Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Sep 2, 2020
Date Accepted: Aug 12, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Management and treatment of patients with obstructive sleep apnea using an Intelligent Monitoring System based on machine-learning: a Randomized Controlled Trial
ABSTRACT
Background:
Continuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnea (OSA), but treatment compliance is often unsatisfactory.
Objective:
To assess the effectiveness and cost-effectiveness of an Intelligent Monitoring System for improving CPAP compliance.
Methods:
Prospective, open label, parallel, randomized controlled trial including 60 newly-diagnosed OSA patients requiring CPAP (apnea-hypopnea index >15) from Lleida, Spain. Participants were randomized (1:1) to standard management or the MiSAOS Intelligent Monitoring System, consisting on early compliance detection, machine-learning-based adherence prediction, and 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 healthcare costs.
Results:
Randomized patients had mean (SD) age 57 (11) years, apnea-hypopnea index 50 (27), and 13% were women. Patients in the intervention arm had a mean (95% CI) of 1.14 (0.04 to 2.23) h/day higher adjusted CPAP compliance than controls (P = 0.047). Patients’ satisfaction was excellent in both arms, and up to 88% of intervention patients reported willingness to keep using MiSAOS App in the future. No significant differences were found in costs (control: mean (SD) 90.2€ (53.1); intervention: mean (SD) 96.2€ (62.13); P = 0.688). 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 CPAP-treated OSA patients and confirms the value of patients’ empowerment in the management of chronic diseases. Clinical Trial: ClinicalTrials.gov NCT03116958.
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