Identifying congestion phenotypes using unsupervised machine learning in acute heart failure.

ABSTRACT:

Aims

Data-driven clustering techniques may improve heart failure (HF) categorisation and provide prognostic insights. The present study aimed to elucidate the underlying pathophysiology of acute HF phenotypes based on pulmonary and systemic congestion at both the tissue (PTC, pulmonary tissue congestion; STC, systemic tissue congestion) and intravascular (PIVC, pulmonary intravascular congestion; SIVC, systemic intravascular congestion) level and to assess the association of identified phenotypes with a composite outcome of HF hospitalisation and death.

Methods and results

Nineteen clinical, laboratory, and echocardiographic congestion markers were analyzed using clustering techniques to identify phenotypes in patients with worsening HF in the Nancy-HF cohort (n = 741

SUBMITTER: Rastogi T 

PROVIDER: S-EPMC12450512 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature