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

Study objectives: 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.

Methods: 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.

Results: 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 H₂O). 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 H₂O were most likely to remain on ASV therapy at 1 year.

Conclusions: 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.

Citation: 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. J Clin Sleep Med. 2021;17(12):2355-2362.