Abstract
BACKGROUND: Identifying the risk of stroke in patients with atrial fibrillation (AF) presents a challenge, as existing tools such as the CHA2DS2-VASc score primarily emphasize medical history, affording relatively less attention to the electrocardiogram (ECG). Exploring HRV (heart rate-related variability) parameters could help refine risk prediction for thromboembolic ischemic stroke and enhance personalized management. This paper investigates how HRV parameters may inform the risk of acute ischemic stroke in atrial fibrillation patients. METHODS: Data were retrospectively collected from Zhongnan Hospital's electronic registry for patients with atrial fibrillation admitted in 2022. The sample was sorted into cases and controls based on specific inclusion and criteria. The outcome was the onset of acute ischemic stroke (AIS) in recurrent paroxysmal non-valvular atrial fibrillation. Cases and control data were analyzed using different statistical methods based on distribution, and the association between HRV parameters and the risk of stroke in paroxysmal atrial fibrillation was assessed. Binary logistic regression was utilized to assess a predictive model for stroke risk based on heart rate variability (HRV) parameters. RESULTS: A total of 3218 medical records with atrial fibrillation were retrieved. Out of 3218 medical records with atrial fibrillation, 192 were selected after screening, including 93 cases and 99 controls. The cohort comprised 93 females (50 cases) and 99 males (43 cases). Mean ages were 68.67 ± 1.95 for cases and 68.35 ± 1.19 for controls, with no significant difference (P > 0.05). Hypertension history was significantly more common in cases (P = 0.006), indicating a dependency between hypertension and acute ischemic stroke in non-valvular paroxysmal AF. SDNN mean rank was significantly lower in cases (P = 0.024), and LF was significantly reduced (P = 0.044). Other HRV parameters showed lower values in cases, but differences were not statistically significant. We found that AVTD (abnormal variation in the time domain) alone was significantly associated with acute ischemic stroke in non-valvular paroxysmal AF. While AVFD (abnormal variation in frequency domain) and AVA (abnormal variation in arrhythmogenicity) were not individually significant, a combined variable (CDV) from AVTD, AVFD, and AVA was also statistically significant (P = 0.029). In this model, SDNN, AVTD, history of hypertension, as well as the CHA2DS2-VASc score and the use of novel anticoagulant medicines (NOACS), were significant predictors in both univariate and multivariate analyses. At the same time, CDV was significant only in univariate analysis. These findings may suggest that HRV parameters may provide clues to the risk of acute ischemic stroke in non-valvular paroxysmal AF. CONCLUSION: This study found that abnormal variation in HRV parameters was more observed in patients with acute ischemic stroke. Then, unlike previous research, this study uniquely integrated multiple HRV parameters to develop novel indices such as AVTD, AVFD, AVA, and CDV, which somehow demonstrated a significant association with the risk of AIS. These new metrics, combined with conventional risk factors and CHA2DS2-VASc score, brought great improvement to the model prediction for AIS risk.