Surpoint algorithm for improved guidance of ablation for ventricular tachycardia (SURFIRE‐VT): A pilot study

Introduction The utility of ablation index (AI) to guide ventricular tachycardia (VT) ablation in patients with structural heart disease is unknown. The aim of this study was to assess procedural characteristics and clinical outcomes achieved using AI‐guided strategy (target value 550) or conventional non‐AI‐guided parameters in patients undergoing scar‐related VT ablation. Methods Consecutive patients (n = 103) undergoing initial VT ablation at a single center from 2017 to 2022 were evaluated. Patient groups were 1:1 propensity‐matched for baseline characteristics. Single lesion characteristics for all 4707 lesions in the matched cohort (n = 74) were analyzed. The impact of ablation characteristics was assessed by linear regression and clinical outcomes were evaluated by Cox proportional hazard model. Results After propensity‐matching, baseline characteristics were well‐balanced between AI (n = 37) and non‐AI (n = 37) groups. Lesion sets were similar (scar homogenization [41% vs. 27%; p = .34], scar dechanneling [19% vs. 8%; p = .18], core isolation [5% vs. 11%; p = .4], linear and elimination late potentials/local abnormal ventricular activities [35% vs. 44%; p = .48], epicardial mapping/ablation [11% vs. 14%; p = .73]). AI‐guided strategy had 21% lower procedure duration (−47.27 min, 95% confidence interval [CI] [−81.613, −12.928]; p = .008), 49% lower radiofrequency time per lesion (−13.707 s, 95% CI [−17.86, −9.555]; p