Sleep-related breathing disorders in patients with heart failure with reduced and mildly reduced ejection fraction: main types and their dependence on heart failure etiology

Aim. To identify and study the nature of sleep-related breathing disorders (SBDs) in a cohort of hospitalized patients with heart failure (HF) with reduced and mildly reduced ejection fraction (EF), as well as to clarify the relationship between SBD type, etiology and severity of HF.

Material and methods. The study included 117 patients with HF with reduced and mildly reduced ejection fraction hospitalized at the National Medical Research Center for Therapy and Preventive Medicine from 2019 to 2021. All patients underwent clinical and paraclinical examination, including cardiorespiratory sleep study. Patients were divided into three groups according to the type and severity of SBD: no or mild SBD, predominantly with obstructive sleep apnea (OSA) and predominantly with central sleep apnea (CSA). Severity of SBD and clinical data were compared between these groups.

Results. A total of 5 patients (4,27%) did not have any SBDs, while 47 (40,17%) were diagnosed with CSA, and 65 (55,56%) — OSA of varying severity. The proportions of patients with moderate and severe CSA and OSA differed insignificantly and amounted to 35,9% (n=42) and 44,4% (n=52), respectively. There were following proportions of diseases related to HF: coronary artery disease (41,88%), nonischemic cardiomyopathy (26,5%), arrhythmogenic cardiomyopathy (15,38%) and other causes (16,24%) (hypertension, myocarditis, heart defects). We found that reduced EF <40%, end-diastolic volume >210 ml, and ventricular ectopy (>300 extrasystoles/day) were associated with CSA, and body mass index >30 kg/m² was traditionally associated with OSA.

Conclusion. More than half of HF patients with reduced and mildly reduced EF have SBDs. Decreased LVEF and ventricular ectopic activity are associated with CSA, while increased body mass index is associated with OSA. Consideration of SBD risk factors may improve patient phenotyping for individualized therapy.

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