Risk stratification of major arrhythmia events in Japanese patients with Brugada syndrome using machine learning models.

ABSTRACT:

Background

Brugada syndrome (BrS) has been known to cause fatal arrhythmias, and an effective risk stratification method should be developed.

Objective

This study aimed to construct a risk prediction model for BrS using machine learning models.

Methods

We enrolled 234 Japanese patients with BrS and analyzed the clinical information including the age, gender, history of syncope, family history of BrS or sudden cardiac death, PR interval in lead Ⅱ, QRS duration in V6, RR interval in V1, r-J interval in V1, T-peak-to-T-end interval, max QTc, fragmented QRS, Type-1 in peripheral leads, spontaneous type 1 pattern, aVR sign, presence of early repolarization (ER), and presence of ER in the peripheral leads. We validated the previous stratification method (BRUGADA-RISK and Pr

SUBMITTER: Ishida S 

PROVIDER: S-EPMC12302177 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature