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
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 - PowerPoint PPT Presentation
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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