Identifying Cardiogenic Shock Sub-Phenotypes with Machine Learning: A Multicenter Study Combining Clinical and Echocardiographic Data.

ABSTRACT:

Background

Subphenotyping cardiogenic shock (CS) patients using nontraditional clustering methods represent a step toward precision medicine, potentially improving outcomes in this heterogeneous and high-mortality condition.

Objectives

This study aimed to apply an unsupervised machine learning approach to integrate clinical and advanced echocardiographic data, identifying CS subphenotypes associated with different outcomes and features, beyond etiology.

Methods

This multicenter observational study prospectively analyzed 172 patients admitted to cardiac intensive care units with overt CS, from 2021. An exploratory statistical analysis preceded patient clustering using the Elbow Method and K-Means algorithm, based on clinical presentation. Dimensionality reduction was p

SUBMITTER: Ghionzoli N 

PROVIDER: S-EPMC12605059 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature