Using machine learning to enhance prediction of atrial fibrillation recurrence after catheter ablation.

ABSTRACT:

Background

Traditional risk scores for recurrent atrial fibrillation (AF) following catheter ablation utilize readily available clinical and echocardiographic variables and yet have limited discriminatory capacity. Use of data from cardiac imaging and deep learning may help improve accuracy and prediction of recurrent AF after ablation.

Methods

We evaluated patients with symptomatic, drug-refractory AF undergoing catheter ablation. All patients underwent pre-ablation cardiac computed tomography (cCT). LAVi was computed using a deep-learning algorithm. In a two-step analysis, random survival forest (RSF) was used to generate prognostic models with variables of highest importance, followed by Cox proportional hazard regression analysis of the selected variables. Events of inte

SUBMITTER: Brahier MS 

PROVIDER: S-EPMC10692862 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature