Accurate prediction of mortality in children with sepsis: development and validation of an explainable model based on real-world data

BACKGROUND: Sepsis remains the leading cause of in-hospital deaths among children, and there is currently a lack of precise early prediction models. This study aimed to develop an interpretable machine learning (IML) model to predict in-hospital mortality in pediatric patients diagnosed with sepsis according to the 2024 Phoenix Sepsis Diagnostic Criteria. METHODS: This single-center retrospective cohort study analyzed 464,459 children who were hospitalized in the Children's Hospital Affiliated to Nanjing Medical University from January 2018 to December 2023 were screened. Based on the Phoenix Sepsis Score, 2,477 cases of childhood sepsis were finally diagnosed. The features were filtered using three methods the minimum absolute contraction and selection operator (LASSO) regression, the Boruta algorithm, and random forest importance ranking, ultimately identifying 12 features. Eight machine learning algorithms were trained on 70% of the data and evaluated on a 30% test set. The optimal model was screened based on the accuracy of the test set and the area under the receiver operating characteristic curve (AUC). The interpretability of the model is enhanced by leveraging SHapley Additive exPlanations (SHAP) summary graphs, individual SHAP force maps, and partial dependency plots (PDPs). RESULTS: ratio were the top five features that had the greatest impact on the CatBoost model. SHAP analysis provided both global feature importance and individualised risk explanations, and the model has been deployed as a freely accessible web calculator. CONCLUSIONS: The IML model developed in this study provides a highly interpretable and accurate tool for the early prediction of in-hospital mortality in pediatric sepsis patients. The SHAP approach improves model interpretability and helps clinicians understand the factors driving those predictions. However, as a single-center retrospective study, the generalizability of our findings to other clinical settings may be limited. Future multi-center prospective studies are necessary to further validate the model and address its current limitations in sensitivity to minimize the risk of missing high-risk cases.