Early prediction of the efficacy of local epicardial radiofrequency ablation for the robotic treatment of persistent atrial fibrillation.

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.