Objective: To develop and validate a method for identifying persons with undiagnosed chronic obstructive pulmonary disease (COPD) using outpatient pharmacy data.
Study design: Case-control analysis of managed care administrative data with clinical validation by spirometry and standardized questionnaires.
Methods: Patients with a new diagnosis of COPD were matched to 3 control subjects by age and sex. Outpatient pharmacy utilization for the 2 years prior to the initial diagnosis was captured. Drugs associated with an eventual diagnosis of COPD were identified using conditional logistic regression, and then entered into a predictive algorithm using discriminant function analysis. The algorithm was tested in a second population from the same health plan and externally validated using 2 large multicenter databases. This system was clinically validated by testing 100 individuals identified by the algorithm with spirometry plus health status and respiratory symptoms questionnaires.
Results: COPD patients used significantly more antibiotics, cardiac medications, and respiratory drugs than their matched controls. The final algorithm identified COPD patients with a sensitivity of 60% and specificity of 70%, without the benefit of knowing any patient's smoking history. Of the first 100 persons identified by the algorithm as being at risk and recruited for testing, 25 were proven to have previously undiagnosed COPD.
Conclusions: Pharmacy utilization increases in the years prior to initial COPD diagnosis. Algorithms based on pharmacy utilization can efficiently identify persons at risk for undiagnosed COPD.