such as time
constraints or lack of a reminder system to perform recommendations.
The use of information technology in health care, especially the introduction
of (advanced) clinical decision support (CDS) which links data from different
systems and integrates clinical guidelines, may result in substantial improvement
in patient safety29. A broadly accepted definition of CDS is: “Providing clinicians
or patients with computer-generated clinical knowledge and patient-related
information, intelligently filtered or presented at appropriate times, to enhance
patient care”30. At present, CDSs have been developed for a myriad of clinical
issues, including systems for facilitating diagnosis31, reminders for prevention and
disease management 32 and medication management33,34. Clinical trials with CDS
as intervention have shown positive results, but evidence that CDS systems can
improve long term patient outcomes is still inconclusive35-37.
Current situation: basic CDS
Basic clinical decision support for medication management is in place in most
currently used computerized order entry systems (CPOEs) and includes drug-
allergy checking, dosing guidance, formulary decision support, duplicate therapy
checking, contra-indication checking and drug–drug interaction management34.
Basic CDS (bCDS) systems usually only check whether the newly prescribed drug is
in accordance with general guidelines on a population level, for example, whether
the drug potentially interacts with another drug that is already in use by the patient.
Additional clinical data specific for the patient at hand such as the patients’ actual
laboratory test results are not taken into account, and the alert is not tailored to a
specific setting given its monitoring facilities (e.g. hospital, ICU) nor to the expertise
/ specialty of the individual physician. For example, when two potassium drugs
are prescribed concomitantly, the bCDS on pDDI alerting will always generate an
alert for the risk of hyperkalemia, even when baseline serum potassium levels are
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low and the two potassium increasing drugs are prescribed with the intention to
increase serum potassium concentration. Moreover, bCDS also lacks the ability
to deal with different problems simultaneously, i.e. it assesses the clinical risk of
a pDDI and that of renal insufficiency separately from each other. As such, most
bCDS within computerized order entry systems (CPOEs) have a high sensitivity but
a low specificity, i.e. many false positive alerts appear in the prescribing stage. As
a consequence, to assess whether the patients is really at risk for a hyperkalemia,
prescribers have to interrupt their prescribing process to check in another system
or another module of the EPR for additional data on (co)morbidity, kidney function
and serum potassium levels. When they have to consult other information systems,
this may be a time consuming process. Although physicians generally believe that
a bCDS on pDDIs improves their prescribing performance, they also fear the high
burden of nonspecific alerts38.
Chapter 2 of this thesis confirms that basic pDDI alerting indeed generates a large
number of nonspecific warnings for patients both hospitalized at general hospital
department and at the ICU. This may contribute to alert fatique and non-adherence
to the risk mitigation advises given in the alerts39,40.
From bCDS to more advanced CDS
Several studies have investigated methods to increase the specificity of the alerts
but only some have shown to be partially effective (table 1) 41-44.
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Table 1: Methods to improve specificity of DDI alerting and to prevent alert fatigue
Reference Method to enhance DDI Outcome Remarks
vd Sijs 200841 turning off frequently overriden drug alerts
no consensus between physicians
turning off alerts per physician is still option
Paterno 200942 divide pDDIs into severity level indicatorsfor life-threatening DDIs: hard stop for one of the interacting drugs for less serious pDDIs: alert + select override reasonfor least serious pDDIs: alert providing information only
Acceptance rates for tiered versus non-tiered sites:100% vs 34% (p<0.001) for life-threatening DDIs;29% vs 11% (p<0.001) for less severe DDIs
only 0.2% of the alerts is life-threatening
Shah 200643 designate only critical to high-severity alerts to be interruptive to clinician workflow
acceptance rates: 100% for the life-threatening DDIs41% of the severe DDIs
acceptance rates are not compared to general DDI alerting
Duke 201344 integration of relevant patient specific laboratory data for serum potassium or creatinine into pDDI alerts for two potassium increasing drugshigh risk patients baseline potassium >5.0 mEq/l and/or creatinine ≥1.5 mg/dl (132 μmol/l)
no significant difference in alert-adherence in high-risk patients between the intervention group (15.3%) and the control group (16.8%) ( p=0.71)
adherence was defined as discontinuation of one of the interacting drugs; other risk mitigation strategies such as monitoring frequency were not taken into account
One study investigated the possibility to turn off alerts, but this option was not
successful as physicians could not agree on which alerts could be turned off41.
Another trial integrated relevant patient specific test results for serum potassium
and serum creatinine levels into the pDDI alert when two potassium increasing
drugs were prescribed concomitantly, but this option did not improve adherence
in terms of stopping one of the interacting drugs, even when baseline serum
potassium levels were high (> 5.5 mmol/L)44. Only when pDDIs were accompanied
with information on severity and combined with a preprogrammed order to
discontinue one of the interacting drugs, adherence improved42. These studies,
however, always measured adherence in terms of stopping one of the interacting
drugs, while often multiple options for risk mitigation strategies are advised.
Chapter 2, however, shows that the most frequently advised risk mitigation
strategy consists of monitoring and that 48% of this monitoring even constituted
of measurement of laboratory markers. Likewise, Seidling et al reported that the
possible outcome of 43% of the pDDI pairs might be modulated by patient-related
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factors which mainly consisted of measurement of three laboratory values, i.e.
renal function, potassium, and therapeutic drug monitoring results45. This means
that physicians, who did not stop one of the interacting drugs, still might have
adhered to the pDDI guidelines by performing monitoring. Because it is known
that “willing to monitor” is the most frequently mentioned reason for “not willing
to stop”42,46, appropriate monitoring would have been a better outcome measure
than discontinuation of one of the two interacting drugs. Linking laboratory values
to pharmacy data is therefore still considered as a promising option to enhance
pDDI alerting.
Although physicians have good intentions on monitoring, literature indicates that
adherence to monitoring protocols may be poor47. Poor adherence to monitoring
was indeed confirmed in chapter 3.1 in which is shown that the use of specific
medications requiring the monitoring of serum potassium, sodium or creatinine
did not, or only modestly, increase the percentage of monitoring. Strategies to
improve adherence to pDDI guidelines, should therefore also focus on monitoring.
This monitoring may consists of both measurement of baseline values and follow-
up values. Electronic reminders have shown to improve the adherence to baseline
monitoring48, but as pDDI alerts are only generated at moment of prescribing in
bCDS, improvement of follow-up measurement is not likely. The proposal of a
second “corollary” order for monitoring that directly follows the first order, may
overcome this mismatch in timing49. For example, when an alert is generated for
a pDDI between two potassium increasing drugs, the alert may automatically be
followed by a corollary order for serum potassium and creatinine measurement.
The corollary order may even consist of a series of pending orders for serum
potassium and creatinine measurements and propose to measure these values
according to the guideline as long as the pDDI is active. When also other orders
for measurements are pending, the system may propose to combine these. As
monitoring is important not only when a new drug is started, but sometimes also
when a drug is discontinued (chapter 3.3) or when the dose is changed, corollary
orders may also be useful in these situations. Subsequently, when new information
on laboratory values becomes available, these values might show that risk-benefit
balance for a drug therapy has been changed for a specific patient. The physician
has than again to decide whether to change the drug therapy regimen. The CDS
should therefore also be triggered when new information on laboratory values is
entered into the system. The same may apply for other relevant information such
as for example on (co)morbidity or patient data.
A model for an advanced CDS that may account for changes in a multifactorial
approach is proposed in Figure 1. The model is adapted from Berner et al. who
described an advanced CDS on diagnosing50. The scheme starts from a database
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including all kind of structured patient related data and may for example consist
of basic patient characteristics, medications, biomarkers, and (co)morbidity (1). The
use of “smart forms” may, for example, help to enter these data in a structured
way51. When new information on one of the patient related data is entered into the
database (2) the reasoning engine is triggered (3) to activate relevant clinical rules
(4). Based on information in the knowledge base (5), which includes information on
guidelines, such as threshold values for laboratory markers and risk estimates on
adverse events, the reasoning engine estimates whether there is an increased risk
on ADEs in the new situation or not. When the knowledge base indicates that also
data is needed on other patient related parameters, the reasoning engine extracts
this information from the patient related data. When the engine determines
an increased risk that requires action, the action is communicated through the
output (6). As a rule-based method requires explicit knowledge of the details of each
medical domain, this may result in many complex rules in the reasoning engine.
Rules also use knowledge from the knowledge base, which is based on clinical
guidelines that are developed for populations and not for the individual patients.
An alternative for rule based reasoning may be case based reasoning. Cased based
reasoning uses knowledge in the form of specific cases to solve a new problem,
and the solution is based on the similarities between the new problem and the
available cases. Experiments have shown that addition of a case based engine to a
rule based engine may add details that are necessary in the knowledge base and
that the output is more appropriate to the individual patient. Despite, case based
reasoning also faces a number of problems. First, case based reasoning is based
on historic cases, which means that comparable cases need to be available in a
case database. Secondly, information which has been derived from historic cases
may also lack reliability. And last, but not least, case based reasoning is based on
complex algorithms that need to be programmed. Despite these limitations case
based reasoning seems promising to overcome the limitations of the rule based
engine when it is added to rule based reasoning52.
Figure 1: a schematic representation of advanced CDS on the monitoring stage.
For example, when a new test result for a serum potassium is entered into the patient related database
(=input), the reasoning engine is triggered to check whether this new information on serum potassium
alters the risk on an ADE. The knowledge base may provide risk estimates on serum potassium levels
that may increase the risk of an ADE, in the presence or absence of certain potassium increasing or
decreasing medications or combination of such medications, kidney function and (co)morbidity. When
the reasoning engine estimates an increased risk and action is required, the engine generates an output.
The output can consist of a preprogrammed/corollary order or an advice to the doctor, the pharmacist
or even to the patient48. The model is now elaborated on pDDIs, but when rules and knowledge are
introduced on for example monitoring of efficacy, this concept may also support monitoring of
efficacy.
Some remarks have to be made for this concept. First, for the rule based engine, the knowledge base
should be expanded with relevant data on risks. Guidelines on risk mitigation strategies, for pDDIs for
example, only contain free text fields with information on monitoring data, while these guidelines can
only work when this information is available in separate data fields on the identity, normal ranges, and
monitoring frequency of the relevant biomarkers. The databases on risk management of pDDIs should
therefore be expanded with these fields. Second, as every change in the steady state of the database
will trigger the reasoning engine, it may be necessary to expand the knowledge base with a set of
biomarkers, medications and comorbidities which may canalize the set of rules that are activated.
Finally, as input may consist of multiple kinds of data, it should be determined to whom, when, and by
which means the output is communicated. As the treating physician is primarily responsible for the
Figure 1: a schematic representation of advanced CDS on the monitoring stage.
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For example, when a new test result for a serum potassium is entered into the
patient related database (=input), the reasoning engine is triggered to check
whether this new information on serum potassium alters the risk on an ADE.
The knowledge base may provide risk estimates on serum potassium levels that
may increase the risk of an ADE, in the presence or absence of certain potassium
increasing or decreasing medications or combination of such medications, kidney
function and (co)morbidity. When the reasoning engine estimates an increased risk
and action is required, the engine generates an output. The output can consist of
a preprogrammed/corollary order or an advice to the doctor, the pharmacist or
even to the patient48. The model is now elaborated on pDDIs, but when rules and
knowledge are introduced on for example monitoring of efficacy, this concept may
also support monitoring of efficacy.
Some remarks have to be made for this concept. First, for the rule based engine,
the knowledge base should be expanded with relevant data on risks. Guidelines
on risk mitigation strategies, for pDDIs for example, only contain free text fields
with information on monitoring data, while these guidelines can only work
when this information is available in separate data fields on the identity, normal
ranges, and monitoring frequency of the relevant biomarkers. The databases on
risk management of pDDIs should therefore be expanded with these fields. Second,
as every change in the steady state of the database will trigger the reasoning
engine, it may be necessary to expand the knowledge base with a set of biomarkers,
medications and comorbidities which may canalize the set of rules that are
activated. Finally, as input may consist of multiple kinds of data, it should be
determined to whom, when, and by which means the output is communicated.
As the treating physician is primarily responsible for the patient, it is reasonable
that risks and required actions are communicated with the treating physician.
Although, there are also situations in which action is required from another health
care professional, such as clinical observation by the nurse. Information may then
also be communicated to another health care professional. It can even be imagined
that action of a health care professional is not always necessary, for example when
measurement of serum potassium levels is advised, the system may check whether
the test can be performed in material which is still available from the patient in
the laboratory. When material is still available, the order can automatically be
generated without an active action of the health care professional. However, this
may only work as long as all information on required and the performed actions
are recorded in the central patient information system.
Current basic CDS systems are developed to provide guidance on drug treatment
but often lack specificity. ICT builders should therefore work on a multifactorial
approach of CDS systems. Only when the reasoning engine is able to use
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multifactorial data, i.e. not only medication data but also data on laboratory values
and morbidity, specificity of the output can be improved. ICT builders should also
work on the possibility to trigger the CDS by multifactorial changes. When changes
on laboratory data or comorbidity may also trigger the system, the monitoring
stage of the medication process is also supported. Moreover, it should be possible
that the reasoning engine works with both a static knowledge base and a flexible
knowledge based that is filled with information that is obtained from cases. Finally,
the CDS should be able to generate several types of output to several kind of health
care providers, i.e. output may also consist of (pending) corollary orders and a
change of a laboratory value may also alert the pharmacist.
Clinical decision support on the ICUBecause patients as well as the setting are different, possibilities to increase
the specificity of pDDI alerts may differ for the ICU compared to the general
hospital. The ICU differs in many aspects from general hospital departments. ICU
patients are more severely ill compared to patients admitted at general hospital
departments and often require support of their vital physiological functions like
mechanical ventilation, renal replacement therapy and intravenous support of
their blood pressure. Given the urgent need for life supporting therapy, higher
risks may be also accepted for medical treatment. Higher risks are also accepted
for treatment with medications, despite it is known that these patients, due to
their altered drug disposition and metabolism and their decreased capacity to
deal with physiological disturbances, may be more vulnerable to the side effects
of drugs. The life supporting therapy is often provided with only a limited set of
highly pharmacologic active medications. Although these medications are often
considered as “high-risk” medications53, ICU physicians are very familiar with the
use of these medications. Parenteral administration and the short half-life enable to
titrate the dose to a desired effect which is needed in acute illness while the effect
may also be easily reversed by discontinuation of the medication. Due to the focus
on life supporting therapies, ICU physicians may be less familiar with medications
the patients use at home for chronic diseases. Although these medications are often
(temporarily) discontinued when a patient is admitted at the ICU, these medications
may possibly create a higher risk especially when the patient is transferred to a
next health care provider again. To mitigate risks, ICU patients are intensively
monitored, both clinically, physiologically and biochemically. Chapter 2 shows that
the most frequently advised risk mitigation strategy is monitoring. The high rate of
monitoring at the ICU therefore offers possibilities to enhance a CDS for pDDIs at
the ICU. As suggested in the previous part, these laboratory data could be integrated
in the CDS. Whether laboratory enhancement of pDDI alerts at the moment of
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prescribing will actually change drug therapy, however, may be argued, also when
at the moment of prescribing it is known that laboratory values deviate from
reference values. As laboratory marker values may vary highly within the same ICU
patient over time, the single previous value at moment of prescribing does usually
not adequately represent the patient’s condition over time. Moreover, medical need
of drug treatment will often prevail, despite laboratory values outside normal
limits at moment of prescribing. Risks, however, can also be accepted more easily
as patients are continuously monitored and deviations may, due to the technical
facilities and intravenous access of ICU patients, be corrected quickly. Due to the
intensive monitoring, some pDDIs may even lack clinical relevance on the ICU, such
as for example the combination of insulin with a beta-blocker. In general practice
this combination is discouraged because beta-blockers may mask the symptoms of
a hypoglycemia, resulting in a possible delay of its detection and correction. ICU
patients, in contrast, usually do not notice hypoglycemia themselves anyway, and
due to the tight glucose protocol, glucose levels are frequently monitored. As such,
alerting for this pDDI is not clinically relevant in an ICU setting.
Because patients on the ICU are usually intensively monitored, and physicians
rather act on deviating monitoring values than on alerts of possible risks on pDDIs,
significant deviation of biomarkers might be a better moment to trigger the CDS on
the ICU and reflect whether there is a possible relation with the use of medications54.
The CDS may then not only be triggered when laboratory values fall outside normal
limits, but also when they are highly variable, for example when drug levels deviate
more than 50% compared to the previous level. Large within patient variability
has for example been associated with negative outcomes for lithium55. Likewise,
chapter 3.4 shows that highly variable serum potassium levels are also associated
with increased ICU mortality. As many medications may influence potassium levels,
physicians should always have to consider the role of medication in this. However,
due to their severity and complexity of disease, deviations in biomarkers are very
common in ICU patients. Often, it will not be clear which part of the deviation
can be attributed to the use of a certain drug or combination of drugs (DDI) and
which part is attributable to the myriad of other potential conditions, such as renal
insufficiency and infection. To overcome these problems, it has been suggested that
case based reasoning would be a valuable additional strategy especially at the ICU.
