Computer-based models to identify high-risk adults with asthma: is the glass half empty of half full?

J Asthma. 1999 Jun;36(4):359-70. doi: 10.3109/02770909909068229.


This study developed and evaluated the performance of prediction models for asthma-related adverse outcomes based on the computerized hospital, clinic, and pharmacy utilization databases of a large health maintenance organization. Prediction models identified patients at three- to four-fold increased risk of hospitalization and emergency department visits, and were valid for test samples from the same population. A model that identified 19% of patients as high risk had a sensitivity of 49%, a specificity of 84%, and a positive predictive value of 19%. We conclude that prediction models that are based on computerized utilization data can identify adults with asthma at elevated risk, but may have limited sensitivity and specificity in actual populations.

MeSH terms

  • Adult
  • Asthma / epidemiology*
  • Cohort Studies
  • Computer Simulation
  • Emergency Service, Hospital / statistics & numerical data
  • Female
  • Health Maintenance Organizations / statistics & numerical data
  • Hospitalization / statistics & numerical data
  • Humans
  • Male
  • Models, Statistical*
  • Retrospective Studies
  • Risk Assessment
  • Sensitivity and Specificity