Automatic algorithmic driven monitoring of atrioventricular nodal re-entrant tachycardia ablation to improve procedural safety.

Abstract

During slow pathway modification for atrioventricular nodal reentrant tachycardia, heart block may occur if ablation cannot be stopped in time in response to high risk electrogram features (HREF). To develop an automatic algorithm to monitor HREF and terminate ablation earlier than human reaction. Digital electrogram data from 332 ablation runs from February 2020 to June 2022 were included. They were divided into training and validation sets which contained 126 and 206 ablation runs respectively. HREF in training set was measured. Then a program was developed with cutoff values decided from training set to capture all these HREF. Simulation ablation videos were rendered using validation set electrogram data. The videos were played to three independent electrophysiologists who each determined when to stop ablation. Timing of ablation termination, sensitivity, and specificity were compared between human and program. Reasons for ablation termination in the training set include short AA time, short VV time, AV block and VA block. Cutoffs for the program were set to maximize program sensitivity. Sensitivity and specificity for the program in the validation set were 95.2% and 91.1% respectively, which were comparable to that of human performance at 93.5% and 95.4%. If HREF were recognized by both human and program, ablations were terminated earlier by the program 90.2% of times, by a median of 574 ms (interquartile range 412-807 ms, p < 0.001). Algorithmic-driven monitoring of slow pathway modification can supplement human judgement to improve ablation safety.