# Evaluation of the clinical value of heart rate variability in predicting vasovagal syncope

## TL;DR

This study evaluates how heart rate variability (HRV) can help predict vasovagal syncope, a common type of fainting, using data from Holter monitoring and tilt tests.

## Contribution

The study demonstrates that specific HRV parameters can independently predict vasovagal syncope with moderate accuracy.

## Key findings

- HRV parameters like 24-hour average heart rate and SDNN were independently associated with vasovagal syncope.
- ROC analysis showed moderate predictive ability of HRV metrics for identifying vasovagal syncope.
- HRV could aid in noninvasive screening and classification of vasovagal syncope patients.

## Abstract

Vasovagal syncope (VVS) is the most common type of reflex syncope. Although typically benign in its clinical course, VVS may lead to injury and reduced quality of life. Autonomic nervous system imbalance is considered the core pathophysiological mechanism of VVS. Heart rate variability (HRV), a noninvasive marker of autonomic regulation, may have practical value in identifying VVS and its subtypes; however, its predictive utility has not been fully elucidated.

In this single-center retrospective case-control study, we included 415 patients with syncope symptoms who underwent both 24-hour Holter monitoring and a head-up tilt test (HUTT) between January 2021 and December 2024. Based on HUTT results, patients were classified into a VVS-positive group (n = 279) and a control group (n = 136). HRV parameters extracted from Holter recordings included 24 h average, maximum and minimum heart rates (HRs), standard deviation of NN intervals (SDNN), triangular index (TI), root mean square of successive differences (rMSSD), and the percentage of NN intervals differing by more than 50 ms (pNN50). Associations and predictive performance were assessed using logistic regression and receiver operating characteristic (ROC) analysis.

Multivariable logistic regression revealed that 24 h average HRs (OR: 0.935; 95% CI: 0.912–0.959; P < 0.001), 24 h maximum HRs (OR: 0.976; 95% CI: 0.964–0.989; P < 0.001), 24 h minimum HRs (OR: 0.947; 95% CI: 0.915–0.980; P = 0.002), TI (OR: 1.032; 95% CI: 1.009–1.056; P = 0.006), SDNN (OR: 1.029; 95% CI: 1.016–1.043; P < 0.001), rMSSD (OR: 1.023; 95% CI: 1.007–1.038; P = 0.004), and pNN50 (OR: 1.028; 95% CI: 1.006–1.051; P = 0.013) were independently associated with the occurrence of VVS. ROC analysis showed that 24 h average HRs (AUC: 0.688; 95% CI: 0.632–0.744), 24 h maximum HRs (AUC: 0.652; 95% CI: 0.594–0.709), and SDNN (AUC: 0.614; 95% CI: 0.557–0.672) exhibited moderate predictive ability for VVS.

HRV parameters are associated with the occurrence of VVS. As a noninvasive and continuous physiological biomarker, HRV may aid in the clinical screening, risk stratification, and phenotypic classification of patients with suspected VVS.