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Abstract：
Electro-anatomical voltage, conduction velocity (CV) mapping, and late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) have been correlated with atrial cardiomyopathy (ACM). However, the comparability between these modalities remains unclear. This study aims to (i) compare pathological substrate extent and location between current modalities, (ii) establish spatial histograms in a cohort, (iii) develop a new estimated optimized image intensity threshold (EOIIT) for LGE-MRI identifying patients with ACM, (iv) predict rhythm outcome after pulmonary vein isolation (PVI) for persistent atrial fibrillation (AF).
Thirty-six ablation-naive persistent AF patients underwent LGE-MRI and high-definition electro-anatomical mapping in sinus rhythm. Late gadolinium enhancement areas were classified using the UTAH, image intensity ratio (IIR >1.20), and new EOIIT method for comparison to low-voltage substrate (LVS) and slow conduction areas <0.2 m/s. Receiver operating characteristic analysis was used to determine LGE thresholds optimally matching LVS. Atrial cardiomyopathy was defined as LVS extent ≥5% of the left atrium (LA) surface at <0.5 mV. The degree and distribution of detected pathological substrate (percentage of individual LA surface are) varied significantly (P < 0.001) across the mapping modalities: 10% (interquartile range 0-14%) of the LA displayed LVS <0.5 mV vs. 7% (0-12%) slow conduction areas <0.2 m/s vs. 15% (8-23%) LGE with the UTAH method vs. 13% (2-23%) using IIR >1.20, with most discrepancies on the posterior LA. Optimized image intensity thresholds and each patient\'s mean blood pool intensity correlated linearly (R2 = 0.89, P < 0.001). Concordance between LGE-MRI-based and LVS-based ACM diagnosis improved with the novel EOIIT applied at the anterior LA [83% sensitivity, 79% specificity, area under the curve (AUC): 0.89] in comparison to the UTAH method (67% sensitivity, 75% specificity, AUC: 0.81) and IIR >1.20 (75% sensitivity, 62% specificity, AUC: 0.67).
Discordances in detected pathological substrate exist between LVS, CV, and LGE-MRI in the LA, irrespective of the LGE detection method. The new EOIIT method improves concordance of LGE-MRI-based ACM diagnosis with LVS in ablation-naive AF patients but discrepancy remains particularly on the posterior wall. All methods may enable the prediction of rhythm outcomes after PVI in patients with persistent AF.
Thirty-six ablation-naive persistent AF patients underwent LGE-MRI and high-definition electro-anatomical mapping in sinus rhythm. Late gadolinium enhancement areas were classified using the UTAH, image intensity ratio (IIR >1.20), and new EOIIT method for comparison to low-voltage substrate (LVS) and slow conduction areas <0.2 m/s. Receiver operating characteristic analysis was used to determine LGE thresholds optimally matching LVS. Atrial cardiomyopathy was defined as LVS extent ≥5% of the left atrium (LA) surface at <0.5 mV. The degree and distribution of detected pathological substrate (percentage of individual LA surface are) varied significantly (P < 0.001) across the mapping modalities: 10% (interquartile range 0-14%) of the LA displayed LVS <0.5 mV vs. 7% (0-12%) slow conduction areas <0.2 m/s vs. 15% (8-23%) LGE with the UTAH method vs. 13% (2-23%) using IIR >1.20, with most discrepancies on the posterior LA. Optimized image intensity thresholds and each patient\'s mean blood pool intensity correlated linearly (R2 = 0.89, P < 0.001). Concordance between LGE-MRI-based and LVS-based ACM diagnosis improved with the novel EOIIT applied at the anterior LA [83% sensitivity, 79% specificity, area under the curve (AUC): 0.89] in comparison to the UTAH method (67% sensitivity, 75% specificity, AUC: 0.81) and IIR >1.20 (75% sensitivity, 62% specificity, AUC: 0.67).
