Abstract Introduction Personalizing positive airway pressure (PAP) comfort through machine-learning (ML)–derived insights may help improve early therapy comfort and long-term adherence. Personalized Therapy Comfort Settings (PTCS), FDA-cleared and marketed as Smart Comfort, is an AI-enabled medical device that provides personalized PAP comfort setting recommendations to support patients when using their compatible therapy device. We conducted retrospective analyses to evaluate whether model-guided comfort setting recommendations, developed using data from 1.4 million patients, were associated with improved PAP device usage and patient-reported early therapy experience. Methods Retrospective analyses using de-identified Resmed PAP therapy datasets were conducted to evaluate whether comfort settings aligned with model-recommended configurations (PTCS-Matched) were associated with improved outcomes when compared with Default or Other (non-PTCS/non-Default) settings. Propensity score matching (SMD 0.10) controlled for imbalances between comparison groups. Outcomes included nightly usage, days used, residual AHI (rAHI), mask leak, and long-term discontinuation at 90 days and 1 year. Day-3 patient-reported outcome measures (PROMs) assessing early therapy experience were evaluated in patients who completed them. Results Datasets of over 900,000 AirSense11 device users between October 2022 – May 2025 were analyzed. Across retrospective comparisons, patients whose settings aligned with model recommendations (PTCS-Matched) showed improved PAP device usage when compared with Default configurations. For AS11 devices, PTCS-matched users demonstrated +20 minutes/night at 90 days, +15 minutes/night at 1 year, and +21 usage days at 1 year (all p 0.001). Residual AHI remained well within clinical limits ( 5), mask leak differences were minimal, and AS11 discontinuation was slightly lower (–2.8%, p 0.001). Findings were directionally consistent when compared against Other settings. Day-3 PROMs showed PTCS-Matched users were less likely to find therapy challenging than those on Default comfort settings (19.8% vs 26.5%; –5.9 pp; p 0.001) and more likely to rate therapy as great (+2.7 pp; p=0.003). Combined pressure/breath-in discomfort was modestly lower (–2.2 pp; p=0.012). Conclusion These large-scale real-world analyses show that individualizing comfort settings from the start of PAP therapy, aligning with ML-derived recommendations, is associated with higher PAP usage and better early therapy experience without compromising safety, supporting personalized PAP comfort settings at scale. Support (if any) Funded by Resmed.
Santizo et al. (2026) studied this question.