Machine learning-based prediction of atrial fibrillation in patients with atrial high-rate episodes.

ABSTRACT:

Background

Given the modest performance of available predictive models in estimating the risk of atrial fibrillation (AF) in patients with atrial high-rate episodes (AHREs) detected by cardiac implantable electronic devices (CIEDs), this study explores the potential use of machine learning (ML) algorithms in this context.

Purpose

To assess the ability of ML techniques in identifying patients with AHRE at high risk of AF.

Methods

In this prospective study, we enrolled patients without a prior history of AF who experienced at least one AHRE episode detected by CIEDs. ML techniques were applied to predict the 1-year risk of developing new-onset AF based on the following variables: age, BMI, sex, smoking, hypertension, diabetes, coronary artery disease, chronic kidney dis

SUBMITTER: Askarinejad A 

PROVIDER: S-EPMC12811835 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature