AbdelKebir Sabil1, Marc Le Vaillant2, Christy Stitt3, François Goupil4, Thierry Pigeanne5, Laurene Leclair-Visonneau6, Philippe Masson7, Acya Bizieux-Thaminy8, Marie-Pierre Humeau9, Nicole Meslier10, Frédéric Gagnadoux10. 1. Clinical Research, Philips Respironics, Paris, France. kebir.sabil@cloudsleeplab.com. 2. Pays de le Loire Respiratory Health Research Institute (IRSR-PL), Angers, France. 3. Philips Respironics, Murrysville, PA, USA. 4. Department of Respiratory Diseases, Le Mans General Hospital, Le Mans, France. 5. Respiratory Unit, Pôle santé des Olonnes, Olonne-sur-Mer, France. 6. Department of Physiology and Sleep Medicine, Nantes University Hospital, Nantes, France. 7. Department of Respiratory Diseases, Cholet General Hospital, Cholet, France. 8. Department of Respiratory Diseases, La Roche sur Yon General Hospital, La Roche-sur-Yon, France. 9. Respiratory Unit, Nouvelles Cliniques Nantaises, Nantes, France. 10. Department of Respiratory and Sleep Medicine, University Hospital of Angers, INSERM Unit 1063, University of Angers, Angers, France.
Abstract
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.
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.
Entities:
Keywords:
Adherence prediction algorithm; CPAP machine data analysis; Obstructive sleep apnea; Retrospective study
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