CHARACTERIZATION OF THE RIGHT VENTRICULAR SUBSTRATE PARTICIPATING IN POST INFARCTION VENTRICULAR TACHYCARDIA

Echocardiographic Predictors of Ventricular Arrhythmias in Patients With Left Ventricular Assist Devices and Implantable Cardioverter-Defibrillator

AIM

To evaluate the predictive value of preoperative echocardiographic parameters for occurrence of VAs in patients with preexisting ICD undergoing LVAD implantation.

METHODS AND RESULTS

All consecutive patients (n = 264) with previous ICD who underwent LVAD surgery between May 2011 and December 2019 at our institution were included. The patients were predominantly male (89%) with NICM (59%) and a mean age of 59 ± 10 years. All LVADs were continuous flow device (154 HVAD, 21 HeartMate II, and 89 HeartMate 3). A total of 102 (39%) patients had VAs in the first year after LVAD implantation. We compared echocardiographic parameters in patients with and without VAs before LVAD, at 1 month and 1 year after LVAD implantation. Increased pre-LVEDD ≥ 72 mm predicted the occurrence of VAs after LVAD implantation for ICM patients (HR: 2.9, 95% confidence interval (CI): [1.3-6.6], p = 0.012), while a larger pre-RVEDD ≥ 46 mm was predictive in NICM patients (HR: 2.8, (CI): [1.4-5.9], p = 0.004). Moreover, a larger RVEDD at 1 year after LVAD was highly associated with VAs in the first year after LVAD implantation (50 ± 10 vs. 45 ± 8 mm, p = 0.001). All patients demonstrated a significant decrease in LVEDD as well as a reduction in severity of mitral and tricuspid regurgitation during 1 year after LVAD implantation, reflecting left ventricular unloading through the LVAD.

CONCLUSIONS

Larger left and right ventricular diameters before LVAD predict the occurrence of VAs after LVAD implantation in ICM and NICM patients. Persistent RV remodeling post-LVAD is also associated with VAs.

Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias

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. KEY 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.

Importance of Right Ventricular Mapping and Ablation for Ventricular Tachycardia in Postinfarction Patients

BACKGROUND

The characteristics of patients with post-myocardial infarction (PMI) ventricular tachycardia (VT) who require right ventricular (RV) ablation are underreported.

OBJECTIVES

The aims of this study were to examine the characteristics and outcomes of patients undergoing PMI VT ablation who have target sites in the right ventricle and to compare patient and VT characteristics between patients with free wall vs septal RV target sites.

METHODS

Consecutive patients undergoing ablation for PMI VT with target sites located within the right ventricle were included. Patients were stratified on the basis of the presence of free wall vs septal RV target sites.

RESULTS

Among 277 consecutive patient undergoing PMI VT ablation, 30 (11%) had RV target sites (mean age 68.71 ± 9.5 years, 29 men [97%], mean left ventricular ejection fraction [LVEF] 28.7% ± 16.7%). Twenty patients had only septal VTs, and 10 patients had only free wall VTs. Fifty-seven VTs with RV targets (1.9 ± 1.4 per patient, mean cycle length 338 ± 90 ms, 53 left bundle branch, 36 superior axis) were induced. Patients with RV free wall VTs had greater rates of RV dysfunction (80% vs 30%; P = 0.023) but had greater LVEFs (38.3% ± 21.06% vs 23.9% ± 11.93%; P = 0.02). Over a mean follow-up period of 3.4 ± 3.2 years, patients with RV septal target sites had worse survival free of VT, transplantation, or left ventricular assist device placement after ablation (log-rank P < 0.05).

CONCLUSIONS

The arrhythmogenic substrate in PMI patients often involves the right ventricle, including the septum and free wall. The presence of RV dysfunction and greater LVEF were associated with the presence of RV free wall target sites. Patients with only RV septal target sites had worse postablation outcomes.