ArticleSleep & breathing = Schlaf & Atmung2025
Assessing new treatments for central sleep Apnoea in heart failure: sample size considerations for reliable detection of safety signals.
Article in Sleep & breathing = Schlaf & Atmung, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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12 authors.
Funding
Abstract
purposeCentral sleep apnoea (CSA) and Cheyne-Stokes respiration (CSR) are prevalent in patients with heart failure with reduced ejection fraction (HFrEF) and are associated with increased mortality. Previous trials evaluating adaptive servo-ventilation (ASV) for CSA/CSR in HFrEF have yielded conflicting safety outcomes. This analysis aims to determine the sample size and number of events required to reliably detect safety signals, specifically all-cause mortality, in future trials assessing new treatments for CSA/CSR in HFrEF patients.
methodsUtilizing data from the SERVE-HF trial, we conducted event-driven, non-inferiority sample size calculations focusing on all-cause mortality as the primary endpoint. The analysis employed a one-sided alpha of 0.025 and 80% power, considering hazard ratios (HRs) ranging from 1.15 to 1.35, in line with FDA guidelines for cardiovascular safety assessments. Calculations assumed a control group event rate of 0.093 events/year, a 5-year accrual period, and a minimum follow-up of 3 years.
resultsTo detect a non-inferiority margin with an HR of 1.35, a trial would require at least 851 patients and 349 events. More stringent margins necessitate larger sample sizes; for an HR of 1.15, 3,924 patients and 1,607 events are needed. These findings underscore the importance of adequately powered studies to detect potential safety concerns in this patient population.
conclusionFuture clinical trials investigating treatments for CSA/CSR in HFrEF patients must incorporate sufficient sample sizes and event counts to ensure reliable detection of safety signals, particularly concerning all-cause mortality. This approach is critical for accurately assessing the risk-benefit profile of new therapeutic interventions.
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