Computer-based models to identify high-risk adults with asthma: is the glass half empty of half full?
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.
Lieu
T A
TA
Division of Research, Kaiser Permanente, Oakland, California, USA. tracy_lieu@hphc.org
Capra
A M
AM
Quesenberry
C P
CP
Mendoza
G R
GR
Mazar
M
M
D000328:Adult; D001249:Asthma / Q000453:epidemiology*; D015331:Cohort Studies; D003198:Computer Simulation; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D005260:Female; D006279:Health Maintenance Organizations / Q000706:statistics & numerical data; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D015233:Models, Statistical; D012189:Retrospective Studies; D018570:Risk Assessment; D012680:Sensitivity and Specificity