LGE-MRI for diagnosis of left atrial cardiomyopathy as identified in high-definition endocardial voltage and conduction velocity mapping
LGE-MRI for diagnosis of left atrial cardiomyopathy as identified in high-definition endocardial voltage and conduction velocity mapping
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LGE-MRI 用于诊断左心房心肌病（通过高清心内膜电压和传导速度绘图确定）
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发表时间：
2022
期刊：
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通讯作者：
Amir S. Jadidi
中科院分区：
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作者：
D. Nairn;M. Eichenlaub;B. Müller;H. Lehrmann;C. Nagel;L. Azzolin;G. Luongo;Rosa M Figueras Ventura;Barbara Rubio Forcada;A. Colomer;T. Arentz;O. Dössel;A. Loewe;Amir S. Jadidi
背景：电解剖电压、传导速度(CV)标测和晚期Gd增强磁共振成像(LGE-MRI)是房性心肌病(ACM)的不同诊断方法。然而，在检测到的ACM的位置和程度上仍然存在不一致。目的：(1)比较不同手术方式的ACM范围和部位。(2)开发新的估计最佳图像强度阈值(EOIIT)，用于LGE-MRI诊断ACM患者。方法：对36例未消融的持续性房颤患者行LGE-MRI和高清晰度窦性心律电生理标测。显著的房性心动过速定义为0.5 mV时左心房(LA)表面的[≥]5%的低电压底物(LVS)。采用Utah法、图像强度比(IIR>；1.20)和新的EOIIT方法对LGE区进行分类，并与LVS和慢传导区Lt；0.2m/S进行比较，ROC分析确定了诊断ACM的最大程度。结果：不同标测方法检出病变底物的程度和分布有显著差异(p<；0.001)：3%(IQR0-12%)的左房显示LVS<；0.5mV与14%(3-25%)的慢传导区；0.2m/S与16%(6-32%)LGE与17%(11-24%)的IIR>；OIIT与患者平均血池强度呈线性相关(R2=0.89，p<；0.001)。与犹他州方法(60%灵敏度，75%特异度，AUC：0.76)和IIR>；1.20(58%灵敏度，75%特异度，AUC：0.71)相比，新的EOIIT方法(83%灵敏度，88%特异度，AUC：0.94)改善了基于LGEMRI的ACM诊断。结论：无论使用何种LGE检测方案，LA-LVS、CV和LGE-MRI在病变底物的分布上存在重要的不一致。然而，新的EOIIT方法改善了基于LGE-MRI的ACM诊断，在消融初期的房颤患者中。
Background: Electro-anatomical voltage, conduction velocity (CV) mapping and late gadolinium enhancement magnetic resonance imaging (LGE-MRI) are different diagnostic modalities for atrial cardiomyopathy (ACM). However, discordances remain in the location and extent of detected ACM. Objectives: (1) Comparison of ACM extent and location between current modalities. (2) Development of new estimated optimised image intensity thresholds (EOIIT) for LGE-MRI identifying patients with ACM. Methods: Thirty-six ablation-naive persistent AF patients underwent LGE-MRI and high-definition electro-anatomical mapping in sinus rhythm. Significant ACM was defined as low voltage substrate (LVS) extent [≥] 5% of the left atrium (LA) surface at < 0.5mV. LGE areas were classified using the Utah, image intensity ratio (IIR > 1.20) and new EOIIT method for comparison to LVS and slow conduction areas < 0.2m/s. ROC analysis determined the LGE-extent enabling accurate diagnosis of ACM. Results: The degree and distribution of detected pathological substrate varied significantly (p < 0.001) across the mapping modalities: 3% (IQR 0-12%) of the LA displayed LVS < 0.5mV vs. 14% (3-25%) slow conduction areas < 0.2m/s vs. 16% (6-32%) LGE with Utah method vs. 17%(11-24%) using IIR > 1.20, with enhanced discrepancies on posterior LA. A linear correlation was found between the OIIT and each patients mean blood pool intensity (R2=0.89, p < 0.001). LGEMRI-based ACM diagnosis improved with the novel EOIIT (83% sensitivity, 88% specificity, AUC:0.94) in comparison to the Utah method (60% sensitivity, 75% specificity, AUC:0.76), and IIR > 1.20 (58% sensitivity, 75% specificity, AUC:0.71). Conclusion: Important discordances in distribution of pathological substrate exist between LA-LVS, CV and LGE-MRI, irrespective of the LGE-detection protocol that is used. However, the new EOIIT method improves LGE-MRI based ACM diagnosis in ablation-naive AF-patients.