Dasí Albert, Roy Aditi, Sachetto Rafael, Camps Julia, Bueno-Orovio Alfonso, Rodriguez Blanca
Department of Computer Science, University of Oxford, Oxford, United Kingdom.
Departamento de Ciência da Computação, Universidade Federal De São João Del-Rei, São João del Rei, Brazil.
Front Physiol. 2022 Sep 15;13:966046. doi: 10.3389/fphys.2022.966046. eCollection 2022.
Atrial fibrillation (AF) inducibility, sustainability and response to pharmacological treatment of individual patients are expected to be determined by their ionic current properties, especially in structurally-healthy atria. Mechanisms underlying AF and optimal cardioversion are however still unclear. In this study, in-silico drug trials were conducted using a population of human structurally-healthy atria models to 1) identify key ionic current properties determining AF inducibility, maintenance and pharmacological cardioversion, and 2) compare the prognostic value for predicting individual AF cardioversion of ionic current properties and electrocardiogram (ECG) metrics. In the population of structurally-healthy atria, 477 AF episodes were induced in ionic current profiles with both steep action potential duration (APD) restitution (eliciting APD alternans), and high excitability (enabling propagation at fast rates that transformed alternans into discordant). High excitability also favored 211 sustained AF episodes, so its decrease, through prolonged refractoriness, explained pharmacological cardioversion. In-silico trials over 200 AF episodes, 100 ionic profiles and 10 antiarrhythmic compounds were consistent with previous clinical trials, and identified optimal treatments for individual electrophysiological properties of the atria. Algorithms trained on 211 simulated AF episodes exhibited >70% accuracy in predictions of cardioversion for individual treatments using either ionic current profiles or ECG metrics. In structurally-healthy atria, AF inducibility and sustainability are enabled by discordant alternans, under high excitability and steep restitution conditions. Successful pharmacological cardioversion is predicted with 70% accuracy from either ionic or ECG properties, and it is optimal for treatments maximizing refractoriness (thus reducing excitability) for the given ionic current profile of the atria.
预计个体患者的心房颤动(AF)诱导性、持续性及对药物治疗的反应取决于其离子电流特性,尤其是在结构正常的心房中。然而,AF的潜在机制及最佳复律方法仍不明确。在本研究中,使用一组人类结构正常的心房模型进行了计算机模拟药物试验,以:1)确定决定AF诱导性、维持及药物复律的关键离子电流特性;2)比较离子电流特性和心电图(ECG)指标对预测个体AF复律的预后价值。在结构正常的心房组中,在具有陡峭动作电位时程(APD)恢复(引发APD交替)和高兴奋性(使快速传导得以发生,将交替转变为不一致)的离子电流图谱中诱发了477次AF发作。高兴奋性也有利于211次持续性AF发作,因此通过延长不应期使其降低可解释药物复律。对200次AF发作、100种离子图谱和10种抗心律失常化合物进行的计算机模拟试验与先前的临床试验一致,并确定了针对心房个体电生理特性的最佳治疗方法。在211次模拟AF发作上训练的算法在使用离子电流图谱或ECG指标对个体治疗的复律预测中准确率超过70%。在结构正常的心房中,在高兴奋性和陡峭恢复条件下,不一致的交替引发AF诱导性和持续性。根据离子或ECG特性预测成功的药物复律准确率为70%,对于使给定心房离子电流图谱的不应期最大化(从而降低兴奋性)的治疗是最佳的。