Management and Treatment of Patients With Obstructive Sleep Apnea Using an Intelligent Monitoring System Based on Machine Learning Aiming to Improve Continuous Positive Airway Pressure Treatment Compliance: Randomized Controlled Trial

• Published in: Journal of medical Internet research 2021-10, Vol.23 (10), p.e24072-e24072
• Main Authors: Turino, Cecilia, Benítez, Ivan D, Rafael-Palou, Xavier, Mayoral, Ana, Lopera, Alejandro, Pascual, Lydia, Vaca, Rafaela, Cortijo, Anunciación, Moncusí-Moix, Anna, Dalmases, Mireia, Vargiu, Eloisa, Blanco, Jordi, Barbé, Ferran, de Batlle, Jordi
• Format: Article
• Language: English
• Subjects: Chronic illnesses; Clinical trials; Compliance; Continuous positive airway pressure; Cost analysis; Costs; Empowerment; Feedback; Health care expenditures; Hypertension; Intelligence; Intervention; Machine learning; Monitoring systems; Multimedia; Nasal continuous positive airway pressure; Newly diagnosed; Obstructive sleep apnea; Original Paper; Patient compliance; Patient satisfaction; Patients; Quality of life; Simulation; Sleep apnea; Sleepiness; Teams; Websites; Women
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/24072

Background: Continuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnea (OSA), but treatment compliance is often unsatisfactory. Objective: The aim of this study was to assess the effectiveness and cost-effectiveness of an intelligent monitoring system for improving CPAP compliance. Methods: This is a prospective, open label, parallel, randomized controlled trial including 60 newly diagnosed patients with OSA requiring CPAP (Apnea–Hypopnea Index [AHI] >15) from Lleida, Spain. Participants were randomized (1:1) to standard management or the MiSAOS intelligent monitoring system, involving (1) early compliance detection, thus providing measures of patient’s CPAP compliance from the very first days of usage; (2) machine learning–based prediction of midterm future CPAP compliance; and (3) rule-based recommendations for the patient (app) and care team. Clinical and anthropometric variables, daytime sleepiness, and quality of life were recorded at baseline and after 6 months, together with patient’s compliance, satisfaction, and health care costs. Results: Randomized patients had a mean age of 57 (SD 11) years, mean AHI of 50 (SD 27), and 13% (8/60) were women. Patients in the intervention arm had a mean (95% CI) of 1.14 (0.04-2.23) hours/day higher adjusted CPAP compliance than controls (P=.047). Patients’ satisfaction was excellent in both arms, and up to 88% (15/17) of intervention patients reported willingness to keep using the MiSAOS app in the future. No significant differences were found in costs (control: mean €90.2 (SD 53.14) (US $105.76 [SD 62.31]); intervention: mean €96.2 (SD 62.13) (US $112.70 [SD 72.85]); P=.70; €1=US $1.17 was considered throughout). Overall costs combined with results on compliance demonstrated cost-effectiveness in a bootstrap-based simulation analysis. Conclusions: A machine learning–based intelligent monitoring system increased daily compliance, reported excellent patient satisfaction similar to that reported in usual care, and did not incur in a substantial increase in costs, thus proving cost-effectiveness. This study supports the implementation of intelligent eHealth frameworks for the management of patients with CPAP-treated OSA and confirms the value of patients’ empowerment in the management of chronic diseases. Trial Registration: ClinicalTrials.gov NCT03116958; https://clinicaltrials.gov/ct2/show/NCT03116958