Malnutrition Severity Drives Mortality in Geriatric Heart Failure: A Multicenter Extreme Gradient Boosting Analysis.

Figures

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  Fig. 1.. A flowchart illustrating the data screening process of this study. HF, heart failure; AMI, acute myocardial infarction; BMU 1st AH, first affiliated hospital of bengbu medical university; BCH, bengbu central hospital.

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  Fig. 2.. Variable importance and correlation analysis. (A) AUC values of each variable. (B) Heatmap of correlations among variables, with more intense colors indicating higher correlation. *, **, ***, and **** indicate statistical significance at different levels, with more asterisks representing stronger significance.

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  Fig. 3.. LASSO regression for variable selection. (A) LASSO coefficient path plot. (B) LASSO cross-validation curve, the vertical dashed lines indicate the optimal λ values where the model achieves improved predictive performance while retaining the corresponding variables. LASSO, least absolute shrinkage and selection operator.

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  Fig. 4.. ROC curves of the predictive model. (A) Training set. (B) Validation set. (C) Testing set. ROC, receiver operating characteristic.

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  Fig. 5.. Feature importance and impact analysis. (A) Feature importance plot. Features are ranked by mean absolute SHAP values in descending order. (B) Beeswarm plot of significant variables, demonstrating the impact of each feature on model output. Each dot represents a patient, with color indicating feature value (yellow: higher values; purple: lower values). Increased dot dispersion suggests greater influence on predictions. SHAP, shapley additive explanation.

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  Fig. 6.. Model calibration and clinical utility evaluation. (A) Calibration curve plotted on the testing set to evaluate the agreement between predicted and observed probabilities. The gray solid line represents the ideal reference where predicted probability equals observed probability. The black solid line indicates the logistic calibration fit, reflecting the overall calibration trend of the model. The dotted line depicts the nonparametric calibration curve, capturing local deviations from ideal calibration. (B) Clinical decision curve based on the testing set. The x-axis is the risk threshold, and the y-axis is the benefit. The blue line represents intervening only when the model predicts risk above the threshold, the red line represents treating all patients, and the green line represents treating none. Where the blue line lies above the other two, using the model provides the greatest net benefit.