Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using machine learning.

ABSTRACT:

Aims

Atrial fibrillation (AF) is the most common sustained arrhythmia among patients with hypertrophic cardiomyopathy (HCM), leading to increased symptom burden and risk of thromboembolism. The HCM-AF score was developed to predict new-onset AF in patients with HCM, though sensitivity and specificity of this conventional tool are limited. Thus, there is a need for more accurate tools to predict new-onset AF in HCM. The objective of the present study was to develop a better model to predict new-onset AF in patients with HCM using machine learning (ML).

Methods and results

In this prospective, multicentre cohort study, we enrolled 1069 patients with HCM without a prior history of AF. We built a ML model (logistic regression with Lasso regularization) using clinical variables.

SUBMITTER: Lu R 

PROVIDER: S-EPMC11859767 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature