We recently developed two non-invasive methodologies to help guide VT ablation: population-derived automated VT exit localization (PAVEL) and virtual-heart arrhythmia ablation targeting (VAAT). We hypothesized that while very different in their nature, limitations, and type of ablation targets (substrate-based vs. clinical VT), the image-based VAAT and the ECG-based PAVEL technologies would be spatially concordant in their predictions. This project is to test this hypothesis in ischemic cardiomyopathy patients in a retrospective feasibility study.

Prior site of origin systems to identify idiopathic ventricular arrhythmias (IVA) are limited by the need to create complete electroanatomic maps (EAM), inability to localize intracardiac structures/vessels and require pre-procedural cardiac imaging. Our Automatic Arrhythmia Origin Localization (AAOL) system addresses these issues. The AAOL system combines 3-lead, 120-ms QRS integrals with pace mapping to predict the site of earliest ventricular activation and project that site onto patient-specific EAM geometry. In a prospective, multicenter study of patients undergoing IVA catheter ablation, twenty-three IVA origin sites were localized by the AAOL system with a mean localization accuracy of 3.6 mm, better than any prior published system.