Machine Learning Risk Prediction for Incident Heart Failure in Patients With Atrial Fibrillation.

ABSTRACT:

Background

Atrial fibrillation (AF) increases the risk of heart failure (HF); however, little focus is placed on the risk stratification for, and prevention of, incident HF in patients with AF.

Objectives

This study aimed to construct and validate a machine learning (ML) prediction model for HF hospitalization in patients with AF.

Methods

The Fushimi AF Registry is a community-based prospective survey of patients with AF in Fushimi-ku, Kyoto, Japan. We divided the data set of the registry into derivation (n = 2,383) and validation (n = 2,011) cohorts. An ML model was built to predict the incidence of HF hospitalization using the derivation cohort, and predictive ability was examined using the validation cohort.

Results

HF hospitalization occurred in 606 patient

SUBMITTER: Hamatani Y 

PROVIDER: S-EPMC9700042 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature