title: Development and validation of a predictive algorithm to identify adult asthmatics from medical services and pharmacy claims databases.
abstract: OBJECTIVE
  To develop and validate the accuracy of a predictive model to identify adult asthmatics from administrative health care databases.
  
  
  METHODS
  An existing electronic medical record project in Montreal, Quebec.
  
  
  METHODS
  One thousand four hundred and thirty-one patients with confirmed asthma status were identified from primary care physician's electronic medical record.
  
  
  METHODS
  Therapeutic indication of asthma in an electronic prescription and/or confirmed asthma from an automated problem list were used as the gold standard. Five groups of asthma-specific markers were identified from administrative health care databases to estimate the probability of the presence of asthma. Cross-validation evaluated the diagnostic ability of each predictive model using 50 percent of sample.
  
  
  RESULTS
  The best performance in discriminating between the patients with asthma and those without it included indicators from medical service and prescription claims databases. The best-fitting algorithm had a sensitivity of 70 percent, a specificity of 94 percent, and positive predictive value of 65 percent. The prescriptions claims-specific algorithm demonstrated a nearly equal performance to the model with medical services and prescription claims combined.
  
  
  CONCLUSIONS
  Our algorithm using asthma-specific markers from administrative claims databases provided moderate sensitivity and high specificity.
authors.affiliation: Centre for Health Services and Policy Research, University of British Columbia, 201-2206 East Mall, Vancouver, BC, Canada. ykawasumi@chspr.ubc.ca
authors.forename: Yuko
authors.initials: Y
authors.lastname: Kawasumi
authors.forename: Michal
authors.initials: M
authors.lastname: Abrahamowicz
authors.forename: Pierre
authors.initials: P
authors.lastname: Ernst
authors.forename: Robyn
authors.initials: R
authors.lastname: Tamblyn
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001249:Asthma / Q000453:epidemiology / Q000517:prevention & control*; D011307:Drug Prescriptions / Q000706:statistics & numerical data; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D008331:Management Information Systems / Q000706:statistics & numerical data*; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D015999:Multivariate Analysis; D011792:Quebec / Q000453:epidemiology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index