Table 3_Construction and validation of a 28-day mortality prediction model for refeeding syndrome in critically ill patients.docx

Model interpretability was enhanced using Shapley additive explanations (SHAP). Kaplan–Meier curves were used to compare survival between the high- and low-risk, and nomograms were constructed to facilitate clinical application.</p>Results<p>Nine variables were ultimately selected to construct the logistic regression model. The model demonstrated excellent discriminative performance and generalizability, with area under the receiver operating characteristic curve (AUC) values of 0.821, 0.809, and 0.731 in the internal test set, external validation set I (MIMIC-III), and external validation set II (the Affiliated Hospital of Yangzhou University), respectively.

SHAP analysis identified partial pressure of oxygen, mean blood glucose level, and age as the key prognostic factors and further revealed nonlinear effects among the variables. Kaplan–Meier survival curves confirmed that the model significantly distinguished between the high- and low-risk groups across all cohorts (all p < 0.05). Decision curve analysis showed that the model provided favorable clinical net benefit in both external validation cohorts.</p>Conclusion<p>The interpretable machine learning model developed in this study effectively predicts the 28-day mortality risk of RFS in critically ill patients, provides an objective reference for individualized clinical decision-making, and may help improve patient prognosis.</p>