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, but is often insufficient in regards to maintaining a healthy heart rate. Thus, the AV node properties are modified using rate-control drugs. Hence, quantifying individual differences in diurnal and short-term variability of AV-nodal function could aid in personalized treatment selection. 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 hours using non-invasive data. This was achieved using a network model together with a problem-specific genetic algorithm and an approximate Bayesian computation algorithm. Diurnal and short-term variability in the estimated RP and CD was quantified by the difference between the daytime and nighttime estimates and by the Kolmogorov-Smirnov distance between adjacent 10-minute segments in the 24-hour trends. Holter ECGs 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 ($\rho=0.48, p<0.005$ in RP, $\rho=0.35, p<0.05$ in CD) were found. The proposed methodology enables non-invasive estimation of the AV node properties during 24 hours, which may have the potential to assist in treatment selection.

翻译：心房颤动（AF）是最常见的心律失常，给患者和医疗系统带来沉重负担。房室结在调节AF期间的心率中起关键作用，但常不足以维持健康心率。因此，临床采用心率控制药物调节房室结特性。量化房室结功能的昼夜节律与短期变异性个体差异，有助于个性化治疗方案选择。本研究提出一种新方法，利用无创数据估计24小时内房室结两条传导通路的不应期（RP）和传导延迟（CD）趋势及其不确定性。通过结合网络模型、问题特异性遗传算法与近似贝叶斯计算算法实现该方法。采用日间与夜间估计值之差，以及24小时趋势中相邻10分钟片段间的Kolmogorov-Smirnov距离，量化RP和CD的昼夜与短期变异性。分析51例永久性AF患者基线期动态心电图，研究RP和CD变异性对四种心率控制药物治疗后心率降低的预测能力。昼夜变异与治疗结局无相关性，且机器学习工具无法预测药物结局。但发现快通路RP和CD的短期变异性与美托洛尔治疗期间心率降低存在相关性（RP：$\rho=0.48, p<0.005$；CD：$\rho=0.35, p<0.05$）。该方法可实现24小时内房室结特性的无创估计，有望辅助治疗方案选择。