Heart Rate Variability’s Value in Predicting Out-of-Hospital Major Adverse Cardiovascular Events in Patients With Chronic Heart Failure

Abstract

Background: Chronic heart failure (CHF) involves changes in cardiac structure and function, along with extensive neuroendocrine adaptations and metabolic abnormalities. Heart rate variability (HRV) is a noninvasive measure of autonomic nervous system function and is associated with mortality in CHF. However, the significance of HRV in predicting major adverse cardiovascular events (MACEs) in CHF patients has not been fully explored. This study was aimed at investigating the predictive value of HRV parameters assessed by 24-h Holter monitoring for MACEs in CHF patients.

Methods: This prospective cohort study included 906 CHF patients from five centers in Xinjiang, China, who underwent Holter monitoring and were followed up. Cox proportional hazards regression models were used to assess the independent associations between HRV parameters and the incidence of MACEs. Receiver operating characteristic (ROC) curve analysis was conducted to determine the predictive accuracy of each HRV parameter, and the incremental predictive value of HRV parameters was evaluated using coherence index (C-index), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results: During a median follow-up of 16 months, 211 (23.3%) MACEs occurred. Cox regression analysis indicated that SDNN (HR: 0.976, 95% CI: 0.970~0.981), triangular index (HR: 0.963, 95% CI: 0.953~0.973), SDNN index (HR: 0.983, 95% CI: 0.974~0.992), SDANN index (HR: 0.974, 95% CI: 0.967~0.981), NN50 (HR: 0.859, 95% CI: 0.787~0.937), rMSSD (HR: 0.980, 95% CI: 0.970~0.989), TP (HR: 0.890, 95% CI: 0.816~0.971), VLF (HR: 0.889, 95% CI: 0.815~0.969), LF (HR: 0.817, 95% CI: 0.743~0.898), and HF (HR: 0.806, 95% CI: 0.728~0.893) were independently associated with MACEs. ROC analysis revealed that the triangular index and SDNN had the highest area under the curve (AUC) for predicting MACEs, with values of 0.699 (95% CI: 0.655~0.743) and 0.711 (95% CI: 0.668~0.753), respectively. Incorporation of HRV parameters into traditional risk models improves the C-index, NRI, and IDI of the model’s predictive ability for MACE and cardiovascular mortality to varying degrees.

Conclusion: SDNN and triangular index demonstrated the strongest predictive abilities; other time–domain and frequency–domain parameters also showed certain predictive values for MACEs.