ArticleAsia Pacific journal of clinical nutrition2026
Establishment and validation of a machine learning model to stratify malnutrition risk in hospitalized older patients with chronic heart failure.
Article in Asia Pacific journal of clinical nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
BACKGROUND AND
objectivesMalnutrition among older hospitalized adults with chronic heart failure (CHF) is associated with adverse clinical outcomes, yet reliable early risk stratification tools remain lacking. This study aimed to develop and validate a machine learning (ML) model for malnutrition risk stratification in this population. METHODS AND STUDY
designMalnutrition among older hospitalized adults with chronic heart failure (CHF) is associated with adverse clinical outcomes, yet reliable early risk stratification tools remain lacking. This study aimed to develop and validate a machine learning (ML) model for malnutrition risk stratification in this population.
resultsMalnutrition prevalence was 44.1% (348/790). In the internal testing, CatBoost (CAT) achieved superior performance with an AUC of 0.901 (95% confidence interval [CI]: 0.858-0.943), accuracy of 0.840, recall of 0.753, and the lowest Brier score of 0.113. This model demonstrated strong calibration, clinical utility, and the highest composite score (62/64). External validation confirmed CAT's generalizability (AUC: 0.916, 95% CI: 0.887-0.945). SHAP analysis identified body mass index (BMI), calf circumference, New York Heart Association (NYHA) classification, age, and diabetes as signifi-cant contributors to malnutrition risk.
conclusionsThe CAT-based model effectively stratifies malnutrition risk in older hospitalized CHF patients, offering a tool for early intervention to improve outcomes. Further multicenter prospective studies are needed to validate its real-world applicability.
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