JTCVS open · 2026
Artificial-intelligence electrocardiography and computed tomography mapping to guide surgical ablation of ventricular arrhythmias.
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Sung Kevin, Pretorius Gert Victor, Coletta Joelle M, Kearns Mark J, Glenn Ian, Feld Gregory K, Madani Michael M, Krummen David E, Ho Gordon
Аннотация
OBJECTIVE: Surgical arrhythmia ablation is challenging as a result of arrhythmia induction and mapping limitations in the operating room. We hypothesized that a novel preoperative workflow involving artificial intelligence (AI) electrocardiographic (ECG) and computed tomography (CT) analysis to localize arrhythmia sources and identifying myocardial scar would facilitate surgical arrhythmia ablation.
METHODS: We conducted a feasibility study at 2 institutions under institutional review board-approved protocols. ECG recordings of target arrhythmias were analyzed to localize ventricular tachycardia (VT) and premature ventricular complex (PVC) sources. Concurrently, cardiac CT studies underwent AI analysis to identify myocardial scar. The results were combined into a 3-dimensional model for surgical ablation planning. The primary end point was the composite of death from any cause, VT storm, sustained VT, appropriate implantable cardioverter-defibrillator shock, and (for patients who underwent PVC ablation) PVC burden reduction less than 50% at 1-year follow-up.
RESULTS: Of the 13 patients enrolled (9 PVC ablation and 4 VT ablation procedures), concomitant surgeries included mitral valve repair (4 patients, 31%), left ventricular assist device implantation (n, 3, 23%), and bypass surgery (n, 2, 15%). At a median of 1-year follow-up, 11 of 13 (84.6%) patients were free from the primary end point. PVC burden decreased from an average of 22.1 ± 15.9% to 2.6 ± 2.30% (P = .008). In patients with VT, implantable cardioverter-defibrillator shocks decreased from 9.2 ± 5.2 per month to 0 ± 0 per month (P = .012). Complications were not increased compared with a weighted average from prior published studies.
CONCLUSIONS: AI-based cardiac CT analysis and ECG arrhythmia mapping are feasible and facilitate surgical arrhythmia ablation, yielding significant reductions in arrhythmia burden compared with baseline. Additional studies are required to validate these findings in larger populations.
METHODS: We conducted a feasibility study at 2 institutions under institutional review board-approved protocols. ECG recordings of target arrhythmias were analyzed to localize ventricular tachycardia (VT) and premature ventricular complex (PVC) sources. Concurrently, cardiac CT studies underwent AI analysis to identify myocardial scar. The results were combined into a 3-dimensional model for surgical ablation planning. The primary end point was the composite of death from any cause, VT storm, sustained VT, appropriate implantable cardioverter-defibrillator shock, and (for patients who underwent PVC ablation) PVC burden reduction less than 50% at 1-year follow-up.
RESULTS: Of the 13 patients enrolled (9 PVC ablation and 4 VT ablation procedures), concomitant surgeries included mitral valve repair (4 patients, 31%), left ventricular assist device implantation (n, 3, 23%), and bypass surgery (n, 2, 15%). At a median of 1-year follow-up, 11 of 13 (84.6%) patients were free from the primary end point. PVC burden decreased from an average of 22.1 ± 15.9% to 2.6 ± 2.30% (P = .008). In patients with VT, implantable cardioverter-defibrillator shocks decreased from 9.2 ± 5.2 per month to 0 ± 0 per month (P = .012). Complications were not increased compared with a weighted average from prior published studies.
CONCLUSIONS: AI-based cardiac CT analysis and ECG arrhythmia mapping are feasible and facilitate surgical arrhythmia ablation, yielding significant reductions in arrhythmia burden compared with baseline. Additional studies are required to validate these findings in larger populations.