In silico pace mapping identifies pacing sites more accurately than inverse body surface potential mapping.

Background: Electrocardiographic imaging (ECGi) is a noninvasive technique for ventricular tachycardia ablation planning. However, it is limited to reconstructing epicardial surface activation. In silico pace mapping combines a personalized computational model with clinical electrocardiograms (ECGs) to generate a virtual 3-dimensional pace map.

Objective: The purpose of this study was to compare the ability of ECGi and in silico pace mapping to determine the site of ventricular pacing.

Methods: ECGi recordings were collected during left ventricular (endocardial: n=5; epicardial: n=1), septal (n=3), and right ventricular (RV) apical (n=15) pacing along with compute tomography. Personalized computed tomography-based ventricular-torso computational models were created and aligned with the 252 ECGi vest electrodes. Ventricles were paced at 1000 random sites, and the corresponding body surface potentials (BSPs) and ECGs were derived. In silico pace maps were then reconstructed by correlating all simulated ECGs or BSPs with the corresponding paced clinical signals. The distance (d) between the pacing electrode (ground truth) and the location with the strongest correlation was determined; for ECGi, the site with the earliest activation time was used.

Results: In silico pace mapping consistently outperformed ECGi in locating the pacing origin, with the best results when all BSPs were used. During left ventricular pacing, the spatial accuracy of in silico pacing mapping was 9.5 mm with BSPs and 12.2 mm when using ECGs as compared with 30.8 mm when using ECGi. During RV pacing, d = 26.1 mm using BSPs, d = 30.9 mm using ECGs, and d = 29.1 mm using ECGi.

Conclusion: In silico pace mapping is more accurate than ECGi in detecting paced activation. Performance was optimal when all BSPs were used and reduced during RV apical pacing.