ArticleSleep & breathing = Schlaf & Atmung2021
A CPAP data-based algorithm for automatic early prediction of therapy adherence.
Article in Sleep & breathing = Schlaf & Atmung, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Early prediction of longitudinal treatment adherence in obstructive sleep apnea using machine learning approaches.BioData mining · 2026Article
- A new telemonitoring feature for detection of long-term CPAP adherence.Sleep medicine: X · 2025Article
- Positive airway pressure therapy adherence in obstructive sleep apnea patients with or without web-based telemonitoring: A real-world study.Journal of family medicine and primary care · 2025Article
- Article
- Prediction of Optimal Positive Airway Pressure in Chinese Patients With Obstructive Sleep Apnea.The clinical respiratory journal · 2024Article
- Adherence to continuous positive airway pressure treatment in a cohort of elderly adults with newly diagnosed obstructive sleep apnea.Sleep & breathing = Schlaf & Atmung · 2023Article
- Sleep Endoscopy with Positive Airway Pressure: A Method for Better Compliance and Individualized Treatment of Patients with Obstructive Sleep Apnea.Life (Basel, Switzerland) · 2022Article
- Factors Affecting Long-Term Compliance of CPAP Treatment-A Single Centre Experience.Journal of clinical medicine · 2021Article
- Artificial intelligence for predicting adherence to CPAP therapy in obstructive sleep apnoea: A systematic review.Digital healthArticle
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAdherence 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).
methodThe 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/m
resultsIn 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.
conclusionAutomatic 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.
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