Abstract 11921: Risk Stratification for Ventricular Arrhythmia in Patients With Repaired Tetralogy of Fallot (TOF) via Image-Based Computational Simulations: A Pilot Study

Abstract

Introduction: Patients with repaired TOF have increased arrhythmia risk due to myocardial fibrosis, scar and right ventricular dilation. Current risk stratification lacks consistent predictive value and clinical practicality. We surmised that personalized models developed from cardiac late gadolinium enhancement MRI (LGE-MRI) could correctly assess risk of arrhythmia in patients with repaired TOF using our previously-validated Virtual Arrhythmia Risk Prediction (VARP) protocol. Methods: As a proof-of-concept retrospective study, we developed personalized 3D computational models of TOF hearts from LGE-MRI scans. Three patients with clinically disparate arrhythmia status were included: patient 1 (P1) had clinical episodes of sustained ventricular tachycardia (VT) and inducibility in electrophysiologic study (EPS); patient 2 (P2) had negative EPS despite documented runs of non-sustained VT; patient 3 (P3) had no clinical evidence of VT. Then, for each heart, we applied our VARP protocol to generate a computational model, including personalized representations of scar and fibrosis. We then assessed the inducibility of reentrant arrhythmia from 26 pacing sites. Results: For all three cases, VARP outcomes correlated with clinical observations. In the P1 model sustained VT was induced with rapid ventricular pacing, while only non-sustained VT was induced in the P2 model (Fig 1). No arrhythmia was induced in the P3 model. Reentrant wavefronts in P1 and P2 occurred near the right ventricular outflow tract (RVOT) or ventricular septal defect patch, common sites of reentry observed in EPS in patients with TOF (Fig 1). Conclusion: Application of the VARP approach in repaired TOF patients is feasible and has the potential to correctly identify patients with a high risk of developing arrhythmia. With additional validation this non-invasive technique could serve as an effective tool for longitudinal risk assessment and help guide clinical decision making.