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Malnutrition Severity Drives Mortality in Geriatric Heart Failure: A Multicenter Extreme Gradient Boosting Analysis
Yiming Chen1, Min He2, Mengyu He3
1Department of Geriatrics, The First Affiliated Hospital of Bengbu Medical University, 233004 Bengbu, Anhui, China.
Reviews in Cardiovascular Medicine
|June 4, 2026
Summary
A new machine learning model accurately predicts in-hospital mortality in older heart failure (HF) patients with malnutrition. The Geriatric Nutritional Risk Index (GNRI) is a key predictor, aiding clinical risk assessment.
Area of Science:
- Geriatric medicine
- Cardiology
- Artificial intelligence in healthcare
Background:
- Heart failure (HF) and malnutrition are common in older adults (≥65 years), significantly increasing in-hospital mortality.
- Limited predictive models exist for this high-risk population, necessitating novel approaches.
Purpose of the Study:
- To develop and validate a personalized machine learning model for assessing key risk factors of in-hospital mortality.
- To identify critical predictors for improved risk stratification in older HF patients with malnutrition.
Main Methods:
- A multicenter retrospective study involving older HF patients with malnutrition.
- Utilized least absolute shrinkage and selection operator (LASSO) regression for predictor selection and extreme gradient boosting (XGBoost) for model development.
- Assessed model performance using ROC analysis, accuracy, sensitivity, specificity, F1 score, and validated externally. Interpretable feature importance was determined via SHAP analysis.
Main Results:
- The study included 1080 patients, with an in-hospital mortality rate of 22.6%.
- The XGBoost model demonstrated high predictive performance (AUCs: training 0.979, validation 0.890, test 0.936).
- SHAP analysis identified the Geriatric Nutritional Risk Index (GNRI) as the primary predictor, followed by inflammatory, cardiorenal, and electrolyte markers.
Conclusions:
- The developed XGBoost model shows robust predictive capabilities for in-hospital mortality in this population.
- SHAP analysis offers valuable clinical insights into key risk factors, enhancing risk assessment strategies.
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