Key result
Machine learning model predicts epicardial ablation efficacy within 5 seconds with ~95% accuracy.
Why the study?
Early prediction of the efficacy of local epicardial radiofrequency ablation is crucial for optimizing the robotic treatment of persistent atrial fibrillation.
Does a machine learning model utilizing the first 5 seconds of impedance data improve the prediction of local epicardial radiofrequency ablation efficacy compared to zero-time impedance alone in patients with persistent atrial fibrillation?
Observational (n=92)
No
Does a machine learning model utilizing the first 5 seconds of impedance data improve the prediction of local epicardial radiofrequency ablation efficacy compared to zero-time impedance alone in patients with persistent atrial fibrillation?
A machine learning model analyzing the first 5 seconds of impedance data can highly accurately predict epicardial ablation efficacy, potentially reducing unnecessary tissue damage by enabling early termination of ineffective energy deliveries.
Answers are grounded in this paper and cite their sources.
May enable early termination of ineffective epicardial ablations to limit tissue injury; leaves open need for prospective validation before practice change.
Background: Early prediction of the efficacy of local epicardial radiofrequency ablation (LERFA) is crucial for optimizing the robotic treatment of persistent atrial fibrillation. Objective: This study aimed to develop a machine learning model that accurately predicts LERFA efficacy within the first 5 seconds of the procedure, to stop ineffective procedures and reduce unnecessary cardiac tissue damage. Methods: Impedance data from 92 patients who underwent robotic LERFA were analyzed, with a total of 2486 LERFAs included in the final dataset. LERFA efficacy predictors, including zero-time impedance value, slope, and harmonic components, were extracted from the first 5 seconds of each time-impedance curve. Several supervised machine learning approaches were then tested to predict LERFA efficacy. Results: Random Forest demonstrated the highest performance, achieving 94.5% accuracy, 88.3% sensibility, and 97.2% specificity. This Random Forest model significantly outperformed the benchmark approach based on the zero-time impedance value alone, which achieved an accuracy of only 55.6% and a specificity of only 37.7%. Conclusion: The developed model enables fast and accurate prediction of LERFA efficacy, potentially reducing the number of completed LERFAs by 56.8%. This reduction results in minimal damage to cardiac tissue, a lower risk of complications, a reduction in operating time, and greater precision and safety in the ablation process.
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Celentano et al. (2025) conducted an observational in Persistent or long-standing persistent atrial fibrillation (n=92). Random Forest machine learning model (using 5-second impedance data) vs. Zero-time impedance value alone (>120 Ω threshold) was evaluated on Prediction accuracy of local epicardial radiofrequency ablation efficacy. A Random Forest machine learning model predicted the efficacy of local epicardial radiofrequency ablation within 5 seconds with 94.5% accuracy, significantly outperforming the zero-time impedance benchmark.
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