Leonique Niessen - Nictiz · Vul de code in. Workshopronde 1 17:05–17:50 uur BG 1e 1.1 EvT:...
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Transcript of Leonique Niessen - Nictiz · Vul de code in. Workshopronde 1 17:05–17:50 uur BG 1e 1.1 EvT:...
Leonique Niessendirecteur-bestuurder Nictiz
Tom van ‘t HekDagvoorzitter
Programma16:10 uur: Paneldiscussie
17:05 uur: Workshopronde 1
17:50 uur: Buffet en Netwerken
18:40 uur: Workshopronde 2
19:30 uur: Succesverhaal uit het buitenland
20:00 uur: Samen naar een hogere versnelling
20:45 uur: Afsluitende borrel
Filmpje
Paneldiscussie
JeroenWindhorst
GabrielleSpeijer
SylviaVeereschild
IrisVerberk
MariëtteWillems
Workshopronde 117:05– 17:50 uur
BG 1e
1.1 EvT: introductie1.2 Hands-on use1.3 Large Scale Analysis of EHR1.4 Gestructureerde vastlegging
1.5 eOverdracht1.6 Taal van de huisarts1.7 Navigeer door eigen data1.8 EPD bouwen
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Buffet en netwerken17:50-18:40
Workshopronde 218:40 – 19:25 uur
BG 1e
2.1 Registratie voor hergebruik2.2 Beslisondersteuning verpl.2.3 Kwaliteitsregistraties2.4 Live data-analyse
2.5 EPD bouwen2.6 Semantisch interoperabiliteit2.7 EvT voor patiënt2.8 EvT vanuit zorgverzekeraars
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W. Scott CampbellUniversity of Nebraska
Medical Center
SNOMED CT in Use for Clinical and Translational ResearchScott Campbell, MBA, PhDConnection and Innovation with SNOMEDUtrecht, The NetherlandsFebruary 13, 2020
Disclosures
1. Dr. W. Scott Campbell and Dr. James R. Campbell partially supported by NIH Award: U01HG009455; Patient Centered Outcomes Research Institute (PCORI) Award CDRN-1306-04631); Funding from UNMC Departments of Pathology and Microbiology and Internal Medicine
Questions that need answers• Find all patients diagnosed for lower GI adenocarcinoma
• Find all patients with Chronic Kidney Disease subsequently diagnosed with cancer. What is incidence rate at UNMC compared to state and national rates of cancer by type
• Find all cases of lower GI adenocarcinoma with MMR IHC results, MSI testing and any *RAS mutation by pathogenicity. Did these patients receive any gene targeted therapy AND did it followed FDA guidelines
• Find all positive opioid screens in urine or blood performed in ED and “repeat” patient encounters
• Find all bacterial infections in post-transplanted patients, identify frequency by organism, susceptibility patterns, associated treatments and subsequent outcomes
Primary Data – Bridge the Gap
Patients Phenotypes Outcomes Intervention Experiment Hypothesis
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Bedside Bench
EHR Data Lake?Use of clinical terminologies (standards) in a standard way: useful EHR data lakes
Characterized data is cleaner data
Dirty data needs to be cleaned
Not all data is clean or “good”
What is SNOMED CT?
• 19 Hierarchies
• (Clinical finding, Body Structure, Organism, Procedures, Pharmaceutical product)
• Stated definition• Polyhierarchy• Use of OWL axioms (DL)• Inferential views/Inferred views
• International standard (SNOMED) used in 40 nations plus affiliate licensees
Streptococcal pneumonia
Disease
Lung
Consol-Infiltration
Infectious
Genus StrepMorphologica
bnormality
Disease
Body Structure
Pathologic Process
Organism
IS A
SNOMED CT Example
Morphologicabnormality
Disease
Body Structure
Pathologic Process
Organism
IS A
Streptococcal pneumonia
Dx due to bacteria
LungGenus Strep
Disease
Dxrespiratory
stystem
Left Lung
RtLower lobe
Respiratory system
Strep. Pneum.