Discordances in detected pathological substrate exist between LVS, CV, and LGE-MRI in the LA, irrespective of the LGE detection method. The new EOIIT method improves concordance of LGE-MRI-based ACM diagnosis with LVS in ablation-naive AF patients but discrepancy remains particularly on the posterior wall. All methods may enable the prediction of rhythm outcomes after PVI in patients with persistent AF.
摘要：
目的：电解剖电压，传导速度（CV）标测和晚期钆增强磁共振成像（LGEMRI）与心房心肌病（ACM）相关。然而,这些模式之间的可比性尚不清楚.目的：（1）比较当前模式之间的病理底物范围和位置。(2)在队列中建立空间直方图。（3）为LGE-MRI识别ACM患者开发新的估计优化图像强度阈值（EOIIT）。(4)预测持续性心房颤动肺静脉隔离术(PVI)后的心律转归。
方法：36例未接受消融治疗的持续性房颤患者在SR中接受了LGE-MRI和高清晰度电解剖标测。LGE地区使用UTAH分类，图像强度比（IIR>1.20）和新的EOIIT方法，用于与LVS和<0.2m/s的慢传导区域进行比较。使用ROC分析来确定最佳匹配LVS的LGE阈值。ACM定义为在<0.5mV时低电压底物(LVS)程度≥左心房(LA)表面的5%。
结果：在标测模式下，检测到的病理底物的程度和分布显着变化（p<0.001）：LA显示的LVS的3％（IQR0-12％）<0.5mVvs.14%（3-25%）慢传导面积<0.2m/svs.16％（6-32％）使用UTAH方法的LGE与17％（11-24％）使用IIR>1.20，与后部LA差异最大。优化的图像强度阈值与每位患者的平均血池强度呈线性关系（R2=0.89，p<0.001）。基于LGE-MRI和基于LVS的ACM诊断之间的一致性随着新的EOIIT应用于前部LA而得到改善(83%灵敏度,79%的特异性，AUC：0.89）与UTAH方法（67%灵敏度，75%特异性，AUC：0.81）和IIR>1.20（75%灵敏度，62%的特异性，AUC：0.67）。
结论：LVS之间存在病理底物检测的不一致，洛杉矶的CV和LGE-MRI，与LGE检测方法无关。新的EOIIT方法改善了基于LGE-MRI的ACM诊断与LVS的一致性，但差异仍然存在，尤其是在后壁。所有方法都可以预测持续性房颤患者PVI后的节律结果。
方法：36例未接受消融治疗的持续性房颤患者在SR中接受了LGE-MRI和高清晰度电解剖标测。LGE地区使用UTAH分类，图像强度比（IIR>1.20）和新的EOIIT方法，用于与LVS和<0.2m/s的慢传导区域进行比较。使用ROC分析来确定最佳匹配LVS的LGE阈值。ACM定义为在<0.5mV时低电压底物(LVS)程度≥左心房(LA)表面的5%。
结果：在标测模式下，检测到的病理底物的程度和分布显着变化（p<0.001）：LA显示的LVS的3％（IQR0-12％）<0.5mVvs.14%（3-25%）慢传导面积<0.2m/svs.16％（6-32％）使用UTAH方法的LGE与17％（11-24％）使用IIR>1.20，与后部LA差异最大。优化的图像强度阈值与每位患者的平均血池强度呈线性关系（R2=0.89，p<0.001）。基于LGE-MRI和基于LVS的ACM诊断之间的一致性随着新的EOIIT应用于前部LA而得到改善(83%灵敏度,79%的特异性，AUC：0.89）与UTAH方法（67%灵敏度，75%特异性，AUC：0.81）和IIR>1.20（75%灵敏度，62%的特异性，AUC：0.67）。
结论：LVS之间存在病理底物检测的不一致，洛杉矶的CV和LGE-MRI，与LGE检测方法无关。新的EOIIT方法改善了基于LGE-MRI的ACM诊断与LVS的一致性，但差异仍然存在，尤其是在后壁。所有方法都可以预测持续性房颤患者PVI后的节律结果。
