Machine learning-based prediction of adherence to continuous positive airway pressure (CPAP) in obstructive sleep apnea (OSA). (3rd July 2022)

• Record Type: Journal Article
• Authors: Scioscia, Giulia; Tondo, Pasquale; Foschino Barbaro, Maria Pia; Sabato, Roberto; Gallo, Crescenzio; Maci, Federica; Lacedonia, Donato
• Is Part Of: Informatics for health & social care. Volume 47:Number 3(2022)
• Page Start: 274
• Page End: 282
• Publication Date: 2022-07-03
• Subjects: Adherence CPAP -- longer-term users -- machine learning -- OSA -- sleep apnea; Medicine -- Information services -- Periodicals; Medical informatics -- Periodicals; Medicine -- Data processing -- Periodicals; 025.0661
• DOI: 10.1080/17538157.2021.1990300
• Languages: English
• ISSNs: 1753-8157

Abstract

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.