Using the Explorys Platform for Clinical
Effectiveness Research (CER) with
De-identified, Population Level Data
David C Kaelber, MD, PhD, MPH, FAAP, FACP
Associate Professor of Internal Medicine, Pediatrics, Epidemiology, and Biostatistics
Director of the Center for Clinical Informatics Research and Education
Chief Medical Informatics Officer
The MetroHealth System
Case Clinical and Translational Science Center (CTSC)
Case Western Reserve University
Disclosures
• I receive no compensation from Epic, although tens
of millions of dollars of institutional funds and my
academic career are committed to Epic .
• I have no financial relationship with Explorys, Inc.
The MetroHealth System was one of the first
Explorys, Inc. partners and contributes all of its
electronic health record data in exchange for use of
the Explorys Explore tool. And Explorys, Inc. seems
to be helping my academic career .
Case 1
Patient Characteristics association with Venous
Thromboembolic Events (VTEs) – A Cohort Study
using Pooled Electronic Health Record (EHR) data
• Relationship between weight, height, and blood clots
(venous thromboembolic events)
• Not CER Example
Kaelber, et al, JAMIA, e-published 3 July 2012
•
•
•
•
959,030 patients (vs 26,714 -> ~40 times more)
21,210 VTE patients (vs 451 -> ~50 times more)
12 year retrospective study (vs 14 years)
~2 months from idea to submission (vs 18 years)
Similar results with much higher power!
Not human studies research (No PHI; No IRB)!
Kaelber, et al, JAMIA, e-published 3 July 2012
Case 2
Azathioprine - A case study using pooled electronic
health record data and co-morbidity networks for
post-market drug surveillance
• Post-market surveillance of Azathioprine
– Anti tumor necrosis factor medication
• CER Example
Manuscript submitted and under review
Study Design
•
Design: A “prospective” cohort study (from a
retrospective cohort).
•
Setting: Explorys network of ~11 million patients (at
the time of the study).
•
Patients: All patients in the Explorys network who
were prescribed Azathioprine (AZA) and/or similar
medication(s).
•
Main Outcome Measures: Side effects from AZA (and
how side effects compare to other similar drugs).
Side Effects Investigates
Side Effect
Anemia
Cell lysis
Fever
Hepatotoxicity
Hepatotoxicity
Hypertension
Lab Value
Hemoglobin (Hgb)
Lactate dehydrogenase (LDH)
Temperature
AST, ALT
Total bilirubin (Bili)
Blood pressure (BP)
Nephrotoxicity
Neutropenia
Neutrophilia
Creatinine (Cr)
Neutrophil count
Neutrophil count
Abnormal Range
<11 g/dL
>190 IU/L
>37.8oF
AST>40 IU/L and ALT>40 IU/L
>1 mg/dL
Systolic >140 mm Hg
or Diastolic>90 mm Hg
>1.5 mg/dL
Count<57% or <2.5 cells/µl
Count>70%
Results
Control cohort administered one of 12 anti-rheumatic drugs.
Overlap is evident between the cohorts since controlling the
AZA cohort for the absence of the other 12 drug.
Drug Name (RxCUI)
Abatacept (614391)
Adalimumab (327361)
Azathioprine (1256)
Clioquinol (5942)
Etanercept (214555)
Homatropine (27084)
Hydroxychloroquine (5521)
Infliximab (191831)
Iodoquinol (3435)
Leflunomide (27169)
Methotrexate (6851)
Oxyquinoline (110)
Sulfasalazine (9524)
Total
Control Cohort
140 (0.1%)
2660 (2.1%)
3610 (2.8%)
110 (0.1%)
2490 (1.9%)
66170 (51.1%)
22900 (17.7%)
2880 (2.2%)
7350 (5.7%)
1460 (1.1%)
17710 (13.7%)
220 (0.2%)
5320 (4.1%)
129560
AZA Cohort
60 (0.4%)
650 (4.7%)
13890 (100.0%)
0 (0.0%)
250 (1.8%)
680 (4.9%)
2000 (14.4%)
1200 (8.6%)
80 (0.6%)
480 (3.5%)
1750 (12.6%)
0 (0.0%)
570 (4.1%)
13890
Results
% of patients with comorbidities induced by AZA. Diagonal
represents proportion of patients experiencing single side
effect. Cell color indicates relative risk of developing a
comorbidity (compared to other drug in class).
1.0
2°
effect
1° effect
Cr
Cr
AST/ALT
Bili
Neutropenia
Neutrophilia
Temp
BP
Hgb
LDH
11%
20%
15%
2%
4%
19%
6%
22%
29%
AST/
ALT
24%
14%
35%
2%
4%
16%
2%
20%
29%
1.5
Bili
18%
35%
14%
1%
8%
14%
5%
18%
29%
2.0
2.5
3.0
3.5
Relative Risk
Neutro- Neutropenia
philia
12%
29%
10%
25%
5%
50%
25%
0%
0%
45%
5%
22%
5%
13%
10%
46%
0%
71%
4.0
Temp
BP
Hgb
LDH
41%
30%
25%
2%
6%
12%
17%
40%
14%
47%
15%
30%
7%
12%
59%
29%
46%
29%
65%
50%
45%
6%
18%
54%
18%
28%
79%
24%
20%
20%
0%
8%
5%
3%
22%
61%
Results
AZA-induced comorbidity network showing links with
significantly increased risk relative to other anti-rheumatic drugs.
Lab measurements in green have an increased risk for
occurrence in patients taking AZA; grey nodes have a decreased
or non-significant risk. Size of a node corresponds to proportion
patients experiencing that side effect.
Study Conclusions
•
1st study of confirm anecdotal case reports in large
cohort.
•
Able to compare AZA to other drugs in class (CER).
•
Identified temporal relationships among side effects.
•
Identified possible mechanisms to screen for
impending renal dysfunction (anemia and increasing
LDH predict/preceed renal side effects).
•
Study performed by 3rd year Case Medical School
student as part of 4 week informatics rotation.
Discussion
De-identified Population Data
• Advantages
– Not human studies research (no IRB)
– No HIPAA issues (no security issues)
• Disadvantages
– Limited data analytic (statistical) tools
– Limited research questions
Keys to Using EHR Data
• Understanding Data Sources
• Corroborating Data/Findings
– Internal versus external corroboration
• Clinical Data versus Research Data
Understand your data sources, corroborate
your data/findings, and realize that the data
represents clinical practice.
EHR Data Quality
Type of data
Relative Quality
Very High
Demographic (age, gender, race/ethnicity)
Very High
Lab Results
Very High1
Prescriptions
Lots
of Signs
information desired for research is
not stored
High
Vital
inDiagnoses
the electronic
health
record
as
digital
data
during
Medium
(variable)
(ICD-9 codes)
routine clinical care.
Low
Family/PMH/Social History
???
Other
1- for prescriptions written; up to ~40% of prescriptions are never filled
Clinical Research Paradigm
Characteristic
Data
Infrastructure
Resources
Queries/Analysis
Self-Service
Old Paradigm
New Paradigm
siloed
aggregated
significant
none/minimal
days/weeks/
months
real-time/nearreal time
minimal
high
Researchers want quick, easy, access to “all” data themselves!
Clinical Research Implications
Characteristic
Old Paradigm
New Paradigm
Data
Separate Research
Database
Shared Research and
Clinic Database (EHR)
Time
1000+ hours
100+ hours
Money
100,000-1,000,000+
0-10,000+
People
Many
Few
Order of magnitude less time and money
with electronic health records.
EHR data and clinical research informatics tools
are creating a paradigm shift in CER.
THE FUTURE IS NOW!