The Fibrotic Substrate in Persistent Atrial Fibrillation Patients: Comparison Between Predictions From Computational Modeling and Measurements From Focal Impulse and Rotor Mapping.
The Fibrotic Substrate in Persistent Atrial Fibrillation Patients: Comparison Between Predictions From Computational Modeling and Measurements From Focal Impulse and Rotor Mapping.
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DOI：
10.3389/fphys.2018.01151
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发表时间：
2018
影响因子：
4
通讯作者：
Trayanova NA
中科院分区：
文献类型：
--
作者：
Boyle PM;Hakim JB;Zahid S;Franceschi WH;Murphy MJ;Prakosa A;Aronis KN;Zghaib T;Balouch M;Ipek EG;Chrispin J;Berger RD;Ashikaga H;Marine JE;Calkins H;Nazarian S;Spragg DD;Trayanova NA
焦点脉冲和转子标测 (FIRM) 涉及心内检测和折返驱动器 (RD) 的导管消融，其中一些可能导致持续性心房颤动 (PsAF) 中心律失常的持续存在。来自晚期钆增强磁共振成像 (LGE-MRI) 的患者特定计算模型有可能以非侵入性方式识别可能持续 RD 的纤维化基底的所有区域，包括在映射的 AF 发作期间可能不出现 RD 的位置。本研究的目的是通过比较模拟中发现的和 FIRM（RDsim 和 RDFIRM）检测到的 RD 携带区域的位置，对 PsAF 患者纤维化基质的致心律失常倾向进行多模式评估，并分析基于靶向 RD 的消融策略的影响。对于 11 名接受术前 LGE-MRI 和 FIRM 引导消融的 PsAF 患者，我们在个体化心房模型中回顾性模拟 AF，根据消融前 LGE-MRI 扫描重建几何形状和纤维化分布，并确定 RDsim 部位。对包含 RDsim 和 RDFIRM 的区域进行了比较。 RDsim 存在于 38 个心房区域（每个模型的中位数 [四分位数范围 (IQR)] = 4 [3; 4]）。 RDFIRM 在 24 个心房区域（每位患者 2 [1; 3] 个）被识别并随后被消融，这明显少于相应模型中 RDsim 容纳区域的数量 (p < 0.05)。计算模型预测在临床测绘期间确定为 RDFIRM 的 24 个心房区域中的 20 个 (83%) 存在 RDsim。在大量案例中，我们发现了从未观察到 RDFIRM 的 RDsim 隐藏区域（两种模式之间存在差异的 18/22 区域；82%）；我们将此类情况称为“潜在”RDsim 站点。在随访期间（230 [180; 326] 天），7/11 (64%) 的个体发生房颤复发。有趣的是，在与经历复发性 AF 的患者相对应的所有七个计算模型中都观察到了潜在的 RDsim 位点（每个患者 2 [2; 2]）；相比之下，仅在随访期间四分之二的未发生 AF 的患者中发现了潜在 RDsim 位点（每位患者 0.5 [0; 1.5]；与 AF 复发患者相比，p < 0.05）。我们的结论是，基于计算模型的基底消融可以改善结果。
Focal impulse and rotor mapping (FIRM) involves intracardiac detection and catheter ablation of re-entrant drivers (RDs), some of which may contribute to arrhythmia perpetuation in persistent atrial fibrillation (PsAF). Patient-specific computational models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) has the potential to non-invasively identify all areas of the fibrotic substrate where RDs could potentially be sustained, including locations where RDs may not manifest during mapped AF episodes. The objective of this study was to carry out multi-modal assessment of the arrhythmogenic propensity of the fibrotic substrate in PsAF patients by comparing locations of RD-harboring regions found in simulations and detected by FIRM (RDsim and RDFIRM) and analyze implications for ablation strategies predicated on targeting RDs. For 11 PsAF patients who underwent pre-procedure LGE-MRI and FIRM-guided ablation, we retrospectively simulated AF in individualized atrial models, with geometry and fibrosis distribution reconstructed from pre-ablation LGE-MRI scans, and identified RDsim sites. Regions harboring RDsim and RDFIRM were compared. RDsim were found in 38 atrial regions (median [inter-quartile range (IQR)] = 4 [3; 4] per model). RDFIRM were identified and subsequently ablated in 24 atrial regions (2 [1; 3] per patient), which was significantly fewer than the number of RDsim-harboring regions in corresponding models (p < 0.05). Computational modeling predicted RDsim in 20 of 24 (83%) atrial regions identified as RDFIRM-harboring during clinical mapping. In a large number of cases, we uncovered RDsim-harboring regions in which RDFIRM were never observed (18/22 regions that differed between the two modalities; 82%); we termed such cases “latent” RDsim sites. During follow-up (230 [180; 326] days), AF recurrence occurred in 7/11 (64%) individuals. Interestingly, latent RDsim sites were observed in all seven computational models corresponding to patients who experienced recurrent AF (2 [2; 2] per patient); in contrast, latent RDsim sites were only discovered in two of four patients who were free from AF during follow-up (0.5 [0; 1.5] per patient; p < 0.05 vs. patients with AF recurrence). We conclude that substrate-based ablation based on computational modeling could improve outcomes.
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影响因子：
4
作者：
Boyle PM;Hakim JB;Zahid S;Franceschi WH;Murphy MJ;Vigmond EJ;Dubois R;Haïssaguerre M;Hocini M;Jaïs P;Trayanova NA;Cochet H
通讯作者：
Cochet H
影响因子：
64.8
作者：
Gray, RA;Pertsov, AM;Jalife, J
通讯作者：
Jalife, J
影响因子：
6.1
作者：
Boyle, Patrick M.;Zahid, Sohail;Trayanova, Natalia A.
通讯作者：
Trayanova, Natalia A.
影响因子：
2.7
作者：
Lalani, Gautam G.;Coysh, Thomas;Narayan, Sanjiv M.
通讯作者：
Narayan, Sanjiv M.
影响因子：
3.3
作者：
Beg, MF;Helm, PA;Winslow, RL
通讯作者：
Winslow, RL