Characterizing respiratory parameters, settings, and adherence in real-world patients using adaptive servo ventilation therapy: big data analysis

There is minimal guidance around how to optimize inspiratory positive airway pressure (IPAP) levels during use of adaptive servo ventilation (ASV) in clinical practice. This real-world data analysis investigated the effects of IPAP and minimum pressure support settings on respiratory parameters and adherence in ASV-treated patients. A United States-based telemonitoring database was queried for patients starting ASV between August 1, 2014 and November 30, 2019. Patients meeting the following criteria were included: United States-based patients aged ≥ 18 years; AirCurve 10 device (ResMed); and ≥ 1 session with usage of ≥ 1 hour in the first 90 days. Key outcomes were mask leak and residual apnea-hypopnea index at different IPAP settings, adherence and therapy termination rates, and respiratory parameters at different minimum pressure support settings. There were 63,996 patients included. Higher IPAP was associated with increased residual apnea-hypopnea index and mask leak but did not impact device usage per session (average > 6 h/day at all IPAP settings; 6.7 h/day at 95th percentile IPAP 25 cm H O). There were no clinically relevant differences in respiratory rate, minute ventilation, leak, and residual apnea-hypopnea index across all possible minimum pressure support settings. Patients with a higher 95th percentile IPAP or with minimum pressure support of 3 cm H O were most likely to remain on ASV therapy at 1 year. Our findings showed robust levels of longer-term adherence to ASV therapy in a large group of real-world patients. There were no clinically important differences in respiratory parameters across a range of pressure and pressure support settings. Future work should focus on the different phenotypes of patients using ASV therapy. Malhotra A, Benjafield AV, Cistulli PA, et al. Characterizing respiratory parameters, settings, and adherence in real-world patients using adaptive servo ventilation therapy: big data analysis. . 2021;17(12):2355-2362.