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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, FACP.

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Presentation on theme: "Using the Explorys Platform for Clinical Effectiveness Research (CER) with De-identified, Population Level Data David C Kaelber, MD, PhD, MPH, FAAP, FACP."— Presentation transcript:

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.

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