A Machine Learning-Based Prognostic Model for Sepsis-Associated Liver Injury Using Routine Indicators

Objective: Sepsis-associated liver injury (SALI) occurs in approximately 40% of sepsis cases and is linked to high mortality, a challenge that may stem from the absence of effective prognostic models. We developed a machine learning (ML)-based prognostic model for SALI using conventional biomarkers to guide precise clinical interventions and reduce mortality. Methods: We retrospectively analyzed 307 SALI patients (2010–2024), stratified into favorable (n=139) and poor (n=168) prognosis groups by post-treatment progression. The cohort was randomly split into a training set (80%) and a validation set (20%). The routine biomarkers included hematological indices, liver/renal function parameters, and coagulation profiles. Feature selection used LASSO regression. Nine machine learning algorithms constructed prognostic models: eXtreme Gradient Boosting, Logistic Regression, Light Gradient Boosting Machine, Random Forest, Adaptive Boosting, Gradient Boosting Decision Tree, Gaussian Naive Bayes, and Multilayer Perceptron. Model interpretability was evaluated via the SHapley Additive exPlanation (SHAP) algorithm. An independent cohort of 37 SALI patients was used for external validation. Results: Key parameters influencing SALI prognosis were red blood cell distribution width-coefficient of variation (RDW-CV), anion gap (AG), and high-sensitivity cardiac troponin (hs-cTn). Among the nine models, the Random Forest prognostic model performed best, with an area under the curve (AUC) of 0.816 in the validation set and 0.781 in the external validation. Conclusions: The Random Forest model developed in this study can provide some guidance for clinical decision-making in SALI patients, but further validation is still required and should only be implemented in clinical practice after further research.