Atrial digital twins reproducing clinical biomarkers of intracardiac electrograms

Atrial fibrillation (AF) poses significant challenges for effective treatment, particularly in persistent AF cases (psAF). Current pharmacological and ablative therapies remain suboptimal due to the complex nature of arrhythmogenic substrates. In this context, patient-specific simulations have emerged as a promising tool to improve therapy planning and personalization. In this study, we developed atrial digital twins calibrated to reproduce clinical activation patterns and evaluated their ability to reproduce clinical trends in biomarkers derived from intracardiac electrograms (EGMs). We calibrated patient-specific models for 20 patients with psAF using anatomical meshes derived from computed tomography scans (CT), including fiber direction, atlas-derived tissue heterogeneity, and fibrotic regions estimated from clinical bipolar EGM voltage maps. Electrophysiological properties were personalized through a multi-step calibration procedure including global and local diffusion adjustment and ionic channel remodeling optimization. The calibration workflow significantly reduced discrepancies between simulated and clinical local activation time (LAT) maps obtained after the third extrastimulus of the triple short-coupled extrastimuli pacing protocol (3-Extra), decreasing the mean absolute error (MAE) from 36.3 ± 11.1 ms at baseline to 14.3 ± 3.8 ms after complete calibration (p < 0.01). Total depolarization time (TDT) errors were reduced from 41.3 ± 21.3 ms to 4.9 ± 5.8 ms, indicating substantial correction of baseline conduction discrepancies. Biomarkers derived from simulated EGMs reproduced the clinical trends in activation duration, EGM fractionation and LAT variability between clinically annotated healthy and abnormal atrial tissue (p < 0.01). However, the magnitude of these differences was smaller in the simulations and voltage-related behavior was not accurately reproduced. These results demonstrate the feasibility of generating personalized atrial digital twins that reproduce patient-specific activation patterns and partially capture activation-based EGM biomarker trends. The calibration of these models supports their potential use as simulation tools to investigate atrial conduction abnormalities and potentially guide future therapy-planning strategies, while highlighting the need for improved modeling of voltage-related mechanisms before simulated EGMs can fully reproduce clinical signal amplitudes.