No abstract
Aims Multiple wavefront pacing (MWP) and decremental pacing (DP) are two electroanatomic mapping (EAM) strategies that have emerged to better characterize the ventricular tachycardia (VT) substrate. The aim of this study was to assess how well MWP, DP, and their combination improve identification of electrophysiological abnormalities on EAM that reflect infarct remodelling and critical VT sites. Methods and results Forty-eight personalized computational heart models were reconstructed using images from post-infarct patients undergoing VT ablation. Paced rhythms were simulated by delivering an initial (S1) and an extra-stimulus (S2) from one of 100 locations throughout each heart model. For each pacing, unipolar signals were computed along the myocardial surface to simulate substrate EAM. Six EAM features were extracted and compared with the infarct remodelling and critical VT sites. Concordance of S1 EAM features between different maps was lower in hearts with smaller amounts of remodelling. Incorporating S1 EAM features from multiple maps greatly improved the detection of remodelling, especially in hearts with less remodelling. Adding S2 EAM features from multiple maps decreased the number of maps required to achieve the same detection accuracy. S1 EAM features from multiple maps poorly identified critical VT sites. However, combining S1 and S2 EAM features from multiple maps paced near VT circuits greatly improved identification of critical VT sites. Conclusion Electroanatomic mapping with MWP is more advantageous for characterization of substrate in hearts with less remodelling. During substrate EAM, MWP and DP should be combined and delivered from locations proximal to a suspected VT circuit to optimize identification of the critical VT site.
Personalized, image‐based computational heart modelling is a powerful technology that can be used to improve patient‐specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state‐of‐the‐art methods still require manual interactions by expert users. The goal of this study is to evaluate the feasibility of an automated, deep learning‐based workflow for reconstructing personalized computational electrophysiological heart models to guide patient‐specific treatment of VT. Contrast‐enhanced computed tomography (CE‐CT) images with expert ventricular myocardium segmentations were acquired from 111 patients across five cohorts from three different institutions. A deep convolutional neural network (CNN) for segmenting left ventricular myocardium from CE‐CT was developed, trained and evaluated. From both CNN‐based and expert segmentations in a subset of patients, personalized electrophysiological heart models were reconstructed and rapid pacing was used to induce VTs. CNN‐based and expert segmentations were more concordant in the middle myocardium than in the heart's base or apex. Wavefront propagation during pacing was similar between CNN‐based and original heart models. Between most sets of heart models, VT inducibility was the same, the number of induced VTs was strongly correlated, and VT circuits co‐localized. Our results demonstrate that personalized computational heart models reconstructed from deep learning‐based segmentations even with a small training set size can predict similar VT inducibility and circuit locations as those from expertly‐derived heart models. Hence, a user‐independent, automated framework for simulating arrhythmias in personalized heart models could feasibly be used in clinical settings to aid VT risk stratification and guide VT ablation therapy. imageKey points Personalized electrophysiological heart modelling can aid in patient‐specific ventricular tachycardia (VT) risk stratification and VT ablation targeting. Current state‐of‐the‐art, image‐based heart models for VT prediction require expert‐dependent, manual interactions that may not be accessible across clinical settings. In this study, we develop an automated, deep learning‐based workflow for reconstructing personalized heart models capable of simulating arrhythmias and compare its predictions with that of expert‐generated heart models. The number and location of VTs was similar between heart models generated from the deep learning‐based workflow and expert‐generated heart models. These results demonstrate the feasibility of using an automated computational heart modelling workflow to aid in VT therapeutics and has implications for generalizing personalized computational heart technology to a broad range of clinical centres.
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