PDF(Pubmed)
Abstract：
Introduction: Atrial fibrillation (AF) is the most common arrhythmia, associated with significant burdens to patients and the healthcare system. The atrioventricular (AV) node plays a vital role in regulating heart rate during AF by filtering electrical impulses from the atria. However, it is often insufficient in regards to maintaining a healthy heart rate, thus the AV node properties are modified using rate-control drugs. Moreover, treatment selection during permanent AF is currently done empirically. Quantifying individual differences in diurnal and short-term variability of AV-nodal function could aid in personalized treatment selection. Methods: This study presents a novel methodology for estimating the refractory period (RP) and conduction delay (CD) trends, and their uncertainty in the two pathways of the AV node during 24 h using non-invasive data. This was achieved by utilizing a network model together with a problem-specific genetic algorithm and an approximate Bayesian computation algorithm. Diurnal variability in the estimated RP and CD was quantified by the difference between the daytime and nighttime estimates, and short-term variability was quantified by the Kolmogorov-Smirnov distance between adjacent 10-min segments in the 24-h trends. Additionally, the predictive value of the derived parameter trends regarding drug outcome was investigated using several machine learning tools. Results: Holter electrocardiograms from 51 patients with permanent AF during baseline were analyzed, and the predictive power of variations in RP and CD on the resulting heart rate reduction after treatment with four rate control drugs was investigated. Diurnal variability yielded no correlation to treatment outcome, and no prediction of drug outcome was possible using the machine learning tools. However, a correlation between the short-term variability for the RP and CD in the fast pathway and resulting heart rate reduction during treatment with metoprolol (ρ = 0.48, p < 0.005 in RP, ρ = 0.35, p < 0.05 in CD) were found. Discussion: The proposed methodology enables non-invasive estimation of the AV node properties during 24 h, which-indicated by the correlation between the short-term variability and heart rate reduction-may have the potential to assist in treatment selection.
摘要：
简介：心房颤动（AF）是最常见的心律失常，与患者和医疗保健系统的重大负担有关。房室（AV）结通过过滤心房的电脉冲在AF期间调节心率中起着至关重要的作用。然而,它通常不足以保持健康的心率，因此，使用速率控制药物可以改变房室结的特性。此外，永久性房颤期间的治疗选择目前是凭经验进行的。量化房室结功能的昼夜和短期变异性的个体差异可以帮助个性化治疗选择。方法：本研究提出了一种新的方法来估计不应期（RP）和传导延迟（CD）趋势，以及使用非侵入性数据在24小时内房室结两条途径的不确定性。这是通过利用网络模型以及特定于问题的遗传算法和近似贝叶斯计算算法来实现的。通过白天和夜间估计之间的差异来量化估计RP和CD的昼夜变化。通过24小时趋势中相邻10分钟段之间的Kolmogorov-Smirnov距离来量化短期变异性。此外,我们使用多种机器学习工具研究了衍生参数趋势对药物结局的预测价值.结果：分析了基线期间51例永久性房颤患者的动态心电图，研究了RP和CD变化对四种心率控制药物治疗后心率降低的预测能力。昼夜变异性与治疗结果无关，并且使用机器学习工具无法预测药物结果。然而,快速途径中RP和CD的短期变异性与美托洛尔治疗期间导致的心率降低之间的相关性（RP中的ρ=0.48，p<0.005，在CD中发现ρ=0.35，p<0.05)。讨论：所提出的方法可以在24小时内对房室结特性进行非侵入性估计，短期变异性和心率降低之间的相关性表明，这可能有助于选择治疗方案。
