ArticleEuropean heart journal. Digital health2026
Machine learning-based prediction of mortality and hospitalization in diabetic patients with heart failure with preserved ejection fraction: the GUARDIAN-P risk score.
Article in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Aims: Diabetes mellitus (DM) is a major contributor to adverse outcomes in patients with heart failure with preserved ejection fraction (HFpEF). We aim to develop and externally validate a machine learning-based model using a random survival forest (RSF) approach for predicting the composite outcome of hospitalization for heart failure (HHF) and cardiovascular (CV) death in patients with DM and HFpEF. Methods and results: This retrospective cohort study included 1450 adult patients with coexisting DM and HFpEF identified from the National Taiwan University Hospital-Integrated Medical Database. An initial RSF model was trained using 27 clinical variables, and the top 9 predictors were selected to construct a parsimonious final model. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC), and external validation was conducted in an independent cohort ( Conclusion: The RSF-based model incorporating nine routinely available variables accurately predicts HHF and CV death in patients with DM and HFpEF. This tool may support personalized risk assessment and guide clinical decision-making.
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