Development and validation of a predictive algorithm to identify adult asthmatics from medical services and pharmacy claims databases.

OBJECTIVE: To develop and validate the accuracy of a predictive model to identify adult asthmatics from administrative health care databases. STUDY
SETTING: An existing electronic medical record project in Montreal, Quebec. STUDY
DESIGN: One thousand four hundred and thirty-one patients with confirmed asthma status were identified from primary care physician's electronic medical record. DATA COLLECTION/EXTRACTION
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. PRINCIPAL
FINDINGS: 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. © Health Research and Educational Trust.