Department of Electrocardiology, Shaoxing People's Hospital, 312000 Shaoxing, Zhejiang, China.
Discov Med. 2024 Aug;36(187):1610-1615. doi: 10.24976/Discov.Med.202436187.147.
Atrial fibrillation (AF) is the most common type of arrhythmia. Heart rate variability (HRV) may be associated with AF risk. The aim of this study was to test HRV indices and arrhythmias as predictors of paroxysmal AF based on 24-hour dynamic electrocardiogram recordings of patients.
A total of 199 patients with paroxysmal AF (AF group) and 204 elderly volunteers over 60 years old (Control group) who underwent a 24-hour dynamic electrocardiogram from August 2022 to March 2023 were included. Time-domain indices, frequency-domain indices, and arrhythmia data of the two groups were classified and measured. Binary logistic regression analysis was performed on variables with significant differences to identify independent risk factors. A nomogram prediction model was established, and the sum of individual scores of each variable was calculated.
Gender, age, body mass index and low-density lipoprotein (LDL) did not differ significantly between AF and Control groups ( > 0.05), whereas significant group differences were found for smoking, hypertension, diabetes, and high-density lipoprotein (HDL) ( < 0.05). The standard deviation of all normal to normal (NN) R-R intervals (SDNN), standard deviation of 5-minute average NN intervals (SDANN), root mean square of successive NN interval differences (rMSSD), 50 ms from the preceding interval (pNN50), low-frequency/high-frequency (LF/HF), LF, premature atrial contractions (PACs), atrial tachycardia (AT), T-wave index, and ST-segment index differed significantly between the two groups. Logistic regression analysis identified rMSSD, PACs, and AT as independent predictors of AF. For each unit increase in rMSSD and PACs, the odds of developing AF increased by 1.0357 and 1.0005 times, respectively. For each unit increase in AT, the odds of developing AF decreased by 0.9976 times. The total score of the nomogram prediction model ranged from 0 to 110.
The autonomic nervous system (ANS) plays a pivotal role in the occurrence and development of AF. The individualized nomogram prediction model of AF occurrence contributes to the early identification of high-risk patients with AF.
心房颤动（AF）是最常见的心律失常类型。心率变异性（HRV）可能与 AF 风险相关。本研究旨在通过对 2022 年 8 月至 2023 年 3 月期间接受 24 小时动态心电图检查的患者进行分析，检测 HRV 指标和心律失常是否可作为阵发性 AF 的预测因子。
共纳入 199 例阵发性 AF 患者（AF 组）和 204 例 60 岁以上老年志愿者（对照组），均进行 24 小时动态心电图检查。对两组的时域指标、频域指标和心律失常数据进行分类和测量。对差异有统计学意义的变量进行二元逻辑回归分析，以确定独立危险因素。建立列线图预测模型，并计算每个变量的个体得分之和。
AF 组与对照组之间的性别、年龄、体重指数和低密度脂蛋白（LDL）无显著差异（>0.05），而吸烟、高血压、糖尿病和高密度脂蛋白（HDL）有显著差异（<0.05）。所有正常窦性 R-R 间期标准差（SDNN）、5 分钟平均窦性 R-R 间期标准差（SDANN）、窦性 R-R 间期差值的均方根（rMSSD）、前一个间期 50ms 处的窦性 R-R 间期（pNN50）、低频/高频（LF/HF）、LF、房性期前收缩（PACs）、房性心动过速（AT）、T 波指数和 ST 段指数在两组之间有显著差异。逻辑回归分析确定 rMSSD、PACs 和 AT 是 AF 的独立预测因子。rMSSD 和 PACs 每增加 1 个单位，发生 AF 的几率分别增加 1.0357 倍和 1.0005 倍。AT 每增加 1 个单位，发生 AF 的几率降低 0.9976 倍。列线图预测模型的总分为 0 至 110。
自主神经系统（ANS）在 AF 的发生和发展中起着关键作用。AF 发生的个体化列线图预测模型有助于早期识别 AF 高危患者。
