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J Asthma. 1999 Jun;36(4):359-70.

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

Author information

1
Division of Research, Kaiser Permanente, Oakland, California, USA. tracy_lieu@hphc.org

Abstract

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.

PMID:
10386500
[Indexed for MEDLINE]

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