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

ABSTRACT:

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

SUBMITTER: Salvi D 

PROVIDER: S-EPMC12902279 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature