Using machine learning to identify local cellular properties that support re-entrant activation in patient-specific models of atrial fibrillation.
Using machine learning to identify local cellular properties that support re-entrant activation in patient-specific models of atrial fibrillation.
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DOI：
10.1093/europace/euaa386
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发表时间：
2021-03-04
期刊：
影响因子：
--
通讯作者：
Niederer S
中科院分区：
文献类型：
--
作者：
Corrado C;Williams S;Roney C;Plank G;O'Neill M;Niederer S
心房颤动（AF）由折返激动模式维持。已经提出了消融策略，其靶向可能支持折返激活模式的组织区域。我们的目的是表征与经验证的虚拟患者队列中的系留折返激活模式的区域相关的组织特性。生成房颤患者特定模型（7个阵发性和3个持续性），并分别根据冠状窦和高位右心房的S1-S2起搏方案期间的局部激动时间（LAT）测量值进行验证。心房模型从每个肺静脉附近的三个位置用短阵起搏刺激，以启动折返激动模式。5个心房表现出至少80 s的持续激动模式。最大动作电位时程（APD）较短的模型与持续激活相关。相位奇点被映射到整个心房持续激活模式。最大传导速度（CV）较低的区域与相位奇点的束缚有关。在最大局部传导速度和动作电位持续时间上训练支持向量机（SVM），以识别束缚相位奇点的区域。SVM以91%的准确度识别出可以支持栓系的组织区域。当SVM也在表面积上进行训练时，准确率提高到95%。在虚拟患者队列中，可以在临床上测量（CV）或估计（APD;使用有效不应期作为替代）的局部组织特性识别了束缚相位奇点的组织区域。将CV和APD与心房表面积相结合，进一步提高了识别束缚相位奇异性区域的准确性。
Atrial fibrillation (AF) is sustained by re-entrant activation patterns. Ablation strategies have been proposed that target regions of tissue that may support re-entrant activation patterns. We aimed to characterize the tissue properties associated with regions that tether re-entrant activation patterns in a validated virtual patient cohort. Atrial fibrillation patient-specific models (seven paroxysmal and three persistent) were generated and validated against local activation time (LAT) measurements during an S1–S2 pacing protocol from the coronary sinus and high right atrium, respectively. Atrial models were stimulated with burst pacing from three locations in the proximity of each pulmonary vein to initiate re-entrant activation patterns. Five atria exhibited sustained activation patterns for at least 80 s. Models with short maximum action potential durations (APDs) were associated with sustained activation. Phase singularities were mapped across the atria sustained activation patterns. Regions with a low maximum conduction velocity (CV) were associated with tethering of phase singularities. A support vector machine (SVM) was trained on maximum local conduction velocity and action potential duration to identify regions that tether phase singularities. The SVM identified regions of tissue that could support tethering with 91% accuracy. This accuracy increased to 95% when the SVM was also trained on surface area. In a virtual patient cohort, local tissue properties, that can be measured (CV) or estimated (APD; using effective refractory period as a surrogate) clinically, identified regions of tissue that tether phase singularities. Combing CV and APD with atrial surface area further improved the accuracy in identifying regions that tether phase singularities.
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