Accessory Pathway Localisation Optimisation In Silico Heart Model

Abstract

[ES] El síndrome de Wolff-Parkinson-White (WPW) representa la anomalía más común de las vías accesorias. Durante la cirugía de ablación, los médicos suelen utilizar catéteres de mapeo como el AdvisorTM HD Grid de Abbott, que proporciona señales de electrograma (EGM) para intentar localizar la vía accesoria y realizar la ablación en ese punto. Los indicadores actuales carecen de precisión, lo que a menudo provoca una ablación excesiva de tejido. Este trabajo tiene como objetivo mejorar la detección de vías accesorias mediante el desarrollo de modelos de catéter, la recuperación de señales del modelo cardíaco, la extracción de parámetros de estas señales y la comparación de los resultados con los extraídos de casos reales. Todo esto para permitir mayor precisión en futuras ablaciones.

[EN] Wolff-Parkinson-White (WPW) syndrome is a cardiovascular disease characterized by abnormal atrio-ventricular conduction facilitated by accessory pathways (APs). The primary treatment modality is invasive catheter ablation of the AP. Successful ablation outcomes depend on precise localization of the APs; however, current catheter diagnostic procedures often struggle with accurately determining AP locations. In order to gain insight into how a high density mapping catheter can be used to locate an AP, a virtual cardiac model is employed. A cardiac model of electrophysiology that was specifically tailored to represent ante- grade APs in the form of a short atrio-ventricular bypass tract was utilised. An HD- Grid catheter model was laid over the ventricular endocardial surface of the model to obtain EGMs near the AP ending point. The retrieved unipolar EGMs where used to compute bipolar EGMs, omnipolar EGMs and vector maps. The computed bibolars were compared with real bipolar EGMs, showing clear resemblance. The findings highlight the potential of this new tool as a foundation for further research over optimal ablation sites localisation and cardiac mapping in general. Moreover, it will not only deepen our understanding of the underlying mechanisms of WPW syndrome but also potentially enhance the diagnostic accuracy of cardiac mapping procedures.