Construction and validation of a 28-day mortality prediction model for refeeding syndrome in critically ill patients

Refeeding syndrome (RFS) is a common and serious metabolic reaction during nutritional support in critically ill patients, associated with substantially increased mortality risk and poor prognosis. Existing studies mainly focus on predicting its occurrence, while predictive models for 28-day mortality risk remain lacking. This study aimed to develop and validate an interpretable machine learning model to support short-term prognostic assessment in clinical practice.

Data for critically ill patients with RFS were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV, 2008-2019) as the internal cohort. A total of 8,705 patients were randomly assigned to training and test sets at a 7:3 ratio. Thirty-eight candidate variables were initially included, and feature selection was performed using least absolute shrinkage and selection operator (LASSO) logistic regression. Three machine learning models were subsequently constructed, and the optimal model was validated using two independent datasets. 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.

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.

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.