A CPAP data–based algorithm for automatic early prediction of therapy adherence



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).


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.


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.


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.

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Data availability

All detailed data is available upon request.



Adherence Profiler


apnea-hypopnea index


residual apnea-hypopnea index


American Thoracic Society


body mass index


continuous positive airway pressure


day 14


day 90


negative predictive value


obstructive sleep apnea


positive predictive value


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The authors would like to thank Christelle Gosselin and Jean-Louis Racineux, from the Institut de Recherche en Santé Respiratoire des Pays de La Loire. We thank Julien Godey, Laetitia Moreno, and Marion Vincent, sleep technicians in the Department of Respiratory and Sleep Medicine of Angers University Hospital.


This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. However, this work benefited from free technical support (data gathering and statistical analysis by M. Levaillant) provided by the Institut de Recherche en Santé Respiratoire des Pays de La Loire.

Author information




This work was performed under the direction of Professor Frédéric Gagnadoux at the University Hospital of Angers, France.

A.S contributed to the design of the study, the analysis of the data, and the preparation of the manuscript. C.S contributed to the extraction of the CPAP machine data and making it available for the analysis. M.L performed the statistical analysis for all the data considered in this study. All the other authors participated in the recruitment of patients for the cohort and declare that they have seen and approved the manuscript as submitted. All the authors certify that the manuscript is being submitted only to the Sleep and Breathing Journal, that it will not be submitted elsewhere while under consideration

Corresponding author

Correspondence to AbdelKebir Sabil.

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Conflict of interest

At the time of the study, A. Sabil and C. Stitt were full-time employees of Philips Respironics. All other authors declare that they have no conflict of interest on the present study.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Sabil, A., Le Vaillant, M., Stitt, C. et al. A CPAP data–based algorithm for automatic early prediction of therapy adherence. Sleep Breath (2020). https://doi.org/10.1007/s11325-020-02186-y

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  • Obstructive sleep apnea
  • Adherence prediction algorithm
  • CPAP machine data analysis
  • Retrospective study