1. 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

2. 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 .

3. 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

4. 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

5. 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

6. 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).

7. Side Effects Investigates
Lab Value
Abnormal Range
Anemia
Hemoglobin (Hgb)
<11 g/dL
Cell lysis
Lactate dehydrogenase (LDH)
>190 IU/L
Fever
Temperature
>37.8oF
Hepatotoxicity
AST, ALT
AST>40 IU/L and ALT>40 IU/L
Total bilirubin (Bili)
>1 mg/dL
Hypertension
Blood pressure (BP)
Systolic >140 mm Hg
or Diastolic>90 mm Hg
Nephrotoxicity
Creatinine (Cr)
>1.5 mg/dL
Neutropenia
Neutrophil count
Count<57% or <2.5 cells/µl
Neutrophilia
Count>70%

8. 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)
Control Cohort
AZA Cohort
Abatacept (614391)
140
(0.1%) 60
(0.4%)
Adalimumab (327361)
2660
(2.1%)
650
(4.7%)
Azathioprine (1256)
3610
(2.8%)
13890
(100.0%)
Clioquinol (5942)
110
(0.0%)
Etanercept (214555)
2490
(1.9%)
250
(1.8%)
Homatropine (27084)
66170
(51.1%)
680
(4.9%)
Hydroxychloroquine (5521)
22900
(17.7%)
2000
(14.4%)
Infliximab (191831)
2880
(2.2%)
1200
(8.6%)
Iodoquinol (3435)
7350
(5.7%) 80
(0.6%)
Leflunomide (27169)
1460
(1.1%)
480
(3.5%)
Methotrexate (6851)
17710
(13.7%)
1750
(12.6%)
Oxyquinoline (110)
220
(0.2%)
Sulfasalazine (9524)
5320
(4.1%)
570
Total
129560

9. 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° effect
2 °
effect

10. 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.

11. 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.

12. Discussion

13. 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

14. 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.

15. EHR Data Quality Type of data Relative Quality
Demographic (age, gender, race/ethnicity)
Very High
Lab Results
Prescriptions
Very High1
Vital Signs
High
Diagnoses (ICD-9 codes)
Medium (variable)
Family/PMH/Social History
Low
Other
???
Lots of information desired for research is not stored in the electronic health record as digital data during routine clinical care.
1- for prescriptions written; up to ~40% of prescriptions are never filled

16. Clinical Research Paradigm
Characteristic
Old Paradigm
New Paradigm
Data
siloed
aggregated
Infrastructure Resources
significant
none/minimal
Queries/Analysis
days/weeks/
months
real-time/near-real time
Self-Service
minimal
high
Researchers want quick, easy, access to “all” data themselves!

17. 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.

18. EHR data and clinical research informatics tools are creating a paradigm shift in CER.
THE FUTURE IS NOW!