title: Identifying cases of congestive heart failure from administrative data: a validation study using primary care patient records.
abstract: BACKGROUND
  To determine if using a combination of hospital administrative data and ambulatory care physician billings can accurately identify patients with congestive heart failure (CHF), we tested 9 algorithms for identifying individuals with CHF from administrative data.
  
  
  METHODS
  The validation cohort against which the 9 algorithms were tested combined data from a random sample of adult patients from EMRALD, an electronic medical record database of primary care physicians in Ontario, Canada, and data collected in 2004/05 from a random sample of primary care patients for a study of hypertension. Algorithms were evaluated on sensitivity, specificity, positive predictive value, area under the curve on the ROC graph and the combination of likelihood ratio positive and negative.
  
  
  RESULTS
  We found that that one hospital record or one physician billing followed by a second record from either source within one year had the best result, with a sensitivity of 84.8% and a specificity of 97.0%.
  
  
  CONCLUSIONS
  Population prevalence of CHF can be accurately measured using combined administrative data from hospitalization and ambulatory care.
authors.affiliation: Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. sue.schultz@ices.on.ca
authors.forename: S E
authors.initials: SE
authors.lastname: Schultz
authors.forename: D M
authors.initials: DM
authors.lastname: Rothwell
authors.forename: Z
authors.initials: Z
authors.lastname: Chen
authors.forename: K
authors.initials: K
authors.lastname: Tu
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records; D005260:Female; D006333:Heart Failure / Q000453:epidemiology*; D006801:Humans; D008297:Male; D009864:Ontario / Q000453:epidemiology; D015995:Prevalence; D011320:Primary Health Care; D012680:Sensitivity and Specificity
keywords: congestive heart failure; epidemiologic methods; population prevalence; validation studies