Cumulative resting heart rate load and cardiovascular risk in patients with heart failure in sinus rhythm.

Rev Esp Cardiol (Engl Ed)

Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China. Electronic address:

Published: August 2025


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Article Abstract

Introduction And Objectives: Resting heart rate is a readily available vital sign with important prognostic significance. However, traditional measures overlook both the magnitude and duration of elevated heart rate over time. This study assessed the association between cumulative resting heart rate load and adverse outcomes in patients with chronic heart failure (HF) in sinus rhythm.

Methods: Data from 5 randomized controlled trials (BEST, GUIDE-IT, HF-ACTION, RELAX, and TOPCAT) were analyzed. Cumulative heart rate load was calculated as the area under the curve (AUC) for heart rate ≥70 beats per minute (bpm), relative to the total AUC prior to outcomes. The primary outcome was major adverse cardiac events (MACE), defined as the composite of cardiovascular death and hospitalization for HF. Cox proportional hazards regression models were used to examine associations with outcomes.

Results: A total of 5428 patients were included. Higher cumulative resting heart rate load was significantly associated with increased risk of MACE (hazard ratio [HR], 1.31; 95% CI, 1.24-1.38), cardiovascular death (HR, 1.17; 95% CI, 1.08-1.27), hospitalization for HF (HR, 1.34; 95% CI, 1.26-1.43), all-cause death (HR, 1.20; 95% CI, 1.12-1.29), and any hospitalization (HR, 1.20; 95% CI, 1.15-1.25). Cumulative resting heart rate load demonstrated superior predictive value for all outcomes compared with baseline heart rate, mean heart rate, heart rate standard deviation, and heart rate time in the target range (P < .001 for all comparisons).

Conclusions: Cumulative resting heart rate load provides stronger prognostic value for adverse outcomes in chronic HF. Incorporating this parameter into clinical practice may improve risk stratification and help identify high-risk patients who could benefit from intensive monitoring or therapeutic interventions.

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http://dx.doi.org/10.1016/j.rec.2025.07.006DOI Listing

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