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BMJ Publishing Group, Thorax, 2(77), p. 178-185, 2021

DOI: 10.1136/thoraxjnl-2021-217205

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Adaptive servo ventilation for sleep apnoea in heart failure: the FACE study 3-month data

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Data provided by SHERPA/RoMEO

Abstract

RationaleAdaptive servo ventilation (ASV) is contraindicated in patients with systolic heart failure (HF) who have a left ventricular ejection fraction (LVEF) below 45% and predominant central sleep apnoea (CSA). However, the effects of ASV in other HF subgroups have not been clearly defined.ObjectiveThe European, multicentre, prospective, observational cohort trial, FACE, evaluated the effects of ASV therapy on morbidity and mortality in patients with HF with sleep-disordered breathing (SDB); 3-month outcomes in patient subgroups defined using latent class analysis (LCA) are presented.MethodsConsecutive patients with HF with predominant CSA (±obstructive sleep apnoea) indicated for ASV were included from 2009 to 2018; the non-ASV group included patients who refused/were noncompliant with ASV. The primary endpoint was time to composite first event (all-cause death, lifesaving cardiovascular intervention or unplanned hospitalisation for worsening of chronic HF).Measurements and main resultsBaseline assessments were performed in 503 patients, and 482 underwent 3-month follow-up. LCA identified six discrete patient clusters characterised by variations in LVEF, SDB type, age, comorbidities and ASV acceptance. The 3- month rate of primary outcome events was significantly higher in cluster 1 patients (predominantly men, low LVEF, severe HF, CSA; 13.9% vs 1.5%–5% in other clusters, p<0.01).ConclusionFor the first time, our data identified homogeneous patient clusters representing clinically relevant subgroups relating to SDB management in patients with HF with different ASV usage, each with a different prognosis. This may improve patient phenotyping in clinical practice and allow individualisation of therapy.