A conceptual version of a combined rule based and case based reasoning engine has
indeed shown promising results52,56, but as far as known, case based reasoning has
still not been implemented on ICUs.
Possibly, ICU patients are too complex to capture all covariates in CDS. This means
that human interpretation of data always stays necessary to make decisions in
complex situations. ICU physicians are therefore highly trained professionals who
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are able to make decisions for complex patients. In contrast to the computer, they
can make decisions on the information which is available, also when part of the
information is lacking. Patient information should therefore be easily available.
Over the past ten years, patient data monitoring systems (PDMS) have been developed
in a way that all relevant patient-specific information can be presented in a logical
way, such as in dashboards on topics like fluid balance, circulation, respiration and
medications57. Although it can be argued whether “organization of information into
a PDMS” fits into the definition of a CDS30, organization of information may be very
helpful in human decision making. CDS may also be integrated when information
on for example the dashboard on current drug use is enhanced with information
on for example normal dosage ranges for the prescribed drugs, normal ranges of
therapeutic drug levels, information on current pDDIs and relevant monitoring
data. Logical organization of information in dashboards, may offer the possibility
to evaluate the patient at moments that may optimally fit into the physicians work
flow and it is known that CDS that fits into the work process of the physicians, are
more likely to be successful46.
The myriad of information which is presented in a PDMS, however, also generates
the risk that important information on relevant problems is easily overlooked.
Therefore there is still a need to be alerted for relevant problems. It has been
shown that clinical rules may improve prescribing when a problem is demarcated
such as dosing of antimicrobials for patients with renal failure58. As the twenty
most frequently occurring pDDIs at the ICU are responsible for more than 90%
of the alerts, this may also apply for pDDIs on the ICU. First, these pDDIs should
be judged on clinical relevance, given the monitoring facilities on the ICU. When
clinical relevance is lacking, these pDDIs may be turned off. Secondly, when the
risk of pDDIs may be relevant, and risks may be mitigated by the measurement of
laboratory values, a rule may link these laboratory data to prescription data and
signal when laboratory data deviate from normal limits which are determined
for ICU patients. The alert should than not only be generated when laboratory
values fall outside normal limits at the moment of prescribing, but also in the
monitoring stage of the pDDI. Thirdly, when a combination of drugs is potentially
life-threatening, and the clinician is required either to cancel the order he or she
is writing or discontinue the pre-existing drug order, pDDIs may be signaled in a
basic way. As this method may still generate a high burden of alerts, and physicians
do not want to be disturbed for a high burden of low specific alerts, the alerts may
also be presented on for example the medication dashboard of the PDMS. As direct
action is often not needed, the clinical pharmacist may than judge on the clinical
relevance for the specific patient and use this information in the multidisciplinary
consultation. When direct action is needed, such as for example the pDDI between
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valproate and meropenem, the pharmacist may also decide to directly consult the
physician59. Or, when medication with a narrow therapeutic index is involved in a
pDDI, such as for ciclosporine and tacrolimus, the pharmacist may even decide on
therapeutic drug monitoring.
The role of the clinical pharmacist or pharmacologist at the ICUThe previous parts of this general discussion described that it is important to
support the complex processes on the ICU with a PDMS, CPOE and CDS. CPOEs are
increasingly implemented into these PDMS and have shown to reduce the number
of prescribing errors compared to the hand-written medication order process but
the support of complex medication management processes with CDS systems is
still lagging behind60,61. Basic CDS systems lack specificity and although specificity
may be increased with rules that integrate multifactorial patient data, it will often
be too complex to integrate all these data in clinical rules for the ICU. CDS may
also not detect all drug related problems on the ICU. Recent research showed that a
CPOE/CDS generated an alert for only 8% of the drug related problems which were
identified by the clinical pharmacist62. CDS on medication is therefore often not
implemented into the CPOE of a PDMS on the ICU and evaluation of the risk/benefit
ratio of treatment with medications therefore still relies on the judgment of highly
trained ICU physicians. It was reported, however, that ADEs frequently occur at
ICUs of hospitals in the United States of America (US), and that approximately two-
third of the actually ADEs were judged preventable63. Pharmacists therefore still
search for methods to support the highly trained ICU physicians in the prevention
of these ADEs. Since several trials that were performed at ICUs of hospitals in
the US showed that participation of a clinical pharmacist on the ICU, especially
those that are trained in critical care, may reduce the frequency and costs of drug-
related problems, these clinical pharmacists are increasingly involved in ICU64-68.
The activities that were performed by these clinical pharmacists are summarized
in table 2. It was estimated that in 2006 clinical pharmacists were involved in
approximately two-third of the ICUs69.
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Table 2: Activities performed by clinical pharmacists on the ICU64-68
participation multidisciplinary medicine roundson ward stay for consultationperforming chart reviews on all ICU patientperforming medication / allergy interviews - drug histories and medication verificationproviding drug informationproviding pharmacokinetic monitoring / drug therapy monitoringmaking and documenting recommendationsidentifying, reporting, and assisting in the management and/or prevention of ADEs and medication errorsserving as a liaison between the pharmacy, nursing and medical/surgical servicescounseling patients, family members, and/or caretakersin-service educationnutrition team participation
The setting of a US hospital pharmacy, however, cannot be compared to setting
of a Dutch hospital pharmacy. In the US much more pharmacists are employed
in hospital pharmacies, which makes it easier to allocate time to clinical services.
Moreover, in the US, the order-entry and verification process is still performed by
pharmacists, while in the Netherlands, this task is mainly performed by pharmacy
technicians supervised by pharmacists. Dutch pharmacists therefore traditionally
focus on improving the medication management process (system approach) rather
than on individual patient care. This systems pharmacy approach may consist
of several preventive risk minimization strategies, such as optimization of the
logistic chain, formulary management, participation in guideline development
and implementation and maintenance of a CPOE/CDS70. The risks associated
with the drugs logistic chain, for example, can be mitigated by implementation
of automated dispensing cabinets and bar code verification at moment of drug
dispensing or drug administration71,72. A specific ICU formulary defined by a
multidisciplinary ICU drugs and therapeutic committee may lead to selection of
medications that have less interactions than other medications within the same
therapeutic group. A limited formulary will increase familiarity and knowledge of
all professionals involved and thereby decrease the likelihood of medication errors.
Furthermore, pharmacists increasingly offer services consisting of preparation of
ready-to-use parenteral medication in a decentralized pharmacy satellite located
in and dedicated solely to the care of patients. Pharmacists usually also participate
in guideline development for the ICU. Guidelines that are tailored to the needs
of ICU patients, may also prevent ADEs. The guideline on gastric protection, for
example, is often not applied as the use of proton-pump inhibitors is associated
with an increased risk of pneumonia in patients that are mechanically ventilated.
Because these guidelines may also be necessary to develop a CDS based on rules,
pharmacists may also play a role in improving CDS on medications.
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In 2010, however, Klopowotska et al. showed that on-ward participation of a
specially trained hospital pharmacist also reduced the number of ADEs occurring
at a Dutch ICU73. The on-ward participation consisted of chart review of the patients’
medication and participation into the multidisciplinary patient review meeting
(MDR). Errors that were prevented mainly consisted of drug or dose omissions,
monitoring errors (TDM and renal function), and improper dosage errors. Presence
of the pharmacist on wards and discussion of the issues and recommendations
with the physicians in the MDR apparently led to better acceptance of the advices.
Better acceptance was also reported by Schepers et al, who found that clinicians
most prefer advises that were performed by the pharmacy74. When a potential risk
is established by a pharmacist, the pharmacist can provide additional information
to the physician and motivate why the risk may be relevant for the specific patient.
Pharmacist participation at the ICU may also lead to a better accessibility, enabling
physicians and nurses to approach the pharmacist for questions they otherwise
would not have called for.
The activities performed by clinical pharmacists in the US, however, have shown
to be highly variable and direct patient care often only including providing
distribution functions in a satellite ICU pharmacy69. Chart review, consisting of
evaluation of new medication orders for its appropriateness for given indication,
duration of therapy, drug dosage and frequency, risk of drug-drug and drug-disease
interactions, pharmacological duplications and drug omissions is not always
performed on daily basis. Despite that elements from the chart review might be
performed standardly, a complete chart review is also often not performed by Dutch
pharmacists. The potential problems found by Klopowotoska et al. such as errors of
omission and monitoring errors, however, can only be found with a complete chart
review. When medication safety has to be improved, pharmacists should therefore
first agree on which service is most relevant and effective69,75,76. When is agreed that
chart review is necessary, and this fundamental service is standardly provided at
ICUs, pharmacies may upgrade their services to a “desirable” and “optimal” level
as described by Dager et al. and attend a well- trained pharmacist to the MDR77.
Because the content of desirable and optimal services may depend on the setting of
the ICU such as the number of beds, the level of the ICU and the presence of a CDS
on medications, pharmacists should discuss with the medical staff which level is
desired for the care of their ICU patients.
In all trials that showed a reduction of ADEs and costs, the attending pharmacist was
experienced or did have a special training on critical care. Because the basic training
programs of clinical pharmacists in the US mainly consists of pharmacotherapy
for a broad range of diseases, special training programs on critical care have been
developed by the American organization of hospital pharmacists78-80. This training
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program consists of training on ICU-patient specific pharmacotherapy and solving
drug therapy problems in the context of the pathophysiological changes during
critical illness77. The training program of Dutch hospital pharmacists differs
from the basic training of the clinical pharmacists in the US which consists of
training on a broad range of skills such as drug preparation, therapeutic drug
monitoring, and clinical activities. To perform chart review and participate at MDR
activities, a special training program might also be necessary for Dutch hospital
pharmacists. The clinical pharmacologist in the Netherlands, however, may already
have the knowledge to participate into the MDR of at a Dutch ICU. In contrast
to many other countries where only medical doctors can become certified as a
clinical pharmacologists, hospital pharmacists may also be certified as a clinical
pharmacologist in the Netherlands. The certification program consists of achieving
knowledge on drug-metabolism, pharmacokinetics, pharmacodynamics and
exploration of mechanism of action and effects also in specific patient populations.
Because this specific patient population may consist of the vulnerable intensive care
patient, the knowledge of a clinical pharmacologist may contribute very well to the
safe use of medications on the intensive care81. Because clinical pharmacologists
are experienced to teach pharmacology to medical students, they may also provide
an educational role in pharmacotherapy on the ICU. The attendance of a clinical
pharmacist/pharmacologist to the ICU, however, does not mean that health care
professionals should not invest in the development of CDS systems on medications.
Although CDS systems on medications lack specificity on the ICU, CDS can still
be used to make the medication review process more efficient82. Probably the
combination of improvements in the ICU medication management process (systems
pharmacy) and the attendance of a pharmacist / clinical pharmacologist that is
specialized in critical care, is the most effective strategy to reduce the frequency of
adverse drug events.
Recommendations for clinical practiceThis thesis leads to the following recommendations:
- pDDI guidelines should include which patients are at risk provide
clear instructions on when and how monitoring should be performed,
especially for medications which may influence serum potassium levels.
- CDS systems should integrate pharmacy data with laboratory data and
other clinical patient data with the aim to increase the specificity of pDDI
alerts.
- CDS systems should be developed that support adherence to monitoring
guidelines. Corollary orders and automated reminders on monitoring are
suggested as options to improve adherence, but further research is needed
to investigate whether these options actually prevent ADEs.
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- To prevent ADEs, CDS systems should not only enable triggering when
a new medication is prescribed, but also when new information on
monitoring values or other patient specific data such as morbidity
becomes available or medication is discontinued.
- When measurement of laboratory values is relevant to prevent ADEs
which may occur after discharge of hospitalized patients, measurement
of laboratory values should also be performed before discharge. These
laboratory data should accompany pharmacy data which are provided to
the next health care provider.
- Developers should invest in CDS that is tailored to the needs of the ICU.
Options to improve such a CDS are:
o alerting when significant deviations of biomarkers are detected
in the presence of a pDDI
o alerting to another health care provider, such as a clinical
pharmacist who can judge on the clinical relevance
- The clinical setting and expertise of the professional should be taken into
account to increase the specificity of a CDS on DDI alerting.
- A specific G-standard should be developed for the ICU that includes
medications that are frequently used on the ICU, dosages that are accepted
at the ICU and pDDIs that are relevant for the ICU.
ConclusionsThe findings from the studies presented in this thesis show that only few drug-
drug combinations are responsible for the majority of the pDDI alerts and that
the risk of the majority of these alerts may be mitigated by monitoring. It was
also shown, however, that adherence to these guidelines varies largely, depending
on both patient characteristics and type of medication used. Physicians seem to
adhere most to monitoring guidelines when the effect on laboratory markers is
intended. CDS should therefore better support the monitoring stage of drug
therapy. Special attention for monitoring is needed when risk factors change such
as when medication which is involved in a pDDI is discontinued or at moments
that patients are transferred in care.
A limited number of drug-drug pairs were also responsible for the majority of
the alerts at the ICU, but as monitoring of general laboratory markers is already
frequently performed at the ICU, not all pDDIs are relevant. For the majority of the
pDDIs, however, linking laboratory data to prescription data may be an important
tool for effective management of risks associated with these pDDIs. For other
relevant pDDIs, it may still be worthwhile to signal, despite higher risks may be
accepted for medical treatment at the ICU.
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Samenvatting
List of co-authors
List of publications
Dankwoord
Curriculum vitae
Samenvatting
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Samenvatting
Farmaceutische zorg heeft tot doel de balans tussen risico en baten van behandeling
met geneesmiddelen te optimaliseren. Registratie-autoriteiten beoordelen deze
risico-baten balans voor de populatie waarvoor het geneesmiddel is onderzocht.
Als deze positief uitvalt worden de geneesmiddelen geregistreerd. In de klinische
praktijk bestaan echter vaak grote verschillen tussen patiënten. Dit betekent dat
de risico-baten balans voor de individuele patiënt kan verschillen van die van het
groepsgemiddelde en dat iedere patiënt die een geneesmiddel gebruikt kans heeft
op onverwachte bijwerkingen. Een groot deel van de bijwerkingen kan worden
vermeden door het toepassen van risicomitigerende strategieën. De samenvatting
van productkenmerken van geregistreerde geneesmiddelen en (behandel)richtlijnen
adviseren vaak welke risicomitigerende strategieën kunnen worden toegepast. Als
deze richtlijnen niet goed worden nageleefd, kunnen vermijdbare bijwerkingen
ontstaan. Deze bijwerkingen worden gezien als medicatiefout. Medicatiefouten
ontstaan vaak in het complexe medicatieproces dat bestaat uit het voorschrijven van
geneesmiddelen, het interpreteren en verifiëren van de voorschriften, het afleveren
van geneesmiddelen, de toediening door de zorgverlener of patiënt zelf, het
monitoren van het effect en de evaluatie van de farmacotherapie. Medicatiefouten
blijken vooral te worden gemaakt in het stadium van voorschrijven en stadium
van monitoren. Omdat de farmaceutische zorg mede tot doel heeft deze fouten te
voorkomen, is het van belang dat aandacht wordt besteed aan de medicatiefouten
die optreden in deze twee stadia.
Geneesmiddeleninteracties blijken in belangrijke mate bij te dragen aan de
negatieve effecten van behandeling met geneesmiddelen. Er bestaan daarom veel
richtlijnen die de zorgprofessional ondersteunen om de mogelijke gevolgen van deze
geneesmiddeleninteracties te voorkomen. Voorschrijfsystemen met een geïntegreerd
beslissingsondersteunend systeem voor geneesmiddeleninteracties kunnen op basis
van deze richtlijnen advies geven over de risicomitigerende strategieën die kunnen
worden toegepast op de individuele patiënt. Dergelijke systemen signaleren een
mogelijke geneesmiddeleninteractie vaak op het moment van voorschrijven en
geven dan bijvoorbeeld een voorstel om te kiezen voor een alternatief geneesmiddel
of om tijdens gebruik van deze geneesmiddelen het effect van de interactie goed
te monitoren. Dit monitoren kan bestaan uit klinische monitoring (observatie
van ongewenste effecten zoals huidreacties), het meten van fysische parameters
(bijvoorbeeld het opnemen van de bloeddruk of van een elektrocardiogram) of het
meten van laboratorium markers (geneesmiddelenconcentraties, serum creatinine
waarden, serum elektrolyt waarden en dergelijke). Uit analyse van de Nederlandse
Samenvatting
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richtlijn voor geneesmiddeleninteracties is gebleken dat maar liefst 33% van alle
risicomitigerende strategieën adviseert om een of meer laboratoriummarkers
te meten. Ook is gebleken dat hierbij slechts een beperkte set van laboratorium
markers betrokken is. In 2012 heeft de Nederlandse wet zorgverleners daarom
verplicht gesteld om informatie over deze beperkte set van markers, bestaande
uit geneesmiddelenconcentraties in bloed, international normalized ratio, serum
creatinine, kalium, natrium en farmacogenetische markers uit te wisselen.
Onderzoek naar risicomitigerende strategieën zou zich daarom op deze markers
moeten concentreren.
Het voorschrijven van medicatie wordt in de meeste ziekenhuizen tegenwoordig
informatietechnologisch ondersteund via een geautomatiseerde voorschrijfsysteem
(computerized order entry system, CPOE). In het voorschrijfsysteem is vaak
een basale vorm van klinische beslissingsondersteuning geïntegreerd waarbij
zorgverleners tijdens het voorschrijven gewaarschuwd worden voor het risico
op mogelijke geneesmiddeleninteracties. Daarbij wordt elk nieuw voorschrift
gecontroleerd op een mogelijke interactie met de actuele medicatie die de patiënt
voorgeschreven heeft gekregen en wordt de voorschrijver gewaarschuwd voor het
risico van de mogelijke geneesmiddeleninteractie. Het moment van voorschrijven
is immers een belangrijk moment om na te denken over de geschiktheid van
het geneesmiddel voor de individuele patiënt en hierbij wordt ook het risico op
bijwerkingen door een mogelijke interactie tussen geneesmiddelen in overweging
genomen. Op het moment van voorschrijven kan de arts echter alleen besluiten
om het gebruik van geneesmiddelen te stoppen dan wel voort te zetten, terwijl
de combinatie van twee interacterende geneesmiddelen niet schadelijk hoeft te
zijn als klinische parameters en biomakers van de patiënt adequaat gemonitord
worden tijdens het gebruik van de geneesmiddelen. Dit betekent dat de arts op het
moment van voorschrijven moet beslissen over wat te monitoren, wanneer en hoe
vaak. Het is gebleken dat richtlijnen voor geneesmiddeleninteracties hier vaak geen
duidelijkheid over geven.
Ook is gebleken dat het risico van een mogelijke geneesmiddeleninteractie vaak
niet gemonitord wordt bij patiënten in de ambulante zorg. Het is de verwachting
dat het risico van een mogelijke geneesmiddeleninteractie beter gemonitord
wordt bij patiënten die in het ziekenhuis opgenomen zijn, enerzijds vanwege de
beschikbaarheid van laboratorium- en andere monitoringsfaciliteiten en anderzijds
omdat de ziekte van de opgenomen patiënt dit noodzakelijk maakt. De duur van
de gemiddelde ziekenhuisopname is echter kort en bijwerkingen ten gevolge van
mogelijke geneesmiddeleninteracties kunnen daarom ook pas na ontslag ontstaan.
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Dat betekent dat de monitoring zou moeten doorgaan nadat de patiënt ontslagen
is en dat relevante informatie over zowel het geneesmiddelengebruik als de
monitoring tijdens het ontslag moet worden overgedragen aan de eerstelijns zorg.
Naast de verschillen in setting (ziekenhuis of thuis), kan de risicomitigerende
strategie ook afhangen van de karakteristieken van de individuele patiënt.
Eigenschappen van geriatrische patiënten verschillen van die van kinderen, en
beide verschillen weer van die van Intensive Care (IC) patiënten. IC patiënten zijn in
het algemeen ernstiger ziek waardoor hun capaciteit om fysiologische verstoringen
te kunnen compenseren verstoord kan zijn. Ondanks de intensieve monitoring
van deze patiënten, is de behandeling complex en risicomitigerende strategieën
verdienen daarom extra aandacht.
Hoofdstuk 2 en 3 beschrijven de frequentie en de klinische relevantie van
monitoring van geneesmiddelentherapie met laboratorium markers van in het
ziekenhuis opgenomen patiënten. Speciale aandacht wordt besteed aan mogelijke
geneesmiddeleninteracties, kalium en patiënten die opgenomen zijn op de
Intensive Care.
Het is bekend dat een computersysteem dat veel niet-specifieke waarschuwingen
genereert het veiligheidsdoel van deze waarschuwingen ondermijnt.
Computersystemen met een basale vorm van interactiebewaking houden geen
rekening met individuele patiëntkarakteristieken. Ze signaleren mogelijke
geneesmiddeleninteracties wel nauwkeurig (hoge sensitiviteit) maar de
waarschuwingen zijn vaak niet relevant voor de individuele patiënt (lage
specificiteit). Hoofdstuk 2 laat zien in welke omvang dit probleem heeft binnen
de ziekenhuispopulatie. Potentiële geneesmiddeleninteracties bleken veel voor te
komen bij ziekenhuispatiënten. Van alle patiënten die in het ziekenhuis opgenomen
waren, had 25% minimaal 1 potentiële geneesmiddeleninteractie (hoofdstuk 2.1).
Dit percentage was ruim twee maal zo hoog (54%) bij patiënten die opgenomen
waren op de IC (hoofdstuk 2.2). Het verschil met de algemene ziekenhuispopulatie
werd vooral veroorzaakt door het grotere aantal geneesmiddelen dat de IC
patiënten gebruikten. Hoofdstuk 2.1 en 2.2 laten zien dat slechts een beperkt aantal
geneesmiddelencombinaties verantwoordelijk was voor het grootste deel van de
potentiële geneesmiddeleninteracties. Dit betrof zowel algemene verpleegafdelingen
als IC. Op de algemene verpleegafdelingen waren de 10 meest voorkomende
interacties verantwoordelijk voor 53% van de potentiële geneesmiddeleninteracties
en op de IC voor 79% van de potentiële geneesmiddeleninteracties. In hoofdstuk 2.2
is nader aandacht besteed aan de geneesmiddeleninteracties die optraden bij IC
Samenvatting
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patiënten omdat veel geneesmiddelen die op de IC worden gebruikt een hoog
intrinsiek risico hebben en IC patiënten, door de ernst van hun ziekte, kwetsbaarder
zijn voor de negatieve effecten van potentiële geneesmiddeleninteracties. De
potentiële geneesmiddeleninteracties op de IC bleken inderdaad vooral betrekking
hebben op de specifieke hoog-risico medicamenten die op de IC gebruikt worden.
De top-10 van meest voorkomende potentiële geneesmiddeleninteracties op
de IC verschilt daarom van die op algemene verpleegafdelingen. De meeste
geneesmiddeleninteracties gaven een verhoogde kans op bijwerkingen. Een kans
op verminderde effectiviteit kwam minder vaak voor. De nationale richtlijn voor
het managen van potentiële geneesmiddeleninteracties adviseerde meestal om te
monitoren. Deze monitoring bestond uit het meten van laboratorium waarden,
klinische monitoring op toxiciteit of verminderde effectiviteit en het monitoren
van fysische parameters zoals het opnemen van een elektrocardiogram en het
meten van de bloeddruk. Monitoring van laboratorium markers werd het meest
frequent geadviseerd, namelijk bij 48,6% van de patiënten met een interactie op een
algemene verpleegafdeling en bij 51,6% van de patiënten met een interactie op de IC.
Deze resultaten laten zien dat een geavanceerd klinisch beslissingsondersteunend
systeem waarbij laboratorium waarden gekoppeld worden aan voorschrijfdata
een belangrijk hulpmiddel kan zijn om het geneesmiddeleninteractiesysteem
specifieker te maken.
Hoofdstuk 3.1 laat zien dat bij 50% van de patiënten tijdens opname een meting
gedaan is voor serumkalium, -natrium en/of –creatinine spiegels. Deze drie
laboratoriummarkers zijn vaak betrokken bij de strategieën om bijwerkingen
van geneesmiddelentherapie te voorkomen. Het is echter niet bekend wat de
determinanten zijn om deze laboratoriummarkers te meten. Als één van de drie
laboratoriummarkers gemeten werd, bleek 90% van de patiënten ook een meting
te hebben voor de andere twee laboratoriummarkers. Dit suggereert dat de drie
laboratoriummarkers vaak tegelijkertijd gemeten worden. In het algemeen waren
patiënt- en opname gerelateerde factoren zoals leeftijd en behandelspecialisme
belangrijkere voorspellers voor het meten van deze laboratoriummarkers dan
het gebruik van specifieke geneesmiddelen. Het gebruik van medicatie waarvoor
controle van een van deze laboratoriummarkers geadviseerd wordt, had nauwelijks
invloed op het aantal patiënten waarbij een laboratoriumparameter gemeten werd.
Bij patiënten die een geneesmiddel gebruikten dat door de nier geklaard wordt
en waarvoor de nationale standaard een doseringsaanpassing adviseert als de
nierfunctie verminderd is (VNF geneesmiddelen), is niet vaker een serumcreatinine
meting gedaan dan bij patiënten die geen VNF geneesmiddel gebruikten. Het
voorschrijfsysteem signaleerde echter niet als een VNF geneesmiddel werd
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voorgeschreven en adviseerde dus ook niet om een serum creatinine spiegel
te meten. Signalering zou tot een hoog aantal meldingen hebben geleid, 77%
van de patiënten gebruikte tijdens opname in het ziekenhuis namelijk een
VNF geneesmiddel. Medicatie waarvoor wel vaker gemeten werd, waren de
kaliumsupplementen. Wanneer patiënten kaliumsupplementen gebruikten, nam
het percentage patiënten met een kaliummeting toe van 41 naar 99%.
Om vast te stellen of de uitslag van de metingen tijdens ziekenhuisopname ook
geschikt zijn voor de verificatie van medicatie bij ontslag, is het tijdstip van de
laatste meting van deze laboratorium markers bepaald. Het percentage patiënten
waarbij binnen 48 uur vóór ontslag nog een meting is gedaan bleek minder dan 25%
te zijn. Dit suggereert dat verbetering mogelijk is in de aanvraag en communicatie
van relevante testuitslagen aan de zorgverlener voorafgaand aan een overgang van
zorglijn.
Kalium is betrokken bij één van de meest frequent voorkomende interacties
tussen geneesmiddelen, namelijk die tussen twee kaliumverhogende
geneesmiddelen. Omdat deze interactie mogelijk tot ernstige gevolgen kan leiden,
is in hoofdstuk 3.2 de monitoring van deze elektrolyt nader bestudeerd. In
aanwezigheid van een voorschrijfsysteem met signaleringsfunctie voor potentiële
geneesmiddeleninteracties, bleken kaliumspiegels iets vaker te worden gemonitord
als patiënten een interactie hadden tussen twee of meer kaliumverhogende
geneesmiddelen ten opzichte van patiënten die maar één kaliumverhogend
geneesmiddel gebruikten (67% versus 58% van de patiënten). Of dit verschil kan
worden toegeschreven aan de signaleringsfunctie van het voorschrijfsysteem was
niet vast te stellen. De basiskenmerken van patiënten in de interactie-groep verschilde
namelijk van die van de monotherapie-groep. Ondanks dat de arts tijdens het
voorschrijven een melding kreeg met het advies om kaliumspiegels te monitoren,
bleek bij 33% van de patiënten met een interactie tussen kaliumverhogende
geneesmiddelen geen kalium meting gedaan en 10% van de patiënten ontwikkelde
een hyperkaliëmie.
Bij het starten van geneesmiddelen wordt vaak geadviseerd om te monitoren
maar er is weinig aandacht voor de rol van monitoring als geneesmiddelen
worden gestopt, zelfs als deze geneesmiddelen onderdeel zijn van een potentiële
geneesmiddeleninteractie. Patiënten met hartfalen krijgen vaak een combinatie van
remmers van het renine-antiotensine-aldosteron systeem (RAS-i), spironolactone en
diuretica. Omdat deze geneesmiddelen een tegengesteld effect hebben op de serum
kaliumspiegels, kan het stoppen van een van deze geneesmiddelen gevolgen hebben
Samenvatting
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op de kalium spiegels. Als de kaliumspiegels hierdoor buiten de normaalwaarden
vallen, kan dit bij deze kwetsbare patiëntgroep leiden tot een kalium gerelateerde
hartritmestoornis. In hoofdstuk 3.3 wordt beschreven wat er gebeurt als één van
deze middelen wordt gestopt. Bij 59% van patiënten die zowel een kalium-verhogend
als een kalium-verlagend geneesmiddel gebruikten en stopten met het gebruik
van de kalium-verlager, nam de kaliumspiegel toe. Bij 3,2% van deze patiënten
ontwikkelde zich een hyperkaliëmie (serum kaliumspiegels > 5,5 mmol/L). Als
het kalium-verhogende middel werd gestopt, nam de kaliumspiegel bij 70% van
de patiënten af en ontwikkelde 17% een hypokaliëmie (serum kaliumspiegels
< 3,5 mmol/L). Deze resultaten laten zien dat monitoring belangrijk is bij zowel
starten als stoppen van therapie met geneesmiddelen.
Kaliumspiegels kunnen ook worden beïnvloed door het gebruik van insuline.
Insuline wordt toegepast om glucosespiegels te verlagen en is een belangrijk
onderdeel van de strikte glucose controle (SGC) protocollen op de Intensive Care.
De invloed van insuline op glucosespiegels is ook uitgebreid in de literatuur
beschreven. In welke mate de SGC protocollen invloed hebben op de kaliumspiegels
en mogelijk kunnen leiden tot een kalium gerelateerde bijwerking, is nauwelijks
onderzocht. SGC protocollen zijn daarom niet duidelijk over wanneer en hoe vaak
kaliumspiegels moeten worden gecontroleerd. De resultaten van hoofdstuk 3.4 laten
zien dat SGC niet geassocieerd was met een toegenomen risico op hypokaliëmie.
Dit ondanks de bevinding dat zowel hoge en lage gemiddelde glucose en kalium
waarden en hoge variabiliteit van glucose en kaliumwaarden geassocieerd waren
met een hoge mortaliteit. Het is daarom wel van belang zowel hoge als lage glucose
en kaliumspiegels evenals een hoge variabiliteit van glucose en kaliumspiegels te
voorkomen.
Alle onderzoeken die in dit proefschrift beschreven zijn, tonen aan dat het belangrijk
is om laboratoriummarkers te meten als onderdeel van de risicomitigerende
strategie van geneesmiddelengebruik door in het ziekenhuis opgenomen patiënten.
De afzonderlijke onderzoeken concentreren zich op de rol van monitoring
bij patiënten met een geneesmiddeleninteractie en kwetsbare patiënten die
opgenomen zijn op de Intensive Care. In het laatste hoofdstuk van dit proefschrift,
hoofdstuk 4, worden de resultaten van deze onderzoeken in een breder perspectief
geplaatst aan de hand van drie thema’s. In het eerste thema wordt besproken welke
rol een geavanceerd klinisch beslissingsondersteunend systeem (aCDS) kan spelen in
het verhogen van de specificiteit van een klinisch beslissingsondersteunend systeem
voor geneesmiddeleninteracties. Een basaal klinisch beslissingsondersteunend
systeem (CDS) op basis van potentiële geneesmiddeleninteracties leidt immers tot
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een hoog aantal niet-specifieke waarschuwingen (waarschuwingen werden gegeven
bij 8-10% van de voorschriften) terwijl slechts een beperkt aantal combinaties
van geneesmiddelen verantwoordelijk was voor een groot aantal meldingen. Het
koppelen van patiënt-specifieke monitoringsgegevens aan de algemene gegevens
over geneesmiddelen speelt hierin een belangrijke rol. Ook wordt aandacht
besteed aan de wijze waarop een geavanceerd CDS systeem de naleving van
monitoringsrichtlijnen zou kunnen verbeteren. De risicomitigerende strategie
die in de voorgaande hoofdstukken het meest geadviseerd werd bestond immers
uit monitoring, maar de monitoringsrichtlijnen werden niet altijd nageleefd.
Op de intensive care worden patiënten echter al uitgebreid gemonitord. In het
tweede thema worden daarom de mogelijkheden besproken van een klinisch
beslissingsondersteunend systeem ten behoeve van interactiebewaking binnen de
setting van de intensive care. Tot slot wordt in dit laatste hoofdstuk ingegaan op de
rol van de (ziekenhuis)apotheker en/of klinisch farmacoloog indien gebruik wordt
gemaakt van een geavanceerd klinisch beslissingsondersteunend systeem op de IC.
Naar aanleiding van de onderzoeken in het proefschrift worden de volgende
aanbevelingen gedaan:
− Neem duidelijke instructies op in richtlijnen over wat te monitoren, bij wie
en wanneer, vooral bij geneesmiddelen die de kaliumspiegel beïnvloeden;
− Integreer zowel voorschrijfdata als laboratorium data en andere klinische
patiënt gegevens in een klinisch beslissingsondersteunend systeem om de
specificiteit van interactiesignalen te verhogen;
− Ontwikkel klinische beslissingsondersteunende systemen verder zodat ze
ook de naleving van monitoringsrichtlijnen ondersteunen;
− Ontwikkel klinische beslissingsondersteunende systemen verder zodat
zij niet alleen waarschuwen voor risico’s als geneesmiddelen worden
voorgeschreven, maar ook als gebruik van geneesmiddelen stopt.
