Machine learning-based risk models for procedural complications of radiofrequency ablation for atrial fibrillation.

ABSTRACT:

Background

Radiofrequency ablation (RFA) for atrial fibrillation (AF) is associated with a risk of complications. This study aimed to develop and validate risk models for predicting complications after radiofrequency ablation of atrial fibrillation patients.

Methods

This retrospective cohort study included 3365 procedures on 3187 patients with atrial fibrillation at a single medical center from 2018 to 2021. The outcome was the occurrence of postoperative procedural complications during hospitalization. Logistic regression, decision tree, random forest, gradient boosting machine, and extreme gradient boosting were used to develop risk models for any postoperative complications, cardiac effusion/tamponade, and hemorrhage, respectively. Patients' demographic characteristics, m

SUBMITTER: Li R 

PROVIDER: S-EPMC10636945 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature