Key result
Minimally supervised Graph Neural Network clustering identified 5 survival-associated HFpEF groups with an internal validation accuracy of 0.706 and an F1-score of 0.613.
Why the study?
Substantial biological and clinical heterogeneity in HFpEF limits therapeutic success, and whether graph-based machine learning methods can identify distinct patient clusters linked to clinical characteristics and outcomes needed investigation.
Does a minimally supervised Graph Neural Network improve patient risk stratification and survival prediction compared to conventional unsupervised clustering methods in symptomatic HFpEF patients?
Observational (n=133)
Does a minimally supervised Graph Neural Network improve patient risk stratification and survival prediction compared to conventional unsupervised clustering methods in symptomatic HFpEF patients?
Answers are grounded in this paper and cite their sources.
Minimally supervised graph-based machine learning effectively captures complex multimodal data in HFpEF to identify five distinct patient clusters that independently predict survival.
Heart failure with preserved ejection fraction (HFpEF) is associated with poor outcomes, with 5-year survival after hospitalisation as low as 35-40%. Despite its growing prevalence, effective treatments remain limited. A major barrier to therapeutic success is the substantial biological and clinical heterogeneity within HFpEF, suggesting that distinct patient profiles may require different management strategies. This study investigates whether graph-based machine learning methods can identify distinct patient clusters within the HFpEF population and assess their relationships with clinical characteristics and outcomes. We analysed a multimodal dataset of 133 symptomatic HFpEF patients, comprising more than 240 K attributes derived from comprehensive cardiovascular assessment. These included cardiac MRI, echocardiography, circulating biomarkers of fibro-inflammation, and high-resolution proteomics. Five conventional unsupervised clustering methods were first applied but failed to produce clinically coherent groups, largely due to the complexity and multimodality of the data. To address this, we implemented a minimally supervised Graph Neural Network (GNN) that incorporated limited label information while modelling relationships between patients. This approach produced five clusters with potential clinical relevance. Importantly, cluster membership remained significantly associated with survival after adjustment for established clinical risk factors, including age, sex, BNP levels, symptom burden, and kidney function. Internal validation yielded an accuracy of 0.706 and an $F1$-score of 0.613, indicating reasonably consistent performance and suggesting that graph structure may aid generalisation and class-wise prognostic prediction. Overall, the findings show that minimally supervised graph-based learning can more effectively capture the underlying structure of multimodal HFpEF data than traditional methods. Visualisation and cluster quality metrics further support the effectiveness of the GNN approach.
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Saleem et al. (2026) conducted an observational in Heart failure with preserved ejection fraction (HFpEF) (n=133). Minimally supervised Graph Neural Network (GNN) risk stratification vs. Conventional unsupervised clustering methods was evaluated on Survival and prognostic risk stratification (internal validation accuracy and F1-score) (Accuracy 0.706; F1-score 0.613). Minimally supervised Graph Neural Network clustering identified 5 survival-associated HFpEF groups with an internal validation accuracy of 0.706 and an F1-score of 0.613.
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