− Neem in de richtlijnen op dat laboratoriummarkers kort voor ontslag
worden gemeten als deze betrokken kunnen zijn bij het ontstaan van
bijwerkingen na ontslag;
− Ontwikkel klinische beslissingsondersteunende systemen die specifiek
gericht zijn op de behoefte op de IC. Opties hiervoor zijn om het systeem
Samenvatting
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te laten signaleren als biomarkers afwijken in aanwezigheid van een
geneesmiddeleninteractie en/of de alert door een andere zorgprofessional
te laten beoordelen op klinische relevantie;
− Ontwikkel klinische beslissingsondersteunende systemen die rekening
houden met de klinische setting en expertise van de professional zodat de
specificiteit van geneesmiddeleninteractiebewaking verbetert
− Ontwikkel een specifieke interactiestandaard voor de IC
Concluderend is uit het onderzoek van dit proefschrift gebleken dat slechts
een beperkt aantal geneesmiddelencombinaties verantwoordelijk is voor het
grootste deel van de potentiële geneesmiddeleninteracties en dat het risico van
het grootste deel van deze potentiële geneesmiddeleninteracties kan worden
gemitigeerd door de patiënt goed te monitoren, met name door het meten van
laboratoriummarkers. Ook is gebleken dat de mate waarin monitoringsrichtlijnen
worden nageleefd variëren met de eigenschappen van de patiënt, de setting en
het soort medicatie dat wordt gebruikt. Op de Intensive Care afdeling worden
patiënten uitgebreid gemonitord. Patiëntveiligheid kan worden vergroot door
klinische beslissingsondersteunende systemen te ontwikkelen die beter gebruik
kunnen maken van laboratoriummarkers en die het naleven van richtlijnen om
laboratoriummarkers te meten kunnen verbeteren.
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List of co-authors
Affiliations at the time the research was conducted
Presented in alphabetical order
Dr. M. (Maarten) J. ten Berg
Department of Clinical Chemistry and Haematology, University Medical Centre
Utrecht, Utrecht, the Netherlands
Dr. O. (Olaf) L. Cremer
Department of Intensive Care, University Medical Centre Utrecht, Utrecht, the
Netherlands
Prof. dr. A. (Toine) C.G. Egberts
Department of Clinical Pharmacy, University Medical Center Utrecht, the
Netherlands & Department of Pharmacoepidemiology and Clinical Pharmacology,
Utrecht Institute for Pharmaceutical Sciences, Utrecht University, The Netherlands
Drs. L. (Lieke) L.M. van Harssel
Department of Hospital Pharmacy, Diakonessenhuis, Utrecht, the Netherlands
Dr. G. (Gerard) W.K. Hugenholtz
Department of Hospital Pharmacy, Diakonessenhuis, Utrecht, the Netherlands
Drs. E. (Emile) M. Kuck
Department of Hospital Pharmacy, Diakonessenhuis, Utrecht, the Netherlands
Dr. D. (Dylan) W. de Lange
Department of Intensive Care, University Medical Centre Utrecht, Utrecht, the
Netherlands
Prof. dr. W. (Wouter) W. van Solinge
Department of Clinical Chemistry and Haematology, University Medical Centre
Utrecht, Utrecht, the Netherlands & Department of Pharmacoepidemiology and
Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht
University, The Netherlands
List of co-authors
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Dr. J. (Jeannette) E.F. Zwart- van Rijkom
Department of Clinical Pharmacy, University Medical Center Utrecht, the
Netherlands & Department of Pharmacoepidemiology and Clinical Pharmacology,
Utrecht Institute for Pharmaceutical Sciences, Utrecht University, The Netherlands
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List of publications related to this thesis
Uijtendaal EV, Harssel LL, Hugenholtz GW, Kuck EM, Zwart-van Rijkom JE, Cremer
OL, Egberts TC. Analysis of Potential Drug-Drug Interactions in Medical Intensive
Care Unit Patients. Pharmacotherapy 2014; 34, 213-219.
Uijtendaal EV, Zwart-van Rijkom JE, van Solinge WW, Egberts TC. Serum
Potassium Influencing Interacting Drugs: Risk-modifying strategies also needed at
discontinuation. Ann Pharmacother 2012; 46: 176-182.
Uijtendaal EV, Zwart-van Rijkom JE, van Solinge WW, Egberts TC. Frequency of
laboratory measurement and hyperkalaemia in hospitalised patients using serum
potassium concentration increasing drugs. Eur J Clin Pharmacol. 2011; 67: 933-940.
Zwart-van Rijkom JE, Uijtendaal EV, ten Berg MJ, van Solinge WW, Egberts AC.
Frequency and nature of drug-drug interactions in a Dutch university hospital. Br J
Clin Pharmacol 2009; 68: 187-193.
Other publicationsvan Harssel LM, Uijtendaal EV, Hugenholtz GWK, Kuck EM, Zwart-van Rijkom JEF,
Cremer OL, Egberts TCG. Frequentie en aard van interacties tussen geneesmiddelen
bij Intensive care patiënten. Pharm Weekbl 2012; 6: a1218.
Verstraete E, Veldink JH, Huisman MH, Draak T, Uijtendaal EV, van der Kooi AJ,
Schelhaas J, de Visser M, van der Tweel I, van den Berg LH. Lithium lacks effect on
survival in amyotrophic lateral sclerosis: a phase IIb randomised sequential trial. J
Neurol Neurosurg Psychiatry 2012; 83: 557-564.
Zwart-van Rijkom J, Uijtendaal E, Ten Berg M, Van Solinge W, Egberts A. Frequency
and nature of drug-drug interactions in a Dutch university hospital. Br J Clin
Pharmacol 2010; 70 :618.
van de Vlekkert J, Hoogendijk JE, de Haan RJ, Algra A, van der Tweel I, van der
Pol WL, Uijtendaal EV, de Visser M; Dexa Myositis Trial. Oral dexamethasone pulse
therapy versus daily prednisolone in sub-acute onset myositis, a randomised clinical
trial. Neuromuscul Disord 2010; 20: 382-389.
List of publications
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Piepers S, Veldink JH, de Jong SW, van der Tweel I, van der Pol WL, Uijtendaal EV,
Schelhaas HJ, Scheffer H, de Visser M, de Jong JM, Wokke JH, Groeneveld GJ, van
den Berg LH. Randomized sequential trial of valproic acid in amyotrophic lateral
sclerosis. Ann Neurol 2009; 66: 227-234.
de Smet AM, Kluytmans JA, Cooper BS, Mascini EM, Benus RF, van der Werf TS, van
der Hoeven JG, Pickkers P, Bogaers-Hofman D, van der Meer NJ, Bernards AT, Kuijper
EJ, Joore JC, Leverstein-van Hall MA, Bindels AJ, Jansz AR, Wesselink RM, de Jongh
BM, Dennesen PJ, van Asselt GJ, te Velde LF, Frenay IH, Kaasjager K, Bosch FH, van
Iterson M, Thijsen SF, Kluge GH, Pauw W, de Vries JW, Kaan JA, Arends JP, Aarts LP,
Sturm PD, Harinck HI, Voss A, Uijtendaal EV, Blok HE, Thieme Groen ES, Pouw ME,
Kalkman CJ, Bonten MJ. Decontamination of the digestive tract and oropharynx in
ICU patients. N Engl J Med 2009; 360: 20-31.
de Visser M, van de Vlekkert J, Hoogendijk JE, de Haan RJ, Algra A, van der Tweel I,
van der Pol WL, Uijtendaal EV. TO5 Oral dexamethasone pulse therapy versus daily
prednisolone in subacute inflammatory myopathies: A randomised clinical trial.
Neuromuscul Disord 2008; 18: 833.
Kneyber MC, van Woensel JB, Uijtendaal E, Uiterwaal CS, Kimpen JL; Dutch
Antibiotics in RSV Trial (DART) Research Group. Azithromycin does not improve
disease course in hospitalized infants with respiratory syncytial virus (RSV) lower
respiratory tract disease: a randomized equivalence trial. Pediatr Pulmonol 2008;
43: 142-149.
Uijtendaal EV, Vingerhoets RW, van Orshoven NP, Schobben AFAM, Jansen PAF.
Postprandial hypotension in elderly: effectiveness of midodrine and desmopressin
vs placebo assessed with the portapress; a pilot study. Br J Clin Pharmacol 2006; 62:
732-733.
Uijtendaal EV, Rademaker CM, Schobben AF, Fleer A, Kramer WL, van Vught AJ, van
Dijk A. Once-daily versus multiple-daily gentamicin in infants and children. Ther
Drug Monit 2001; 23: 506-513.
