To establish a prediction model for 28-day in-hospital mortality of patients with septic shock using machine learning (ML) and verify its effectiveness.

The clinical data of patients with septic shock admitted to Suzhou Ninth Hospital Affiliated to Soochow University from January 2019 to June 2024 were analyzed, retrospectively. Patients were divided into death group (104 cases) and survival group (161 cases) based on their prognosis at 28 days after admission. The key variables associated with 28-day in-hospital mortality were screened by the least absolute shrinkage and selection operator (LASSO) algorithm. The entire dataset was randomly divided into training set and test set with a ratio of 3∶1. By comparing eight different ML algorithms, the optimal algorithm was selected to construct the prediction model. The model performance were evaluated by area under the receiver operating characteristic (ROC) curve (AUC) and clinical decision curve analysis (DCA). The model was interpreted by Shapley additive exPlanations (SHAP) algorithm.

Total of 265 patients with septic shock were enrolled. Forty-seven variables were initially included based on clinical preliminary screening; nine key variables were selected by the LASSO model for subsequent model development. Among the eight models constructed, the radial basis function-support vector machine (RSVM) model demonstrated the best overall performance. After optimization by this algorithm, the AUCs were 0.85 for the training set and 0.77 for the test set; the accuracies were 78.8% and 71.6%, respectively; the recall rates were 75.6% and 69.2%, respectively; and the DCA curves indicated higher net benefits. SHAP value analysis revealed that septic shock complicated with multiple organ dysfunction syndrome (involving four or more organs) was the most important predictive variable, followed by the acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score and the sequential organ failure assessment (SOFA) score.

Machine learning could be used to build an accurate prediction model for the 28-day in-hospital mortality risk of patients with septic shock. This model could improve the risk stratification and may guide clinicians in implementing appropriate interventions.