Waight Michael C, Prakosa Adityo, Li Anthony C, Bunce Nick, Marciniak Anna, Trayanova Natalia A, Saba Magdi M
City St. George's, University of London, UK (M.C.W., A.C.L., M.M.S.).
Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD (M.C.W., A.P., N.A.T.).
Circulation. 2025 Feb 25;151(8):521-533. doi: 10.1161/CIRCULATIONAHA.124.070526. Epub 2025 Jan 6.
Current outcomes from catheter ablation for scar-dependent ventricular tachycardia (VT) are limited by high recurrence rates and long procedure durations. Personalized heart digital twin technology presents a noninvasive method of predicting critical substrate in VT, and its integration into clinical VT ablation offers a promising solution. The accuracy of the predictions of digital twins to detect invasive substrate abnormalities is unknown. We present the first prospective analysis of digital twin technology in predicting critical substrate abnormalities in VT.
Heart digital twin models were created from 18 patients with scar-dependent VT undergoing catheter ablation. Contrast-enhanced cardiac magnetic resonance images were used to reconstruct finite-element meshes, onto which regional electrophysiological properties were applied. Rapid-pacing protocols were used to induce VTs and to define the VT circuits. Predicted optimum ablation sites to terminate all VTs in the models were identified. Invasive substrate mapping was performed, and the digital twins were merged with the electroanatomical map. Electrogram abnormalities and regions of conduction slowing were compared between digital twin-predicted sites and nonpredicted areas.
Electrogram abnormalities were significantly more frequent in digital twin-predicted sites compared with nonpredicted sites (468/1029 [45.5%] versus 519/1611 [32.2%]; <0.001). Electrogram duration was longer at predicted sites compared with nonpredicted sites (82.0±25.9 milliseconds versus 69.7±22.3 milliseconds; <0.001). Digital twins correctly identified 21 of 26 (80.8%) deceleration zones seen on isochronal late activation mapping.
Digital twin-predicted sites display a higher prevalence of abnormal and prolonged electrograms compared with nonpredicted sites and accurately identify regions of conduction slowing. Digital twin technology may help improve substrate-based VT ablation.
URL: https://www.clinicaltrials.gov; Unique identifier: NCT04632394.
目前，导管消融治疗瘢痕相关性室性心动过速（VT）的疗效受到高复发率和长手术时间的限制。个性化心脏数字孪生技术提供了一种预测VT关键基质的非侵入性方法，将其整合到临床VT消融中提供了一个有前景的解决方案。数字孪生预测检测侵入性基质异常的准确性尚不清楚。我们首次对数字孪生技术预测VT关键基质异常进行前瞻性分析。
从18例接受导管消融的瘢痕相关性VT患者创建心脏数字孪生模型。使用对比增强心脏磁共振图像重建有限元网格，并在其上应用区域电生理特性。采用快速起搏方案诱发VT并定义VT环路。确定模型中预测的终止所有VT的最佳消融部位。进行侵入性基质标测，并将数字孪生与电解剖图合并。比较数字孪生预测部位和未预测区域之间的电图异常和传导减慢区域。
与未预测部位相比，数字孪生预测部位的电图异常明显更频繁（468/1029 [45.5%] 对519/1611 [32.2%]；<0.001）。与未预测部位相比，预测部位的电图持续时间更长（82.0±25.9毫秒对69.7±22.3毫秒；<0.001）。数字孪生正确识别了等时晚期激活标测中所见的26个减速区中的21个（80.8%）。
与未预测部位相比，数字孪生预测部位显示出更高的异常和延长电图患病率，并准确识别传导减慢区域。数字孪生技术可能有助于改善基于基质的VT消融。