Uijtendaal EV, Rademaker CMA, Schobben AFAM, Fleer A. Aminoglycosides once
daily in children: the need for more evidence. Rev Med Microbiol 2000; 11, 87-100.
Bekers O, Uijtendaal EV, Beijnen JH, Bult A, Underberg WJM. Cyclodextrins in the
pharmaceutical field. Drug Dev Ind Pharm 1991; 17: 1503-1549.
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Dankwoord
Mama, kom je vandaag ook helpen bij de crea-middag op school? Nee, nog even
niet, mama moet nog even wat afmaken. Wat dan? Een boekje. Wat voor een boekje?
Een boekje over mama’s werk. En als het boekje klaar is? Dan gaan hooggeleerde
heren en dames hierover nog vragen stellen. Hooggeleerde heren? Zoals bij Harry
Potter? Nou, daar hadden ze wel oren naar! Zo is het wel een tijdje gegaan, vooral
in het laatste jaar, tijdens de eindsprint. Weekendjes zeilen met het gezin werden
spaarzaam en kindervakantieweken werden gevuld met sportkampen. Gelukkig
vonden ze het altijd leuk om te gaan en hebben hierdoor geleerd dat je met de
juiste sportmentaliteit de finish wel haalt! Zo geldt dat nu ook voor mij: de finish
is in zicht.
Net als bij topsport, wordt promoveren in een bepaald stadium een onderdeel van
het leven, en daarin spelen veel mensen een rol.
Toine, jij kent het traject als geen ander. Je hebt me in de gelegenheid gesteld om
verlof op te nemen voor de eindsprint en ik vond het een bijzondere ervaring dat je
(zonder verlof) in hetzelfde tempo mee sprintte naar de finish. Voor de zesde keer
naar een stuk kijken dat weer gereviseerd was, vaak op tijden dat normale mensen
op één oor liggen, ik heb er respect voor!
Wouter, heerlijk zoals je kan kijken naar de “grote lijn”. Met een grote grijns op je
gezicht kon je vragen stellen als : “Waar doen we dit allemaal voor?” of “Hoe denk je
dat die andere zorgverlener dit leest?”. Het ging om de grote lijn, maar het zijn de
kleine dingen die het doen. Dank hiervoor!
Jeanette, copromotor. Ik bewonder je bijzondere vaardigheid om “concise” te
kunnen schrijven. Daarnaast kan je ook efficiënt werken, dat is ook nodig want
alleen met deze eigenschap red je het om een drukke baan, copromotorschap en
gezin te combineren. Fijn dat we met de warme band die we als collega’s hebben
opgebouwd verder mogen werken aan de toekomst van de apotheek.
Hanneke, wat vond ik het heerlijk dat je wilde meedenken over het onderzoek. En
dat meedenken, dat doe je graag! Je liet zien dat het belangrijk is om de vraagstelling
vooraf helder te hebben, want dan pas kon je de juiste data selecteren. Bovendien
bekeek je de vraag kritisch: Zou je niet beter zus of zo? Je hebt een belangrijke
inbreng gehad. Ik waardeer het daarom enorm dat je ook mijn paranimf wil zijn!
Dankwoord
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Marcel, collega van het eerste uur, oud-kamergenoot, vriend. Op jou kan ik bouwen.
Veel woorden zijn hiervoor niet nodig. Fijn dat je mijn andere paranimf wil zijn.
Monique, Mo, fijn dat je de honneurs voor de IC (-apotheek) hebt waargenomen
tijdens mijn verlof. Je denkt IC, je ademt IC en bent eigenlijk gewoon IC. Ik kon het
werk dus met een gerust hart aan je overlaten.
Collega’s, ook dank aan jullie. Het werk dat bleef liggen moest toch verdeeld
worden en jullie hebben dat toch gefixt! Barbara, Erik, met z’n drieën deden we de
eindsprint. Ik heb nooit het gevoel gehad dat we een wedstrijd deden om de eerste
plaats. Integendeel: waar nodig hielpen we elkaar als echte teamgenoten zodat we
alle drie de finish konden halen.
Vrienden, zonder jullie een voor een te noemen, dank voor jullie geduld. Afspraakjes
zijn nog wel eens uitgesteld geweest, maar ze komen er weer aan hoor! Lot, dank
dat de kids altijd bij jou terecht konden. Caby, gelukkig waren er nog momenten
tijdens de hockeytraining van de jongens om bij te praten. Nu wordt het tijd voor
onze eigen sportmoment!
Lieve Heinz, ondanks jouw drukke baan, heb je een niet te onderschatten rol
gespeeld in de ondersteuning thuis. Waar nodig was je mijn luisterend oor,
meelezer en brede schouder. Ons bootje heeft tot jouw leedwezen erg veel aan de
kant gelegen. Gelukkig is Fred er nog die door weer en wind met je mee wil. Na
het behalen van een mijlpaal bepalen we echter steeds weer een nieuwe koers en
daardoor is het nooit saai. Ik ben benieuwd wat de nieuwe koers gaat brengen!
Pap, mam, jullie hebben gezorgd voor de juiste basis. Jullie hebben jezelf altijd
weggecijferd zodat wij, Raymond en ik onszelf konden ontplooien. Jullie zijn altijd
mijn trouwste supporters geweest en hebben er altijd in geloofd dat ik de finish zou
halen. Ik ben dan ook heel blij dat jullie hiervan getuige mogen zijn. Ik draag dit
boekje dan ook op aan jullie!
Tot slot, Nienke en Tjebbe, eindelijk is het dan zover. De finish is in zicht: op naar
Zweinstein!
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Curriculum vitae
Esther Véronique Uijtendaal was born in ’s-Hertogenbosch, the Netherlands on
April 1, 1967. She graduated Atheneum at the “Sint Janslyceum” in ‘s-Hertogenbosch
in 1985, and subsequently started her studies Pharmaceutical Sciences at Utrecht
University. She obtained her Master of Science degree in pharmacy in 1991 and in
1993 she completed an additional-master program in policy and management at
the Centre of Policy and Management of the University Utrecht (currently Utrecht
University School of Governance (USG)). In 1993 she also completed her PharmD
exam.
She started her professional career as a pharmacist at “Apotheek ‘t Eiland”
in Leerdam, pharmaceutical wholesaler “OPG Medico” in Alkmaar and the
“Waterland Ziekenhuis” in Purmerend. In 1995 she started working as a pharmacist
at the University Medical Centre in Utrecht (UMCU) to implement the electronic
prescribing system “Medicator” and in 1996 she started her training to become
a hospital pharmacist. After finishing her training in 2000, she continued her
professional career as a hospital pharmacist in the UMCU with special interest in
clinical trials. She registered as a clinical pharmacologist in 2007 and is a member
of the medical ethical review board of the UMCU since 2007. In 2009 she started her
PhD research. She combined this PhD thesis with her work as a hospital pharmacist
with special interest in intensive care medicine. Currently she still works as a
hospital pharmacist and clinical pharmacologist in the UMCU with special interest
in vital functions.
Esther lives together with Heinz. They are the proud parents of their daughter
Nienke (2003) and son Tjebbe (2006).
M
onitorin
g drug th
erapy in
hosp
italized patien
ts Esth
er Uijten
daal 2014
Monitoring drug therapy in hospitalized patients
Esther Uijtendaal
UITNODIGING
Voor het bijwonen van deopenbare verdedigingvan het proefschriftt
Monitoring drug therapy in hospitalized
patients
Dinsdag 2 december 2014om 16:15 uur
in het Academiegebouw,Universiteit Utrecht
Domplein 29 te Utrecht
Feestelijke borrel na afloop van de promotie in het
Academiegebouw
Esther UijtendaalOverboslaan 20
3722 BL [email protected]
Paranimfen:
Marcel [email protected]
06-52803009
Hanneke den [email protected]
06-52097419
M
onitorin
g drug th
erapy in
hosp
italized patien
ts Esth
er Uijten
daal 2014
Monitoring drug therapy in hospitalized patients
Esther Uijtendaal
UITNODIGING
Voor het bijwonen van deopenbare verdedigingvan het proefschriftt
Monitoring drug therapy in hospitalized
patients
Dinsdag 2 december 2014om 16:15 uur
in het Academiegebouw,Universiteit Utrecht
Domplein 29 te Utrecht
Feestelijke borrel na afloop van de promotie in het
Academiegebouw
Esther UijtendaalOverboslaan 20
3722 BL [email protected]
Paranimfen:
Marcel [email protected]
06-52803009
Hanneke den [email protected]
06-52097419