From patterns to prognosis: machine learning-derived clusters in advanced heart failure.

ABSTRACT:

Introduction

Advanced heart failure (HF) is a clinically heterogeneous condition with poor prognosis, and traditional classification systems often fail to capture the complexity needed for personalized care. This study aimed to identify clinically meaningful phenotypic subgroups among patients with advanced HF using unsupervised machine learning and to evaluate their association with long-term outcomes.

Methods

A retrospective analysis was conducted on 524 patients with advanced HF who underwent comprehensive clinical, echocardiographic, hemodynamic, and cardiopulmonary exercise assessments. Using k-means clustering on standardized, multidimensional data, two distinct phenotypes were identified. The primary composite outcome was defined as all-cause mortality, left ventricul

SUBMITTER: Karacam M 

PROVIDER: S-EPMC12589050 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature