Validation of methods for assessing cardiovascular disease using electronic health data in a cohort of Veterans with diabetes

Pharmacoepidemiol Drug Saf. 2016 Apr;25(4):467-71. doi: 10.1002/pds.3921. Epub 2015 Nov 11.

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.

Keywords: diabetes mellitus; electronic health data; myocardial infarction; pharmacoepidemiology; smoking; validation.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Validation Study

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms*
  • Cardiovascular Diseases / diagnosis
  • Cardiovascular Diseases / epidemiology*
  • Databases, Factual
  • Diabetes Mellitus, Type 2 / epidemiology*
  • Electronic Health Records / statistics & numerical data
  • Epidemiologic Research Design
  • Female
  • Follow-Up Studies
  • Humans
  • International Classification of Diseases*
  • Male
  • Middle Aged
  • Myocardial Infarction / diagnosis
  • Myocardial Infarction / epidemiology
  • Predictive Value of Tests
  • Sensitivity and Specificity
  • Veterans
  • Washington