Computationally guided personalized targeted ablation of persistent atrial fibrillation
Computationally guided personalized targeted ablation of persistent atrial fibrillation
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DOI：
10.1038/s41551-019-0437-9
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发表时间：
2019-11-01
影响因子：
28.1
通讯作者：
Trayanova, Natalia A.
中科院分区：
文献类型：
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作者：
Boyle, Patrick M.;Zghaib, Tarek;Trayanova, Natalia A.
心房颤动（AF）-最常见的房颤-显著增加中风和心力衰竭的风险。虽然导管消融术可以恢复正常的心律，但发生心房纤维化的持续性AF患者通常会经历多次消融失败，从而增加手术风险。在这里，我们提出了个性化的计算模型，用于可靠的预先确定消融靶点，然后用于指导持续性房颤和心房纤维化患者的消融手术。首先，我们展示了患者心房的计算模型识别了纤维化组织，如果消融，这些组织将不会维持AF。我们报告了在临床标测系统中整合靶消融部位并在10例持续性房颤患者中测试其可行性的结果。消融靶点的计算预测避免了冗长的电标测，并可提高靶向房颤消融的准确性和有效性，患者，同时消除了重复手术的需要。
Atrial fibrillation (AF)-the most common arrhythmia-significantly increases the risk of stroke and heart failure. Although catheter ablation can restore normal heart rhythms, patients with persistent AF who develop atrial fibrosis often undergo multiple failed ablations, and thus increased procedural risks. Here, we present personalized computational modelling for the reliable predetermination of ablation targets, which are then used to guide the ablation procedure in patients with persistent AF and atrial fibrosis. First, we show that a computational model of the atria of patients identifies fibrotic tissue that, if ablated, will not sustain AF. Then, we report the results of integrating the target ablation sites in a clinical mapping system and testing its feasibility in ten patients with persistent AF. The computational prediction of ablation targets avoids lengthy electrical mapping and could improve the accuracy and efficacy of targeted AF ablation in patients while eliminating the need for repeat procedures.