Bacteria
Group B
Inferred Relationships
Use cases in action
• Patient Care decision making
• Analytics for improvement in processes
• Antimicrobial Stewardship
Real Scenario example
• Patients have multiple morbid conditions with conflicting interventions for treatment
• Health care providers need data/support in to work with patients for best outcomes
Scenario:• Patients with Rheumatoid arthritis (RA) have an autoimmune disease that
“hurts”• Medications such Tumor Necrosis Factor Inhibitors (TNF inhibitors) such as
Humira help suppress the immune response that causes the joint pain.
• Many RA patients also have Chronic Obstructive Pulmonary Disease (COPD)• Heart/Lungs not working effectively to clear fluid from lungs in oxygen
exchange process.• Increased risk of lung infections
What is the risk to the patient to get a form of lung infection when given tumor necrosis factor inhibitors?
How to answer the question
Need to know how many patients that:
• Have any form of RA AND any form of COPD
How many of those patients
• Have/have not used a TNF inhibitor
• How many patients have/have not developed any infection within any part of the lung
Groups and calculations
No TNF use TNF use
No infection Group A: Number of patients with RA and COPD and did NOT use a TNF inhibitor and did NOT get a lung infection
Group B:Number of patients with RA and COPD who DID use a TNF inhibitor and did NOT get a lung infection
Infection Group C: Number of patients with RA and COPD who DID get a lung infection and did NOT use a TNF inhibitor
Group D:Number of patients with RA and COPD who DID get a lung infection and DID use a TNF inhibitor
#Group C/#Group A = % patients getting infection w/o TNF use => Baseline risk
#Group D/#Group B = % patients getting infection w/ TNF use => Intervention risk
Intervention risk/Baseline risk = Hazard ratio (absolute increased risk of infection)
UNMC population and database
• 2,159,467 million patients• Over 9 million SNOMED CT diagnoses and Past Medical
History• 30 million medications over 2.1 million patient visits events
• Database structure – GraphDB (Neo4J)• Full, inferred SNOMED CT concept model at core• All medications represented using RxNorm and Nation
Drug Codes (NDC) • Structured using US NLM definitions
The queries
•Find all patients with diagnosis or PMH of COPD and RA• Find all SCTIDs << 13645005 |Chronic obstructive lung disease (disorder)|• Find all SCTIDs << 69896004 |Rheumatoid arthritis (disorder)|
•Find all patients with COPD/RA using TNF inhibitors• TNF inhibitor ingredients, RxNORM coding
• 191831 Active infliximab INGREDIENT IN 20060501• 214555 Active Etanercept INGREDIENT IN 20060501• 327361 Active adalimumab INGREDIENT IN 20060501• 709271 Active certolizumab pegol INGREDIENT IN 20080601• 819300 Active golimumab INGREDIENT IN 20090601
•Find patients in previous queries that have a diagnosis with defining attributes• Finding site = << 39607008 |Lung structure (body structure)|• Pathologic process = << 441862004 |Infectious process (qualifier value)|• Note use of defining Attribute/Value pairs vs. ISA
Query Results
No TNF use TNF use
No Infection Group A: N = 669Number of patients with RA and COPD and did not use a TNF inhibitor and no infection.
Group B: N = 69Number of patients with RA and COPD who DID use a TNF inhibitor
Infection Group C: N = 35Number of patients with RA and COPD who DID get a lung infection and did not use a TNF inhibitor
Group D: N = 5Number of patients with RA and COPD who DID get a lung infection and DID use a TNF inhibitor
#Group C/#Group A = % patients getting infection w/o TNF use => Baseline risk[35/669]*100 = 5.2%#Group D/#Group B = % patients getting infection w/ TNF use => Intervention risk [5/69]*100 = 7.2%Intervention risk/Baseline risk = Hazard ratio (absolute increased risk of infection): Hazard ratio: 7.2%/5.2% = 1.35
Use cases in action
• Patient Care decision making
• Analytics for improvement in processes
• Antimicrobial Stewardship
Data in action: Existing study1. Identify all patients diagnosed with colorectal cancer between 2013 –
2016
2. UNMC and partner University
3. Metastatic disease at diagnosis or developed in time period
4. Identify all patients with biomarker testing
1. Microsatellite Instability or Mismatch Repair- (MLH1, MSH2, MSH6, PMS2)
2. BRAF and *RAS genes
5. Which patients received targeted therapies per guidelines
1. BRAF inhibitors, EGFR inhibitors, Immunotherapy
6. Pre-2015 requires manual chart review
7. 2015 – current can be computed
Cohort identificationInclusion Criteria Partner University UNMC
Patients with Stage IV CRC; ORStage I-III CRC AND metastatic disease advancement between 2013-2016
Internal cancer registry(analytic cases – US category)
ICD9 or 10 codes identifying metastatic disease for patients < Stage IV
Patients with lymphatic involvement only not included
Internal submissions to NAACCR (N. American Cancer Registry) for Stage identification
Metastases identified by a) SNOMED CT code in problem list; b) ICD – 9/10 encounter; and/or CEA levels > 25(Carcinoembryonic antigen)
Patients with lymphatic involvement only included
Confirmed by Chart Review N = 163 N = 223
Confirmed meeting with oncologist
N = 138 N = 75
Terminology Use (guide)
Patient Characteristic Standard Terminology
Values
Colorectal Cancer NAACCR NAACCR|400:{C18, C19, C20) Primary Site: ICD-O3 Colon and rectum
NAACCR NAACCR|523.3* Behavior code: ICD-O3 Malignant
Analytic Status NAACCR NAACCR 610.00-610.22
Metastatic (Problem List) SNOMED CT <<128462008|Secondary malignant neoplastic disease (disorder)|
Metastatic Disease Visit ICD-9-CM 197.5 - Secondary malignant neoplasm of large intestine and rectum
Metastatic Disease Visit ICD-10-CM C78.5 - Secondary malignant neoplasm of large intestine and rectum
Accuracy of patient identificationTrue Positive False Negative
NAACCR Stage IV 45 n/a
SNOMED CT 50 25
ICD-10-CM 27 38
CEA level > 25 49 26
False negative indicates no concept data available that supported metastasis.
Collectively, 100% identification
NAACCR Stage IV indicated inclusion. False negatives n/a
15 patients (20%) had no NAACCR stage record.
Chart Review vs. Standards
N = 14 % N = 1 % N = 7 % N = 12 % N = 1 % N = 6 %
KRAS Normal/Negative 14 100% 1 100% 3 43% 12 100% 1 100% 2 33%
Mutated/Positive 0 0% 0 0% 4 57% 0 0% 0 0% 4 67%
Not done 0 0% 0 0% 0 0% 0 0% 0 0% 0 0%
NRAS Normal/Negative 8 57% 0 0% 4 57% 7 58% 0 0% 4 67%
Mutated/Positive 2 14% 0 0% 1 14% 2 17% 0 0% 0 0%
Not done 4 29% 0 0% 2 29% 3 25% 0 0% 2 33%
HRAS Normal/Negative 10 71% 0 0% 5 71% 9 75% 0 0% 4 67%
Mutated/Positive 0 0% 0 0% 0 0% 0 0% 0 0% 0 0%
Not done 4 29% 0 0% 2 29% 3 25% 0 0% 2 33%
BRAF Normal/Negative 11 79% 1 100% 5 71% 10 83% 1 100% 5 83%
Mutated/Positive 2 14% 0 0% 1 14% 2 17% 0 0% 0 0%
Not done 1 7% 0 0% 1 14% 0 0% 0 0% 1 17%
MSI Stable 4 29% 0 0% 3 43% 3 25% 0 0% 2 33%
High 0 0% 0 0% 0 0% 0 0% 0 0% 0 0%
Not done 10 71% 1 100% 4 57% 9 75% 1 100% 4 67%
MMR Normal 7 50% 0 0% 5 71% 6 50% 0 0% 5 83%
Abnormal 0 0% 0 0% 0 0% 0 0% 0 0% 0 0%
Not done 7 50% 0 0% 2 29% 6 50% 0 0% 1 17%
Molecular tests and results
EMR
Data
Targeted therapies from EMR
Panitumumab
(Chart Review)
Cetuximab
(Chart Review)
Regorafenib
(Chart Review)
Panitumumab
(Computed)
Cetuximab
(Computed)
Regorafenib
(Computed)
Learnings from Study
• Chart review is time consuming!
