How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post‐infarction patients

Advanced Review

Natalia A. Trayanova, Ashish N. Doshi, Adityo Prakosa

Published Online: Jan 09 2020

DOI: 10.1002/wsbm.1477

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Abstract Precision Cardiology is a targeted strategy for cardiovascular disease prevention and treatment that accounts for individual variability. Computational heart modeling is one of the novel approaches that have been developed under the umbrella of Precision Cardiology. Personalized computational modeling of patient hearts has made strides in the development of models that incorporate the individual geometry and structure of the heart as well as other patient‐specific information. Of these developments, one of the potentially most impactful is the research aimed at noninvasively predicting the targets of ablation of lethal arrhythmia, ventricular tachycardia (VT), using patient‐specific models. The approach has been successfully applied to patients with ischemic cardiomyopathy in proof‐of‐concept studies. The goal of this paper is to review the strategies for computational VT ablation guidance in ischemic cardiomyopathy patients, from model developments to the intricacies of the actual clinical application. To provide context in describing the road these computational modeling applications have undertaken, we first review the state of the art in VT ablation in the clinic, emphasizing the benefits that personalized computational prediction of ablation targets could bring to the clinical electrophysiology practice. This article is characterized under: Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Translational, Genomic, and Systems Medicine > Translational Medicine

Application of the VAAT technology in a prospective study for two patients from two different clinical centers (top and bottom rows). A,D, Reconstructed ventricular models with different remodeled regions. B,E, Activation maps corresponding to the two VT morphologies induced in the patient models. White arrows depict the direction of propagation of the excitation wave. The color scale indicates activation times and black indicates tissue regions that did not activate. C,F, Co‐registration of the VAAT‐predicted targets (purple) with the CARTO 3 endocardial surface (green). The red dots correspond to locations of the tip of the catheter during ablation. The left ventricular endocardial surface is shown in green and the total infarct region is shown in gray. Non‐injured and scar tissues are shown in red and yellow, respectively. (Reprinted from Prakosa et al. (). Copyright 2018 Springer Nature.Permission exempt per https://www.nature.com/nature‐research/reprints‐and‐permissions/permissions‐requests)

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Prediction of the VT ablation targets using the VAAT approach in a retrospective study. Representative examples of in silico models and predictions from three patients (A–C). Panels a, Reconstructed ventricular computational models with different structurally remodeled regions. Panels b, Electrical activation maps of the induced VT on the epi‐ or endocardial surfaces. The white arrows show the direction of propagation of the excitation wave. The color scale indicates activation times and black indicates tissue regions that did not activate. Panels c, Predicted ablation sites by VAAT. The purple regions correspond to VAAT‐predicted ablation targets on the endocardial surface, and insets show the zoomed‐in VAAT predictions. Panels d, Co‐registration of VAAT targets with the CARTO 3 endocardial surface (blue) showing clinical ablations corresponding to red dots representing locations of the tip of the catheter during ablation. In C, the patient had ICD. The myocardial wall artifact burden was 59%. Panel a in this case shows, in addition, the LGE‐MRI scans with ICD artifact burden. (Reprinted from Prakosa et al. (). Copyright 2018 Springer Nature. Permission exempt per https://www.nature.com/nature‐research/reprints‐and‐permissions/permissions‐requests)

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Types of reentry in post‐infarction hearts. Top row, schematic diagram of the three types of VTs sustained by propagation through a CCC. Different colors represent different tissue types: non‐injured (red), core scar (yellow), and GZ (green). The white arrows show the predominant direction of reentrant wave propagation for each type of CCC. The black line in the functional reentry schematic represents conduction block. The optimal ablation lesion that terminated each type of reentry is shown schematically in purple. Middle row, examples of VTs in each case shown by the activation map in the LV. Bottom row shows the ablation targets predicted (blue) and the clinical ablation lesions (red). (Reprinted with permission from Deng et al. (). Copyright 2019 Elsevier)

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Distribution of viable tissue in the zone of infarct, shown in short‐axis slices of high‐resolution LGE‐MRI in three infarcted hearts. A, top row, Thin subendocardial layer of surviving tissue (red arrows); B, top row, Intramural viable tissue (yellow arrows); C, top row, Subepicardial layer of surviving myocardium (blue arrows). A, bottom row, Transmembrane voltage maps in one cycle of reentry. White arrows indicate wave propagation direction. The reentry is primarily located in the subendocardial layer. B, bottom row, A breakthrough on the epicardium. Below, delineation of a portion of an intramural surviving tissue embedded inside the infarct (green) that participates in the reentry. The arrows point to the locations of wave entrance and exit from the surviving tissue. C, bottom row, Wave traverses the channel. A border layer around the scar has been rendered semitransparent to visualize channel position. (Reprinted from Pashakhanloo et al. (). Copyright 2018 Wolters Kluwer Health. Permission exempt per copyright transfer agreement.)

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MRI‐based virtual heart reconstruction workflow. A, Contrast‐enhanced cardiac MRI stack (left), with delineation of endocardial and epicardial surfaces (middle), respectively, and the resulting ventricular segmentation (right) into non‐infarcted myocardium, gray zone and scar. B, High‐resolution ventricular structure model (left) with estimated fiber orientation (middle). Action potential traces from the non‐infarcted myocardium (red) and gray zone (green) are in right panel. (Reprinted from Arevalo et al. (). Copyright 2016 Springer Nature. Permission exempt per copyright transfer agreement.)

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