The correlation between heart rate variability index and vulnerability prognosis in patients with acute decompensated heart failure

Abstract

Objective To explore the correlation between Heart Rate Variability Index (HRV) and poor prognosis in patients with acute decompensated heart failure (ADHF). Methods A retrospective compilation of clinical data encompassed 128 cases of patients afflicted with acute decompensated heart failure (ADHF) who were admitted to and discharged from our hospital between April 2019 and July 2022. Subsequent to assessing their follow-up progress during the tracking period, the subjects were categorized into two cohorts: the poor prognosis group (n = 31) and the good prognosis group (n = 97). Comparative analysis of clinical data and Heart Rate Variability (HRV) parameters was executed between these two groups. Moreover, a multiple linear regression analysis was employed to identify the contributing factors associated with adverse prognoses in ADHF patients. Furthermore, the receiver operating characteristic (ROC) curve was employed to evaluate the prognostic predictive capability of HRV parameters among ADHF patients. Results The levels of SDNN (t = 3.924, P < 0.001), SDANN (t = 4.520, P < 0.001) and LF (t = 2.676, P = 0.018) in the poor prognosis group were significantly higher than those in the good prognosis group, and the differences were statistically significant (P < 0.05). The levels of PNN50 (t = 2.132, P = 0.035), HF (t = 11.781, P < 0.001) and LF/HF (t = 11.056, P < 0.001) in the poor prognosis group were significantly lower than those in the good prognosis group (P < 0.05). The results of multiple linear regression analysis indicated that SDNN, SDANN, LF, PNN50, and HF were factors influencing poor prognosis in ADHF patients (P < 0.05). The results of the ROC curve analysis indicate that the area under the curve (AUC) for predicting poor prognosis in ADHF patients using HRV parameters were as follows: SDNN (AUC = 0.818, 95% CI [0.722–0.914]), SDANN (AUC = 0.684, 95% CI [0.551–0.816]), PNN50 (AUC = 0.754, 95% CI [0.611–0.841]), LF/HF (AUC = 0.787, 95% CI [0.679–0.896]), and combined diagnosis (AUC = 0.901, 95% CI [0.832–0.970]). Among these, the combined diagnosis exhibited the highest AUC, sensitivity, and specificity for predicting poor prognosis in ADHF patients (P < 0.001). Conclusion The HRV parameters of SDNN, SDANN, PNN50 and LF/HF are closely related to the prognosis of ADHF patients. The combined detection of the above HRV parameters can improve the efficacy of predicting the poor prognosis of ADHF patients. This suggests that clinical staff can identify ADHF patients at risk of poor prognosis by long-term monitoring of HRV in the future.