Objective: Adherence is a critical issue in the treatment of obstructive sleep apnea with continuous positive airway pressure (CPAP). Approximately 40% of patients treated with CPAP are at risk of discontinuation or insufficient use (< 4 h/night). Assuming that the first few days on CPAP are critical for continued treatment, we tested the predictive value at day 14 (D14) of the Philips Adherence Profiler™ (AP) algorithm for adherence at 3 months (D90).
Method: The AP™ algorithm uses CPAP machine data hosted in the database of EncoreAnywhere™. This retrospective study involved 457 patients (66% men, 60.0 ± 11.9 years; BMI = 31.2 ± 5.9 kg/m2; AHI = 37.8 ± 19.2; Epworth score = 10.0 ± 4.8) from the Pays de la Loire Sleep Cohort. At D90, 88% of the patients were adherent as defined by a mean daily CPAP use of ≥ 4 h.
Results: In a univariate analysis, the factors significantly associated with CPAP adherence at D90 were older age, lower BMI, CPAP adherence (≥ 4 h/night) at D14, and AP™ prediction at D14. In a multivariate analysis, only older age (OR 2.10 [1.29-3.41], p = 0.003) and the AP™ prediction at D14 (OR 16.99 [7.26-39.75], p < 0.0001) were significant predictors. CPAP adherence at D90 was not associated with device-derived residual events, nor with the levels of pressure or leakage except in the case of very significant leakage when it persisted for 90 days.
Conclusion: Automatic telemonitoring algorithms are relevant tools for early prediction of CPAP therapy adherence and may make it possible to focus therapeutic follow-up efforts on patients who are at risk of non-adherence.
Keywords: Adherence prediction algorithm; CPAP machine data analysis; Obstructive sleep apnea; Retrospective study.