A machine learning model for predicting 28-day mortality in patients with alcoholic cirrhosis and sepsis: a study based on the MIMIC-IV database

OBJECTIVE: To develop and validate the best machine learning model for predicting 28-day mortality in septic patients with alcoholic cirrhosis (AC), leveraging data from the Medical Information Mart for Intensive Care Database, 4th Edition (MIMIC-IV). METHODS: Least Absolute Shrinkage and Selection Operator (LASSO) regression. Patients were allocated to a training set (n=1268), a testing set (n=318), and a temporal validation set (n=372). Five models were developed and evaluated based on different performance metrics. Decision Curve Analysis (DCA) and SHapley Additive exPlanations (SHAP) were performed to assess clinical applicability and predictor importance. RESULTS: LASSO regression identified 15 core variables. The eXtreme Gradient Boosting (XGBoost) model achieved the best overall performance, with an area under the ROC curve (AUC) of 0.946 (95%CI [0.932-0.960]) (sensitivity: 0.891) on the training set and an AUC of 0.878 (95% CI [0.838, 0.918]) on the test set, and an AUC of 0.819 on the validation set. The model was well-calibrated and provided a higher net benefit than 'treat-all' or 'treat-none' strategies across wide risk thresholds. SHAP analysis revealed the top 5 predictors to be: Sequential Organ Failure Assessment (SOFA) score, Model for End-Stage Liver Disease (MELD) score, age, temperature, and SPO2. CONCLUSION: The XGBoost model had the best predictive performance, providinga robust tool to guide personalized therapy and optimize critical care resource allocation.