Abstract 11178: Non-Invasive Identification of Fibrotic Atrial Cardiomyopathy with Neural Networks Based on 642,400 12-Lead Ecgs from an Extensive Cohort Simulation Study
Abstract 11178: Non-Invasive Identification of Fibrotic Atrial Cardiomyopathy with Neural Networks Based on 642,400 12-Lead Ecgs from an Extensive Cohort Simulation Study
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摘要 11178：基于广泛队列模拟研究中的 642,400 个 12 导联心电图，利用神经网络对纤维化心房心肌病进行无创识别
DOI：
10.1161/circ.144.suppl_1.11178
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发表时间：
2021
期刊：
影响因子：
37.8
通讯作者：
A. Loewe
中科院分区：
文献类型：
--
作者：
C. Nagel;O. Doessel;A. Loewe
纤维化心房心肌病的临床表现是由于心房心肌组织被纤维化基质部分替代所致。经过电气和结构重塑过程，该基材提供了维持心房颤动所需的特性，因此可以成为良好的风险标记。我们假设 12 导联心电图中的 P 波提供了一种非侵入性且经济有效的工具来量化心房纤维化的程度，作为最先进方法的替代方法。我们的虚拟患者队列由 80 种心房几何形状、25 种胸廓几何形状和 27 种心房旋转角度变化组成。对于每个心房模型，通过用表现出重塑的离子和传导速度特性的纤维化组织斑块替换心肌体积的不同部分来定义纤维化的 10 个阶段。对具有一到三个不同的健康基线传导速度的每个模型组合进行窦性心律的电生理模拟，产生了 642,400 个合成心电图。计算并提供 P 波持续时间、离散度、峰峰值振幅和 V1 终端力，以及心房体积和躯干尺寸，作为回归神经网络的输入，以估计心房纤维化的体积分数。真实值与估计的纤维化程度之间的均方根误差为左心房纤维化体积的 8.74%。根据回归结果，区分健康队列和患病队列的准确度为 99.01%，敏感性为 99.57%，特异性为 62.38%。总之，我们在一项广泛的队列模拟研究中评估了 12 导联心电图在估计纤维化心房容积分数方面的潜力，该研究包括功能和解剖学方面的受试者间变异性。下一步，需要使用临床数据来验证这些方法，而临床数据需要强大的特征提取算法。
The clinical picture of fibrotic atrial cardiomyopathy is caused by a partial replacement of atrial myocardial tissue with fibrotic substrate. Being subject to electrical and structural remodeling processes, the substrate provides the necessary properties for the maintenance of atrial fibrillation and could thus be a good risk marker. We hypothesize that the P waves in the 12-lead ECG present a non-invasive and cost-effective tool to quantify the extent of atrial fibrosis as an alternative to state-of-the-art approaches. Our virtual patient cohort comprised a combination of 80 atrial, 25 thoracic geometries and 27 rotation angle variations of the atria. For each atrial model, 10 stages of fibrosis were defined by replacing different fractions of the myocardial volume with fibrotic tissue patches exhibiting remodeled ionic and conduction velocity properties. Conducting electrophysiological simulations in sinus rhythm on each model combination with one to three different healthy baseline conduction velocities yielded 642,400 synthetic ECGs. P wave duration, dispersion, peak-to-peak amplitudes and terminal force in V1 were calculated and served, together with atrial volumes and torso sizes, as an input for a regression neural network to estimate the volume fraction of atrial fibrosis. The root mean square error between the ground truth and the estimated extent of fibrosis was 8.74% left atrial fibrotic volume. Based on the regression results, a distinction between the healthy and the diseased cohorts was possible with an accuracy of 99.01%, a sensitivity of 99.57% and a specificity of 62.38%. In conclusion, we have gauged the potential of the 12-lead ECG for estimating fibrotic atrial volume fractions in an extensive cohort simulation study comprising both, functional and anatomical inter-subject variability. As a next step, the methods need to be validated with clinical data for which robust feature extraction algorithms are required.