OBJECTIVE: Develop a chronic disease index that approximates the number of chronic diseases a patient has using a medication database. METHODS: An expert panel determined whether specific medication classes could be indicative of a chronic disease. Those classes identified were incorporated into a computer program and then used to screen the medication records of 246 randomly selected patients to estimate the number of chronic diseases present in each patient. This number was designated as the chronic disease index (CDI). The CDI was then validated against chart review. The CDI and a measure of disease severity, the chronic disease score (CDS) also were compared. The sensitivity and specificity of the computer program was analyzed for seven common chronic diseases. RESULTS: The expert panel designated 54 drug classes containing medications used to treat chronic diseases. The CDI correlated moderately with the number of chronic diseases found via chart review (r = 0.65; P = 0.001) and highly with the CDS (r = 0.81; P = 0.001). The index predicted the presence of three common diseases with a sensitivity of > or = 75%, and of six common diseases with a specificity of > or = 75%. CONCLUSIONS: The CDI correlates moderately well with the actual number of chronic disease states present. This tool may be useful for researchers when trying to identify patients with specific diseases and also for risk adjustment.
OBJECTIVE: Develop a chronic disease index that approximates the number of chronic diseases a patient has using a medication database. METHODS: An expert panel determined whether specific medication classes could be indicative of a chronic disease. Those classes identified were incorporated into a computer program and then used to screen the medication records of 246 randomly selected patients to estimate the number of chronic diseases present in each patient. This number was designated as the chronic disease index (CDI). The CDI was then validated against chart review. The CDI and a measure of disease severity, the chronic disease score (CDS) also were compared. The sensitivity and specificity of the computer program was analyzed for seven common chronic diseases. RESULTS: The expert panel designated 54 drug classes containing medications used to treat chronic diseases. The CDI correlated moderately with the number of chronic diseases found via chart review (r = 0.65; P = 0.001) and highly with the CDS (r = 0.81; P = 0.001). The index predicted the presence of three common diseases with a sensitivity of > or = 75%, and of six common diseases with a specificity of > or = 75%. CONCLUSIONS: The CDI correlates moderately well with the actual number of chronic disease states present. This tool may be useful for researchers when trying to identify patients with specific diseases and also for risk adjustment.
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