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

Abstract

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

Multiple Machine Learning Methods Reveal Key Biomarkers of Obstructive Sleep Apnea and Continuous Positive Airway Pressure Treatment

“…Moreover, machine learning tools are effective at assessing long-term cardiovascular risk in OSA patients ( Li et al, 2022 ). Machine learning has even been used to help build a CPAP compliance-monitoring system to improve the management of OSA patients ( Turino et al, 2021 ). Additionally, machine learning is used to recognize data from polysomnograms (PSGs) and other monitoring equipment automatically.…”

Tratamiento del síndrome de apnea obstructiva del sueño: revisión de la literatura

“…La cirugía ortognática logra grandes beneficios anatómicos como musculares, evitando el colapso de la vía aérea superior, las apneas e hipoapneas en el ciclo del sueño, mejorando de este modo la calidad de vida del paciente 21 . Es adecuado para los casos de severidad moderada y severa [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] .…”

The role of artificial intelligence in the treatment of obstructive sleep apnea

“…Additionally, ML has been used to help build a CPAP compliance-monitoring system to improve the management of OSA patients. Data from the CPAP device, including compliance, mask leaks and residual respiratory events was sent to the web database [ 25 ]. The intelligence monitoring system utilized this data to predicted expected compliance and provided feedback and interventions to increase compliance.…”

“…The patients with this intervention had a mean of 1.14 h/day higher adjusted CPAP compliance than the control without the system. Overall this approach was cost effective and was associated with excellent patient satisfaction [ 25 ].…”