Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029.
Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029.
Proc Natl Acad Sci U S A. 2021 Sep 14;118(37). doi: 10.1073/pnas.2104019118.
At present, the QT interval on the electrocardiographic (ECG) waveform is the most common metric for assessing an individual's susceptibility to ventricular arrhythmias, with a long QT, or, at the cellular level, a long action potential duration (APD) considered high risk. However, the limitations of this simple approach have long been recognized. Here, we sought to improve prediction of arrhythmia susceptibility by combining mechanistic mathematical modeling with machine learning (ML). Simulations with a model of the ventricular myocyte were performed to develop a large heterogenous population of cardiomyocytes ( = 10,586), and we tested each variant's ability to withstand three arrhythmogenic triggers: 1) block of the rapid delayed rectifier potassium current (I Block), 2) augmentation of the L-type calcium current (I Increase), and 3) injection of inward current (Current Injection). Eight ML algorithms were trained to predict, based on simulated AP features in preperturbed cells, whether each cell would develop arrhythmic dynamics in response to each trigger. We found that APD can accurately predict how cells respond to the simple Current Injection trigger but cannot effectively predict the response to I Block or I Increase. ML predictive performance could be improved by incorporating additional AP features and simulations of additional experimental protocols. Importantly, we discovered that the most relevant features and experimental protocols were trigger specific, which shed light on the mechanisms that promoted arrhythmia formation in response to the triggers. Overall, our quantitative approach provides a means to understand and predict differences between individuals in arrhythmia susceptibility.
目前，心电图（ECG）波形上的 QT 间期是评估个体易患室性心律失常的最常用指标，长 QT 间期或在细胞水平上，长动作电位持续时间（APD）被认为是高风险的。然而，这种简单方法的局限性早已被认识到。在这里，我们试图通过将机械数学建模与机器学习（ML）相结合来提高心律失常易感性的预测。使用心室肌细胞模型进行模拟，以开发出大量异质的心肌细胞群体（= 10586），并测试每种变体抵抗三种致心律失常触发的能力：1）快速延迟整流钾电流（I 阻断）阻断，2）L 型钙电流（I 增加）增强，以及 3）内向电流（电流注入）注入。八种 ML 算法被训练来预测，基于预先扰动细胞中的模拟 AP 特征，每个细胞是否会对每个触发产生心律失常动力学。我们发现 APD 可以准确预测细胞对简单的电流注入触发的反应，但不能有效地预测对 I 阻断或 I 增加的反应。通过结合其他 AP 特征和模拟其他实验方案，可以提高 ML 预测性能。重要的是，我们发现最相关的特征和实验方案是特定于触发的，这揭示了在触发作用下促进心律失常形成的机制。总的来说，我们的定量方法提供了一种理解和预测个体心律失常易感性差异的方法。
