Performance of Claims-Based Algorithms for Adherence to Positive Airway Pressure Therapy in Commercially Insured Patients With OSA.

ABSTRACT

BACKGROUND: Positive airway pressure (PAP) therapy is first-line therapy for OSA, but consistent use is required for it to be effective. Previous studies have used Medicare fee-for-service claims data (eg, device, equipment charges) as a proxy for PAP adherence to assess its effects. However, this approach has not been validated in a US commercially insured population, where coverage rules are not standardized. RESEARCH QUESTION In a commercially insured population in the United States, how well do claims-based algorithms for defining PAP adherence correspond with objective PAP device usage? STUDY DESIGN AND METHODS: Deidentified administrative claims data of commercially insured patients (aged 18-64 years) with OSA were linked to objective PAP therapy usage data from cloud-connected devices. Adherence was defined based on device use (using an extension of Centers for Medicare & Medicaid Services 90-day compliance criteria) and from claims-based algorithms to compare usage metrics and identify potential misclassifications. RESULTS: The final sample included 213,341 patients. Based on device usage, 48% were adherent in the first year. Based on claims, between 10% and 84% of patients were identified as adherent (accuracy, sensitivity, and specificity ranges 53%-68%, 12%-95%, and 26%-92%, respectively). Relative to patients who were claims-adherent, patients who were device-adherent had consistently higher usage across all metrics (mean, 339.9 vs 260.0-290.0 days of use; 6.6 vs 5.1-5.6 d/wk; 6.4 vs 4.6-5.2 h/d). Consistent PAP users were frequently identified by claims-based algorithms as nonadherent, whereas many inconsistent users were classified by claims-based algorithms as adherent. INTERPRETATION: In aggregate US commercial data with nonstandardized PAP coverage rules, concordance between existing claims-based definitions and objective PAP use was low. Caution is warranted when applying existing claims-based algorithms to commercial populations.