Using the Explorys Platform for Clinical Effectiveness Research (CER) with De-identified, Population Level Data

David C Kaelber, MD, PhD, MPH, FAAP, FACPAssociate Professor of Internal Medicine, Pediatrics, Epidemiology, and Biostatistics

Director of the Center for Clinical Informatics Research and EducationChief Medical Informatics Officer

The MetroHealth SystemCase 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

• Relationship between weight, height, and blood clots (venous thromboembolic events)

• Not CER Example

Patient Characteristics association with Venous Thromboembolic Events (VTEs) – A Cohort Study using Pooled Electronic Health Record (EHR) data

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

• Post-market surveillance of Azathioprine– Anti tumor necrosis factor medication

• CER Example

Azathioprine - A case study using pooled electronic health record data and co-morbidity networks for

post-market drug surveillance

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 Lab Value Abnormal RangeAnemia Hemoglobin (Hgb) <11 g/dLCell lysis Lactate dehydrogenase (LDH) >190 IU/LFever Temperature >37.8oFHepatotoxicity AST, ALT AST>40 IU/L and ALT>40 IU/LHepatotoxicity Total bilirubin (Bili) >1 mg/dLHypertension Blood pressure (BP) Systolic >140 mm Hg

or Diastolic>90 mm HgNephrotoxicity Creatinine (Cr) >1.5 mg/dLNeutropenia Neutrophil count Count<57% or <2.5 cells/µlNeutrophilia Neutrophil count Count>70%

ResultsControl 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 (0.1%) 0 (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%) 0 (0.0%)

Sulfasalazine (9524) 5320 (4.1%) 570 (4.1%)Total 129560 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).

Cr AST/

ALT Bili Neutro-penia

Neutro-philia Temp BP Hgb LDH

Cr 11% 24% 18% 12% 29% 41% 47% 65% 24% AST/ALT 20% 14% 35% 10% 25% 30% 15% 50% 20%

Bili 15% 35% 14% 5% 50% 25% 30% 45% 20% Neutropenia 2% 2% 1% 25% 0% 2% 7% 6% 0% Neutrophilia 4% 4% 8% 0% 45% 6% 12% 18% 8%

Temp 19% 16% 14% 5% 22% 12% 59% 54% 5% BP 6% 2% 5% 5% 13% 17% 29% 18% 3%

Hgb 22% 20% 18% 10% 46% 40% 46% 28% 22% LDH 29% 29% 29% 0% 71% 14% 29% 79% 61%

1.0 1.5 2.0 2.5 3.0 3.5 4.0

R e l a t i v e R i s k

1° effect

2 ° effect

ResultsAZA-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 QualityType of data Relative QualityDemographic (age, gender, race/ethnicity) Very High

Lab Results Very High

Prescriptions Very High1

Vital Signs High

Diagnoses (ICD-9 codes) Medium (variable)

Family/PMH/Social History Low

Other ???

1- for prescriptions written; up to ~40% of prescriptions are never filled

Lots of information desired for research is not stored in the electronic health record as digital data during

routine clinical care.

Clinical Research Paradigm

Characteristic Old Paradigm New ParadigmData 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!

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!