Machine learning-based prediction of adherence to continuous positive airway pressure (CPAP) in obstructive sleep apnea (OSA)

ABSTRACT

Continuous positive airway pressure (CPAP) is the “gold-standard” therapy for obstructive sleep apnea (OSA), but the main problem is the poor adherence. Therefore, we have searched for the causes of poor adherence to CPAP therapy by applying predictive machine learning (ML) methods. The study was conducted on OSAs in nighttime therapy with CPAP. An outpatient follow-up was planned at 3, 6, 12 months. We collected several parameters at the baseline visit and after dividing all patients into two groups (Adherent and Non-adherent) according to therapy adherence, we compared them. Statistical differences between the two groups were not found according to baseline characteristics, except gender (P< .01). Therefore, we applied ML to predict CPAP adherence, and these predictive models showed an accuracy and sensitivity of 68.6% and an AUC (area under the curve) of 72.9% through the SVM (support vector machine) classification method. The identification of factors predictive of long-term CPAP adherence is complex, but our proof of concept seems to demonstrate the utility of ML to identify subjects poorly adherent to therapy. Therefore, application of these models to larger samples could aid in the careful identification of these subjects and result in important savings in healthcare spending.

KEYWORDS:

• Adherence CPAP
• longer-term users
• machine learning
• OSA
• sleep apnea

Abbreviation list

AHI (apnea-hypopnea index); BMI (Body Mass Index); BPAP (bilevel-positive airway pressure); COPD (Chronic Obstructive Pulmonary Disease); CPAP (continuous positive airway pressure); ENT (ear, nose and throat); ESS (Epworth Sleepiness Scale); ML (machine learning); OSA (obstructive sleep apnea); SVM (support vector machine).

Acknowledgments

The authors would like to thank Saveria D’Adduzio, Francesco Scardecchia and Matteo Di Maggio for the work done in our Respiratory Medicine outpatient, in the assembly of the respiratory polygraph and data downloaded from the memory card of CPAP devices.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.