• Reliable, consistent use of standards simplifies/shortens data abstraction time
• Chart data hard to find – Natural Language anywhere in the chart
• EMR data readily computable
• Medication orders/dispense events
• Problem lists
• Encounter diagnosis
• Laboratory results
• No misses (i.e., no missed events by computational analysis vs. chart review)
• Caveat (order, dispense but no delivery of med)
• Need clinicians to indicate patient response in discrete fashion
Use cases in action
• Patient Care decision making
• Analytics for improvement in processes
• Antimicrobial Stewardship
Antimicrobial Stewardship
• Coordinated program directing the appropriate use of antimicrobial drugs (e.g., antibiotics)
• Right drug to “bug”
• As narrowly targeted as possible– Save the broad spectrum, “heavy hitter” agents for only
those cases when necessary
• Goal is to reduce multidrug antibiotic resistant organisms (MDRO)
Clinical Reality
• Patients present with symptoms of infection
• Identification of organism and antibiotic susceptibility information not immediately available (up to 48 hours)
• Antibiotic therapy must be started based on empiric data
• How is empiric data generated?
Antibiogram• The antibiogram is a profile of the types of organisms and their
antibiotic susceptibility patterns exhibited
• Required in the US and broadly encouraged by WHO
• Specific guidelines for calculation
• Historically, manually calculated
• Resource intensive
• Infrequent…not necessarily up to date
Micro Laboratory Data Flow
Specimen
IdentificationGram Stain
Morphology
Culture dataSusceptibility to
antibioticMinimum inhibitory
concentrationS/I/R
Data Generation
Data Exchange
HL7 version 2-Real-time
-Incremental results
Parse Data
Database
Data Use and Storage
Preliminary reports
Final report
Sample Message Segment
OBX|3|CWE|41852-5^MICROORGANISM/AGENT XXX^LN^CULT^CultureResult:^SQLRR|1.2|60875001^Staphylococcus epidermidis(organism)^SCT^SEPI^Staphylococcus epidermidis^L^^v1^Staphylococcus epidermidis
SPM|1|S64958||122880004^Urine specimen obtained by clean catch procedure (specimen)^SCT^UCLN^Urine Clean Catch^L^^v1^Urine Clean Catch|||||||||||||date|date
OBX|3|SN|267-5^GENTAMICIN ISLT MIC^LN^GM^Gentamicin^SQLRR|1.1|<=^1|||SS|||F|||date|||50545-3^BACTERIAL SUSC PNL ISLT MIC^LN^MIC^MIC^SQLRR||date||||
Standards
• LOINC: ontology created from LOINC-SNOMED collaboration agreement defining LOINC terms within SNOMED concept model
– Lab orders and performed tests
• SNOMED CT
– Specimen: sample type, procedure for sampling
– Organism: enhanced with staining characteristics, morphology and metabolic features
• RxNorm: ontology developed by NLM employing SNOMED CT concept model
– Pharmaceutical products, Substances
How Used in Antibiogram
• Specimen aggregation–Type of blood specimens
• Line draw• Venipuncture
• Organism–Gram Stain (positive/negative)–Morphology–Metabolism (aerobic, anaerobic…)–Genus and species
Antibiogram for E coli - Inpatient units
Acknowledgements• College of American Pathologists –Raj Dash, MD; Alexis Carter, MD; Mark Routbort ,MD PhD; Mary Kennedy;
Monica de Baca, M; Sam Spencer,MD; Richard Moldwin, MD, PhD; George Birdsong, MD
• UNMC – James R. Campbell, MD, Allison Cushman-Vokoun, MD PhD, Tim Griener, MD, Jay Pedersen, Nick
Staffend
• Swedish Board of Health, Sweden - Daniel Karlsson, PhD, Keng-Ling Wallin, PhD, Carlos Moros (Karolinska
Institute)
• Royal College of Pathologists and eDigital Health (NHS) – Deborah Drake, Laszlo Iglali, MBBS; Brian Rous, MBBS
• SNOMED International Observable Project –Farzaneh Ashrafi, Ian Green,
Peter Hendler, MD
• International Collaboration on Cancer Reporting – David Ellis, MD; John Srigley, MD
Dr. W. Scott Campbell and Dr. James R. Campbell partially supported by NIH Award: 1U01HG009455; Patient
Centered Outcomes Research Institute (PCORI) Award CDRN-1306-04631); Funding from UNMC Departments of
Pathology and Microbiology and Internal Medicine
Questions
Dank voor uw aanwezigheid!
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