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 DataDavid C Kaelber, MD, PhD, MPH, FAAP, FACPAssociate Professor of Internal Medicine, Pediatrics, Epidemiology, and BiostatisticsDirector of the Center for Clinical Informatics Research and EducationChief Medical Informatics OfficerThe MetroHealth SystemCase Clinical and Translational Science Center (CTSC)Case Western Reserve University
2 DisclosuresI 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 1Patient Characteristics association with Venous Thromboembolic Events (VTEs) – A Cohort Study using Pooled Electronic Health Record (EHR) dataRelationship between weight, height, and blood clots (venous thromboembolic events)Not CER ExampleKaelber, 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 2Azathioprine - A case study using pooled electronic health record data and co-morbidity networks for post-market drug surveillancePost-market surveillance of AzathioprineAnti tumor necrosis factor medicationCER ExampleManuscript submitted and under review
6 Study DesignDesign: 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 ValueAbnormal RangeAnemiaHemoglobin (Hgb)<11 g/dLCell lysisLactate dehydrogenase (LDH)>190 IU/LFeverTemperature>37.8oFHepatotoxicityAST, ALTAST>40 IU/L and ALT>40 IU/LTotal bilirubin (Bili)>1 mg/dLHypertensionBlood pressure (BP)Systolic >140 mm Hgor Diastolic>90 mm HgNephrotoxicityCreatinine (Cr)>1.5 mg/dLNeutropeniaNeutrophil countCount<57% or <2.5 cells/µlNeutrophiliaCount>70%
8 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 CohortAZA CohortAbatacept (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%)570Total129560
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° effect2 °effect
10 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.
11 Study Conclusions1st 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.
13 De-identified Population Data AdvantagesNot human studies research (no IRB)No HIPAA issues (no security issues)DisadvantagesLimited data analytic (statistical) toolsLimited research questions
14 Keys to Using EHR DataUnderstanding Data SourcesCorroborating Data/FindingsInternal versus external corroborationClinical Data versus Research DataUnderstand 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 HighLab ResultsPrescriptionsVery High1Vital SignsHighDiagnoses (ICD-9 codes)Medium (variable)Family/PMH/Social HistoryLowOther???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 CharacteristicOld ParadigmNew ParadigmDatasiloedaggregatedInfrastructure Resourcessignificantnone/minimalQueries/Analysisdays/weeks/monthsreal-time/near-real timeSelf-ServiceminimalhighResearchers want quick, easy, access to “all” data themselves!
17 Clinical Research Implications CharacteristicOld ParadigmNew ParadigmDataSeparate Research DatabaseShared Research and Clinic Database (EHR)Time1000+ hours100+ hoursMoney100,000-1,000,000+0-10,000+PeopleManyFewOrder 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!