Long-term prediction of mortality by heart rate turbulence in hemodialysis patients and the impact of diabetes mellitus–a longitudinal observational study

BACKGROUND: Diabetes-driven impaired autonomic nervous system function might contribute to increased mortality in hemodialysis patients. Our study aimed to validate heart rate turbulence as a long-term predictor of mortality in this vulnerable cohort. METHODS: Heart rate turbulence is a non-invasive, 24 h electrocardiography-Holter-based assessment of cardiovascular autonomic responses. Hemodialysis patients of the "rISk strAtification in end-stage Renal disease" (ISAR) study, a prospective, multicenter observational study, were followed up for six years. Mortality hazard, and correlations between clinical characteristics and mortality, were assessed using Cox regression models. RESULTS: Heart rate turbulence measurement at baseline was available in 290 hemodialysis patients, 99 (34%) with diabetes mellitus. In a multivariable analysis, abnormal heart rate turbulence was associated with a 2.1-fold (95% CI: 1.4-3.2; p < 0.001) increased risk for all-cause and 3.1-fold (95% CI: 1.5-6.2; p = 0.001) increased risk for cardiovascular mortality. The co-occurrence of abnormal heart rate turbulence and diabetes mellitus represented the strongest risk constellation, increasing all-cause mortality risk to a hazard ratio of 5.8 (95% CI: 3.3-10.4; p < 0.001) and cardiovascular mortality risk to 6.1 (95% CI: 2.5-15.1; p < 0.001). This association with mortality risk remained significant after multivariate adjustment. The interaction term between the two comorbidities indicated an approximately additive effect on mortality risk. CONCLUSIONS: Heart rate turbulence significantly contributed to the prediction of long-term mortality risk in hemodialysis patients. Diabetes mellitus is a major driver of cardiovascular autonomic dysfunction, which plays a crucial role in mortality among dialysis patients. Heart rate turbulence measurement identifies high-risk patients in the dialysis setting, enhancing precision in risk prediction and stratification, and allowing an opportunity for personalized monitoring and prevention.