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Sleep-Disordered Breathing in Heart Failure: Phenotypes, Mechanisms, and Precision Approach
Huijie Yi1, Luciano F Drager2, Xiao Wang3
1Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Respiratory and Sleep Medicine, Peking University People's Hospital, Beijing, China.
None:
Sleep-disordered breathing (SDB), comprising obstructive sleep apnea (OSA) and central sleep apnea (CSA), is highly prevalent in heart failure (HF) and is associated with adverse remodeling, arrhythmias, and worse clinical outcomes. However, despite improvements in physiological markers, treatment of SDB has not consistently translated into reductions in mortality or HF hospitalization. In OSA, continuous positive airway pressure (CPAP) improves symptoms, oxygenation, and selected physiological parameters, but evidence in HF is derived largely from small, short-term trials and reductions in hard clinical outcomes have not been demonstrated. Emerging data suggest that conventional metrics, such as the apnea-hypopnea index, incompletely capture cardiovascular risk and that physiological markers, including hypoxic burden and autonomic responses, may better identify high-risk phenotypes likely to benefit from treatment. Alternative therapies, including mandibular advancement devices, weight-loss strategies, hypoglossal nerve stimulation, and metabolic interventions, can improve OSA severity, although HF-specific outcome data remain limited. In CSA, which is often secondary to HF and reflects ventilatory-control instability, therapies such as CPAP and adaptive servo-ventilation have shown neutral or adverse long-term outcomes, despite improvements in surrogate markers, suggesting that CSA may represent a marker of HF severity or a compensatory response rather than a straightforward therapeutic target. Emerging evidence also indicates that respiratory instability in HF extends beyond conventional nocturnal assessments. Collectively, these findings support phenotype-specific, precision-based approaches that integrate HF phenotype, physiological markers, and patients' characteristics to optimize SDB management in HF.
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