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.
Data availability
All detailed data is available upon request.
Abbreviations
- AP:
-
Adherence Profiler
- AHI:
-
apnea-hypopnea index
- AHIFLOW :
-
residual apnea-hypopnea index
- ATS:
-
American Thoracic Society
- BMI:
-
body mass index
- CPAP:
-
continuous positive airway pressure
- D14:
-
day 14
- D90:
-
day 90
- NPV:
-
negative predictive value
- OSA:
-
obstructive sleep apnea
- PPV:
-
positive predictive value
References
Javaheri S, Martinez-Garcia MA, Campos-Rodriguez F (2019) CPAP Treatment and cardiovascular prevention: we need to change the design and implementation of our trials. Chest 156(3):431–437. https://doi.org/10.1016/j.chest.2019.04.092
Weaver TE, Maislin G, Dinges DF, Bloxham T, George CF, Greenberg H, Kader G, Mahowald M, Younger J, Pack AI (2007) Relationship between hours of CPAP use and achieving normal levels of sleepiness and daily functioning. Sleep 30:711–719. https://doi.org/10.1093/sleep/30.6.711
Stradling JR, Davies RJ (2000) Is more NCPAP better? Sleep 23(Suppl 4):S150–S153
Pepin JL, Krieger J, Rodenstein D, Cornette A, Sforza E, Delguste P, Deschaux C, Grillier V, Lévy P (1999) Effective compliance during the first 3 months of continuous positive airway pressure. A European prospective study of 121 patients. Am J Respir Crit Care Med 160:1124–1129. https://doi.org/10.1164/ajrccm.160.4.9802027
Powell ED, Gay PC, Ojile JM, Litinski M, Malhotra A (2012) A pilot study assessing adherence to auto-bilevel following a poor initial encounter with CPAP. J Clin Sleep Med 8(1):43–47. https://doi.org/10.5664/jcsm.1658
Weaver TE, Grunstein RR (2008) Adherence to continuous positive airway pressure therapy: the challenge to effective treatment. Proc Am Thorac Soc 5:173–178. https://doi.org/10.1513/pats.200708-119MG
Haniffa M, Lasserson TJ, Smith I (2004) Interventions to improve compliance with continuous positive airway pressure for obstructive sleep apnoea. Cochrane Database Syst Rev:CD003531 10.1002/14651858.CD003531.pub2
Chai-Coetzer CL, Luo YM, Antic NA, Zhang XL, Chen BY, He QY, Heeley E, Huang SG, Anderson C, Zhong NS, McEvoy RD (2013) Predictors of long-term adherence to continuous positive airway pressure therapy in patients with obstructive sleep apnea and cardiovascular disease in the SAVE study. Sleep. 36(12):1929–1937. https://doi.org/10.5665/sleep.3232
Rotenberg et al (2016) Trends in CPAP adherence over twenty years of data collection: a flattened curve. J Otolaryngol Head Neck Surg 45:43. https://doi.org/10.1186/s40463-016-0156-0
Aloia MS, Goodwin MS, Velicer WF, Arnedt JT, Zimmerman M, Skrekas J, Harris S, Millman RP (2008) Time series analysis of treatment adherence patterns in individuals with obstructive sleep apnea. Ann Behav Med 36(1):44–53. https://doi.org/10.1007/s12160-008-9052-9
Pelletier-Fleury N, Rakotonanahary D, Fleury B (2001) The age and other factors in the evaluation of compliance with nasal continuous positive airway pressure for obstructive sleep apnea syndrome. A Cox’s proportional hazard analysis. Sleep Med 2:225–232. https://doi.org/10.1016/s1389-9457(00)00063-0
McArdle N, Devereux G, Heidarnejad H, Engleman HM, Mackay TW, Douglas NJ (1999) Long-term use of CPAP therapy for sleep apnea/hypopnea syndrome. Am J Respir Crit Care Med 159:1108–1114. https://doi.org/10.1164/ajrccm.159.4.9807111
Gagnadoux F, Le Vaillant M, Goupil F, Pigeanne T, Chollet S, Masson P, Humeau MP, Bizieux-Thaminy A, Meslier N (2011) IRSR sleep cohort group. Influence of marital status and employment status on long-term adherence with continuous positive airway pressure in sleep apnea patients. PLoS One 6(8):e22503. https://doi.org/10.1371/journal.pone.0022503
Kohler M, Smith D, Tippett V, Stradling JR (2010) Predictors of long-term compliance with continuous positive airway pressure. Thorax 65:829–832. https://doi.org/10.1136/thx.2010.135848
Rotty MC, Mallet JP, Suehs CM, Martinez C, Borel JC, Rabec C, Bourdin A, Molinari N, Jaffuel D (2019) Affiliations expand. Is the 2013 American Thoracic Society CPAP-tracking system algorithm useful for managing non-adherence in long-term CPAP-treated patients? Respir Res 20:–209. https://doi.org/10.1186/s12931-019-1150-7
Kohler M, Smith D, Tippett V, Strudling JR (2010) Predictors of long-term compliance with continuous positive airway pressure. Thorax 65:829e32. https://doi.org/10.1136/thx.2010.135848
Berry RB, Budhiraja R, Gottlieb DJ, Gozal D, Iber C, Kapur VK, Marcus CL, Mehra R, Parthasarathy S, Quan SF, Redline S, Strohl KP, Davidson Ward SL, Tangredi MM, American Academy of Sleep Medicine (2012) Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events Deliberations of the Sleep Apnea Definitions Task Force of the American Academy of Sleep Medicine. J Clin Sleep Med 8(5):597–619. https://doi.org/10.5664/jcsm.2172
William Hardy RRT, Jeff Jasko MS, Christy Stitt MS Adherence Profiler: A predictive trending algorithm for Positive Airway Pressure therapy adherence. White paper for Philips internal use (Unpublished Results)
Schwab RJ, Badr SM, Epstein LJ, Gay PC, Gozal D, Kohler M, Lévy P, Malhotra A, Phillips BA, Rosen IM, Strohl KP, Strollo PJ, Weaver EM, Weaver TE, ATS Subcommittee on CPAP Adherence Tracking Systems (2013) An official American Thoracic Society statement: continuous positive airway pressure adherence tracking systems. The optimal monitoring strategies and outcome measures in adults. Am J Respir Crit Care Med 188:613–620. https://doi.org/10.1164/rccm.201307-1282ST
Acknowledgments
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.
Funding
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
Authors and Affiliations
Contributions
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
Ethics declarations
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.
Ethical approval
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.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Code availability
Not applicable.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sabil, A., Le Vaillant, M., Stitt, C. et al. A CPAP data–based algorithm for automatic early prediction of therapy adherence. Sleep Breath 25, 957–962 (2021). https://doi.org/10.1007/s11325-020-02186-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11325-020-02186-y