Identifying congestion phenotypes using unsupervised machine learning in acute heart failure

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.

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), followed by validation of the clustering model in the BIOSTAT-CHF cohort (n = 4254). Network analysis was conducted using 363 proteins to identify underlying biological pathways. Five congestion phenotypes were identified: (1) PTC-dilated left ventricle (LV), (2) PTC-HFpEF, (3) PTC, STC-atrial fibrillation (AF), (4) PIVC-dilated left atrium (LA) and LV and (5) Global congestion. Compared with the ‘PTC-dilated LV’ phenotype, the risk of composite outcome was higher in ‘PTC, STC-AF’ and ‘Global’ congestion phenotypes [adjusted HR: 1.74 (1.13–2.67) and 2.41 (1.60–3.63), respectively]. In BIOSTAT-CHF, ‘Global’ congestion phenotype was associated with significantly higher risk [HR: 1.64 (1.04–2.58)]. In network analysis, the immune response pathway was linked to all phenotypes. ‘PTC-HFpEF’ was related to lipid, protein and angiotensin metabolism, ‘PTC, STC-AF’ was related to kinase-mediated signalling, extracellular matrix organisation and TNF-regulated cell death, while ‘PIVC-dilated LA & LV’ was related to kinase-mediated signalling and hemostasis.

In worsening HF, clustering techniques identified clinical congestion profiles associated with both long-term clinical risk and differences in biomarkers, suggesting potential different underlying pathophysiologies. These clusters can be applied using the available online model to identify phenotypes as well as associated risks (https://cic-p-nancy.fr/ai-cong-hf/).