Using Machine Learning to Predict the Duration of Atrial Fibrillation: Model Development and Validation.

ABSTRACT:

Background

Atrial fibrillation (AF) is a progressive disease, and its clinical type is classified according to the AF duration: paroxysmal AF, persistent AF (PeAF; AF duration of less than 1 year), and long-standing persistent AF (AF duration of more than 1 year). When considering the indication for catheter ablation, having a long AF duration is considered a risk factor for recurrence, and therefore, the duration of AF is an important factor in determining the treatment strategy for PeAF.

Objective

This study aims to improve the accuracy of the cardiologists' diagnosis of the AF duration, and the steps to achieve this goal are to develop a predictive model of the AF duration and validate the support performance of the prediction model.

Methods

The study included 272

SUBMITTER: Shimoo S 

PROVIDER: S-EPMC11624443 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature