Modeling the optimal ablation site and lesion characteristics in AVNRT using the cloud-based CARTONET platform

Catheter ablation of atrioventricular nodal reentrant tachycardia (AVNRT) is a routine and highly effective therapy. Yet the optimal site and biophysical parameters for radiofrequency (RF) delivery remain poorly standardized. The emergence of cloud-based mapping software enables multicase geometric and biophysical modeling of ablation data to identify reproducible targets.

To characterize the geometric distribution and lesion parameters of successful versus non-successful ablation sites in AVNRT, and to develop a predictive model using aggregated CARTONET data.

We prospectively analyzed 94 consecutive AVNRT ablation procedures recorded in CARTONET over a 10-year period. Mapping points tagged as "His" defined a patient-specific reference centroid. Ablation points labeled slow junctional rhythm (SJR), fast junctional rhythm (FJR), or AV block were compared for spatial position relative to the His cloud and for biophysical metrics (impedance, power, temperature, duration, stability). Machine-learning modeling (XGBoost classifier) was applied to predict ablation outcomes from geometric and biophysical inputs.

A total of 310 ablation points were analyzed. Mean distance from the His centroid was 18.3±5.9 mm for SJR sites, 17.9±6.1 mm for FJR, and 15.9±4.8 mm for AV block. Successful lesions (SJR) exhibited higher mean power (40 W), greater impedance drop (~11Ω), and longer duration (28 s) compared with non-successful sites. The machine-learning model achieved an accuracy of 82.3% (95% CI 72.6–90.3) in classifying lesion outcome.

CARTONET-based spatial normalization allows reproducible modeling of the ideal slow-pathway ablation region and identifies biophysical features associated with effective lesion formation. This approach supports the development of data-driven tools to guide ablation.