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

Area of Science:

• Cardiology
• Electrophysiology
• Medical Devices

Background:

• The efficacy of ablation index (AI) in guiding ventricular tachycardia (VT) ablation for patients with structural heart disease remains unclear.
• This study aimed to compare AI-guided (target 550) versus conventional methods for scar-related VT ablation.

Purpose of the Study:

• To assess procedural characteristics and clinical outcomes of AI-guided VT ablation compared to non-AI-guided approaches.
• To evaluate the impact of AI on procedure duration, radiofrequency time, and patient outcomes in scar-related VT ablation.

Main Methods:

• A propensity-matched analysis of 74 patients (37 AI-guided, 37 non-AI-guided) undergoing VT ablation from 2017-2022.
• Analysis of 4707 lesions to assess lesion characteristics and procedural parameters.
• Linear regression and Cox proportional hazard models were used to evaluate ablation characteristics and clinical outcomes.

Main Results:

• AI-guided ablation resulted in significantly shorter procedure duration (21% reduction) and radiofrequency time per lesion (49% reduction).
• Fluid administration was also lower in the AI group (21% reduction).
• Both groups showed similar acute procedural success rates and VT recurrence, with lesion analysis indicating impedance drop plateaus at AI 550-600.

Conclusions:

• AI-guided ablation for scar-related VT is associated with improved procedural efficiency, including shorter procedure and radiofrequency times.
• The AI strategy demonstrated comparable acute procedural and intermediate-term clinical outcomes to conventional methods.
• This pilot study suggests AI is a viable option for optimizing VT ablation in structural heart disease.