Construction and validation of a mortality prediction model for patients with sepsis-associated encephalopathy: Interpretable machine learning approach.

Objectives: Sepsis-associated encephalopathy (SAE) is a prevalent complication among critically ill sepsis patients with poor outcomes. This study aimed to develop and validate an interpretable machine learning model for predicting mortality in SAE patients to support clinical decision-making. Methods: The study utilized two large critical care databases: the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database for model construction and internal validation, and the eICU Collaborative Research Database (eICU-CRD, version 2.0.1) for external validation. The XGBoost model was trained to predict patient mortality at 28 and 90 days after ICU admission, and performance was assessed by indicators including the area under the curve (AUC). To improve the interpretability of the model, SHapley Additive exPlanations (SHAP) analysis revealed key features affecting prognosis at both population and individual levels. Results: A total of 4922 SAE patients were included, with 39 variables selected for model development. The XGBoost model performed best, with internal validation AUCs of 0.930 (95% CI: 0.917-0.942) and 0.906 (95% CI: 0.891-0.919) for 28-day and 90-day predictions, respectively. Notably, external validation achieved AUCs of 0.771 (95% CI: 0.748-0.792) and 0.759 (95% CI: 0.736-0.782), respectively. The Shapley value analytical framework was systematically applied to decode feature importance patterns and illuminate individual prognostic determinants. Conclusions: Validated across two large critical care databases, the interpretable XGBoost model serves as a reliable tool for mortality prediction in SAE patients, which may help clinicians identify high-risk SAE patients early and optimize management to improve patient outcomes.