The effectiveness of hardware CPAP therapy in heart failure with preserved left ventricular ejection fraction and obstructive sleep apnea syndrome

Aim. To evaluate the effectiveness of hardware Continuous Positive Airway Pressure therapy (CPAP therapy) and its likely predictors in patients with heart failure with preserved ejection fraction (HFpEF) associated with obstructive sleep apnea syndrome (OSAS).

Materials and methods. The study involved 207 men with HFpEF and OSAS (apnea/hypopnea index >15 per hour) who did not initially have ischemic disease and other structural heart pathology. At inclusion in the study, polysomnography and echocardiography were performed with an assessment of diastolic function and global longitudinal deformation of the left ventricular myocardium, as well as a 6-minute walk test (6MWT) and the level of the brain natriuretic peptide precursor (NT-proBNP) in the blood was determined. 80 patients received hardware CPAP the rapy, 127 patients made up the control group. After 12 months, 6MWT was repeated, NT-proBNP was determined, and clinical outcomes were assessed retrospectively.

Results. The CPAP group had 16% fewer hospitalizations (p=0.011 [95% confidence interval – CI 4.29]) and showed a trend towards an increase in the 6MWT distance (p=0.065). To assess the likely predictors of the effectiveness of CPAP therapy, a subgroup of “responders” was identified, characterized by an increase in the distance according to the 6MWT, a decrease in the level of NT-proBNP, and the absence of adverse clinical events during the observation period. There were significant differences between responders and non-responders in apnea/hypopnea index (p=0.01 [95% CI -10.6; -2.5]), global longitudinal deformation of the left ventricular myocardium (p=0.05 [95% CI -4.7; 0]), diastolic function E/A (p=0.02 [95% CI -0.1; 0]).

Conclusion. CPAP therapy improves clinical outcomes and functional status in patients with OSAS-associated HFpEF. The predictive model built using the identified efficacy predictors can be used to develop a personalized treatment algorithm for this cohort of patients.