ArticleFrontiers in medicine2026
Resting heart rate as an independent predictor of major adverse cardiovascular events in patients receiving maintenance hemodialysis: a prospective cohort study.
Article in Frontiers in medicine, 2026. 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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Abstract
Background: Existing clinical evidence in the literature regarding the association between resting heart rate (RHR) and major adverse cardiovascular events (MACE) in patients receiving maintenance hemodialysis (MHD) remains unclear and inconsistent. Objective: To determine whether RHR independently predicts major adverse cardiovascular events (MACE) in patients receiving maintenance hemodialysis (MHD). Methods: We conducted a prospective cohort study in which 309 adult MHD patients receiving maintenance hemodialysis were stratified into three groups according to the 33rd and 66th percentiles of baseline RHR. The primary outcome was incidence of MACE. Univariaable and multivariaable logistic regression analyses were performed, followed by internal validation of the final multivariable model using bootstrap resampling. A nomogram, calibration plots, and decision curve analysis (DCA) were used to evaluate model performance and clinical utility. It is noteworthy that heart failure events were identified based on the documented clinical diagnosis made by the treating physician after comprehensive assessment of symptoms, signs, medical history, and available investigations. Although BNP measurements and echocardiographic assessments were not available for all patients, their absence did not preclude a clinical diagnosis of heart failure in routine practice. Results: The incidence of total MACE increased progressively across RHR tertiles (T1: 20.5%, T2: 38.0%, T3: 52.6%; Conclusion: In this single-center cohort of patients receiving maintenance hemodialysis, elevated RHR was independently associated with an increased risk of MACE after adjustment for psychosocial and metabolic factors, including HADS score, diabetes, uric acid, and iPTH. Incorporating RHR and these clinical variables into a risk model may facilitate early identification of high-risk patients for targeted intervention, although external validation is required before broader clinical application.
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