James S Floyd1,2, Marc Blondon3, Kathryn P Moore4, Edward J Boyko2,4,5, Nicholas L Smith1,6,4,7. 1. Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA. 2. Medicine, University of Washington, Seattle, WA, USA. 3. Division of Angiology and Haemostasis, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland. 4. Seattle Epidemiologic Research and Information Center, VA Puget Sound Health Care System, Seattle, WA, USA. 5. General Medicine Service, VA Puget Sound Health Care System, Seattle, WA, USA. 6. Departments of Epidemiology, University of Washington, Seattle, WA, USA. 7. Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA.
Abstract
BACKGROUND: Electronic health data are routinely used to conduct studies of cardiovascular disease in the setting of the Veterans Health Administration (VA). Previous studies have estimated the positive predictive value (PPV) of International Classification of Disease, Ninth Revision (ICD-9) codes for acute myocardial infarction (MI), but the sensitivity of these codes for all true events and the accuracy of coding algorithms for prevalent disease status at baseline are largely unknown. METHODS: We randomly sampled 180 Veterans from the VA Puget Sound Health Care System who initiated diabetes treatment. The full electronic medical record was reviewed to identify prevalent conditions at baseline and acute MI events during follow-up. The accuracy of various coding algorithms was assessed. RESULTS: Algorithms for previous acute events at baseline had high PPV (previous MI: 97%; previous stroke: 81%) but low sensitivity (previous MI: 38%; previous stroke: 52%). Algorithms for chronic conditions at baseline had high PPV (heart failure: 72%; coronary heart disease [CHD]: 85%) and high sensitivity (heart failure: 90%, CHD: 84%). For current smoking status at baseline, ICD-9 codes with pharmacy data had a PPV of 77% and sensitivity of 73%. The coding algorithm for acute MI events during follow-up had high PPV (80%) and sensitivity (89%). CONCLUSIONS: ICD-9 codes for acute MI events during follow-up had high PPV and sensitivity. The sensitivity of ICD-9 codes for previous acute events at baseline was low, but a composite variable for baseline CHD had good accuracy.
BACKGROUND: Electronic health data are routinely used to conduct studies of cardiovascular disease in the setting of the Veterans Health Administration (VA). Previous studies have estimated the positive predictive value (PPV) of International Classification of Disease, Ninth Revision (ICD-9) codes for acute myocardial infarction (MI), but the sensitivity of these codes for all true events and the accuracy of coding algorithms for prevalent disease status at baseline are largely unknown. METHODS: We randomly sampled 180 Veterans from the VA Puget Sound Health Care System who initiated diabetes treatment. The full electronic medical record was reviewed to identify prevalent conditions at baseline and acute MI events during follow-up. The accuracy of various coding algorithms was assessed. RESULTS: Algorithms for previous acute events at baseline had high PPV (previous MI: 97%; previous stroke: 81%) but low sensitivity (previous MI: 38%; previous stroke: 52%). Algorithms for chronic conditions at baseline had high PPV (heart failure: 72%; coronary heart disease [CHD]: 85%) and high sensitivity (heart failure: 90%, CHD: 84%). For current smoking status at baseline, ICD-9 codes with pharmacy data had a PPV of 77% and sensitivity of 73%. The coding algorithm for acute MI events during follow-up had high PPV (80%) and sensitivity (89%). CONCLUSIONS: ICD-9 codes for acute MI events during follow-up had high PPV and sensitivity. The sensitivity of ICD-9 codes for previous acute events at baseline was low, but a composite variable for baseline CHD had good accuracy.
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