Scandinavian journal of gastroenterology · 2026
A machine learning model for predicting 28-day mortality in patients with alcoholic cirrhosis and sepsis: a study based on the MIMIC-IV database.
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Cao Xu, Wang Dingmin, Li Wenling, Cheng Congcong, Fei Sujuan
Аннотация
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: Clinical data of 1958 patients with AC complicated with sepsis were retrospectively extracted. Missing data (<20%) were addressed using Multiple Imputation by Chained Equations (MICE). Key predictors were selected via 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.
METHODS: Clinical data of 1958 patients with AC complicated with sepsis were retrospectively extracted. Missing data (<20%) were addressed using Multiple Imputation by Chained Equations (MICE). Key predictors were selected via 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.