Using machine learning to identify local cellular properties that support re-entrant activation in patient-specific models of atrial fibrillation.

ABSTRACT:

Aims

Atrial fibrillation (AF) is sustained by re-entrant activation patterns. Ablation strategies have been proposed that target regions of tissue that may support re-entrant activation patterns. We aimed to characterize the tissue properties associated with regions that tether re-entrant activation patterns in a validated virtual patient cohort.

Methods and results

Atrial fibrillation patient-specific models (seven paroxysmal and three persistent) were generated and validated against local activation time (LAT) measurements during an S1-S2 pacing protocol from the coronary sinus and high right atrium, respectively. Atrial models were stimulated with burst pacing from three locations in the proximity of each pulmonary vein to initiate re-entrant activation patterns. Five atr

SUBMITTER: Corrado C 

PROVIDER: S-EPMC7943361 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature