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Article Dans Une Revue Journal of Clinical Sleep Medicine Année : 2021

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

Résumé

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.

Dates et versions

hal-03464002 , version 1 (24-12-2021)

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Citer

Atul Malhotra, Adam V Benjafield, Peter A Cistulli, Lee Jingjing, Holger Woehrle, et al.. Characterizing respiratory parameters, settings and adherence in real-world patients using adaptive servo ventilation therapy : big data analysis. Journal of Clinical Sleep Medicine, 2021, 17 (12), pp.2355-2362. ⟨10.5664/jcsm.9430⟩. ⟨hal-03464002⟩

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