Data_Sheet_1_In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation.pdf

Catheter ablation therapy for persistent atrial fibrillation (AF) typically includes pulmonary vein isolation (PVI) and may include additional ablation lesions that target patient-specific anatomical, electrical, or structural features. Clinical centers employ different ablation strategies, which use imaging data together with electroanatomic mapping data, depending on data availability. The aim of this study was to compare ablation techniques across a virtual cohort of AF patients. We constructed 20 paroxysmal and 30 persistent AF patient-specific left atrial (LA) bilayer models incorporating fibrotic remodeling from late-gadolinium enhancement (LGE) MRI scans. AF was simulated and post-processed using phase mapping to determine electrical driver locations over 15 s. Six different ablation approaches were tested: (i) PVI alone, modeled as wide-area encirclement of the pulmonary veins; PVI together with: (ii) roof and inferior lines to model posterior wall box isolation; (iii) isolating the largest fibrotic area (identified by LGE-MRI); (iv) isolating all fibrotic areas; (v) isolating the largest driver hotspot region [identified as high simulated phase singularity (PS) density]; and (vi) isolating all driver hotspot regions. Ablation efficacy was assessed to predict optimal ablation therapies for individual patients. We subsequently trained a random forest classifier to predict ablation response using (a) imaging metrics alone, (b) imaging and electrical metrics, or (c) imaging, electrical, and ablation lesion metrics. The optimal ablation approach resulting in termination, or if not possible atrial tachycardia (AT), varied among the virtual patient cohort: (i) 20% PVI alone, (ii) 6% box ablation, (iii) 2% largest fibrosis area, (iv) 4% all fibrosis areas, (v) 2% largest driver hotspot, and (vi) 46% all driver hotspots. Around 20% of cases remained in AF for all ablation strategies. The addition of patient-specific and ablation pattern specific lesion metrics to the trained random forest classifier improved predictive capability from an accuracy of 0.73 to 0.83. The trained classifier results demonstrate that the surface areas of pre-ablation driver regions and of fibrotic tissue not isolated by the proposed ablation strategy are both important for predicting ablation outcome. Overall, our study demonstrates the need to select the optimal ablation strategy for each patient. It suggests that both patient-specific fibrosis properties and driver locations are important for planning ablation approaches, and the distribution of lesions is important for predicting an acute response.

针对持续性房颤（AF）的导管消融治疗通常包括肺静脉隔离（PVI）以及可能涉及针对患者特定解剖、电生理或结构特征的额外消融病变。临床中心采用不同的消融策略，这些策略结合成像数据与电生理学映射数据，依据数据可获得性而有所不同。本研究旨在比较一个虚拟房颤患者队列中的消融技术。我们构建了20个阵发性与30个持续性房颤患者特异性的左心房（LA）双层模型，这些模型整合了来自延迟钆增强（LGE）MRI扫描的纤维化重塑。通过相位映射模拟并后处理AF，以确定15秒内的电驱动位置。测试了六种不同的消融方法：（i）单独的PVI，模拟为肺静脉的广泛包围；（ii）PVI结合屋顶线和下线以模拟后壁箱隔离；（iii）隔离最大的纤维化区域（由LGE-MRI识别）；（iv）隔离所有纤维化区域；（v）隔离最大的驱动热点区域[识别为高模拟相位奇点（PS）密度]；（vi）隔离所有驱动热点区域。评估消融效果以预测针对单个患者的最佳消融治疗方案。随后，我们训练了一个随机森林分类器，用于通过（a）仅使用成像指标，（b）成像和电生理指标，或（c）成像、电生理和消融病变指标来预测消融反应。在虚拟患者队列中，导致终止或如不可能则导致房性心动过速（AT）的最佳消融方法各不相同：（i）20%单独PVI，（ii）6%箱状消融，（iii）2%最大的纤维化区域，（iv）4%所有纤维化区域，（v）2%最大的驱动热点，以及（vi）46%所有驱动热点。大约20%的病例在所有消融策略下均保持AF状态。将患者特异性和消融模式特定的病变指标添加到训练的随机森林分类器中，提高了预测能力，从准确率0.73提升至0.83。训练的分类器结果显示，消融前驱动区域表面积和未由所提消融策略隔离的纤维化组织表面积对于预测消融结果都至关重要。总体而言，我们的研究证明了为每位患者选择最佳消融策略的必要性。它表明，患者特定的纤维化特性和驱动位置对于规划消融方法是重要的，而病变的分布对于预测急性反应同样重要。