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Article

Identifying congestion phenotypes using unsupervised machine learning in acute heart failure

14 Jul 2025

Abstract excerpt

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...

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Cardiac Fibrosis and Remodeling
Cardiovascular Function and Risk Factors
Heart Failure Treatment and Management

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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PMID: 40985000
DOI: 10.1093/ehjdh/ztaf065

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