Machine learning-based predictive model for atrial arrhythmia following transcatheter atrial septal defect closure.

ABSTRACT:

Background

Atrial septal defects (ASDs) are frequently closed percutaneously. Despite successful closure, many patients still develop atrial arrhythmias. There is inconsistent data on the risk factors associated with these atrial arrhythmias. As such, we aimed to develop a machine learning (ML) model predicting atrial arrhythmias following transcatheter ASD closure in adolescents and adults.

Methods

Patients with secundum-type ASDs undergoing transcatheter closure between 2008 and 2024 at a single center were retrospectively analyzed. Patients with prior atrial arrhythmias were excluded. A deep neural network, adapted via transfer learning from a large external ECG dataset, was used to extract predictive features from preprocedural 12-lead ECGs. These features were combined

SUBMITTER: Jacquemyn X 

PROVIDER: S-EPMC12664486 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature