In silico assessment of arrhythmic risk following the implantation of engineered heart tissues in porcine hearts with varying infarct locations.

Engineered heart tissues (EHTs) have shown promise in partially restoring ejection fraction after myocardial infarction (MI); however, their potential to introduce electrophysiological heterogeneities and promote arrhythmias remains underexplored. This study assessed the arrhythmogenic risk following immature EHT engraftment in infarcted ventricles using computational simulations that replicate preclinical protocols. EHT computational models were developed and integrated into nine validated porcine-specific biventricular models from pigs with left circumflex (LCx, n = 4) or left anterior descending (LAD, n = 5) MIs. Ventricular tachycardia (VT) susceptibility was evaluated using an S1-S2 stimulation protocol across varying pacing sites and coupling intervals, accounting for infarct characteristics, implantation site, conductivity, and the ventricular conduction system (CS). VT burden was quantified with a 0-1 inducibility score (IS). In silico reentrant activity qualitatively reproduced the arrhythmic patterns observed experimentally in porcine MI models. VT vulnerability was greater in LAD than in LCx infarcts, consistent with a larger infarct size. Inclusion of the CS modified VT burden by providing conduction shortcuts that either facilitated or suppressed reentry. Remuscularization directly on the MI region (IS = 0.49) heightened VT inducibility in dense, transmural scars (IS = 0.16), whereas lateral EHT implantation (IS = 0.35) reduced this risk with respect to direct implantation. In non-transmural scars, VT inducibility varied with the implantation site. Matching EHT conductivity to host myocardium lowered or contained arrhythmogenicity (LCx-IS: from 0.5 to 0.25; LAD-IS: stable at 0.57). These results highlight the latent arrhythmic risk of EHT-mediated remuscularization after MI, identifying infarct substrate, EHT conductivity, and implantation site as critical determinants, and emphasize the importance of incorporating the CS for accurate risk assessment.