Development and validation of an explainable machine learning model for predicting sepsis risk following flexible ureteroscopic lithotripsy

Sepsis is a severe complication of flexible ureteroscopic lithotripsy (fURL), a widely used treatment for kidney stones. This study aimed to develop and validate a predictive model based on machine learning (ML) for assessing the risk of sepsis following fURL while enhancing its interpretability through Shapley Additive Explanations (SHAP). This retrospective study in China was conducted to develop and validate a prediction model for sepsis following fURL. The derivation cohort comprised 1,386 patients treated between 2019 and July 2024 divided into training and internal validation subsets. External validation was performed on a cohort of 604 patients treated between 2019 and 2023 at a collaborating center. Sepsis was diagnosed according to Sepsis-3.0 consensus guidelines. Fifteen machine learning algorithms were employed to construct predictive models, and their performance was meticulously evaluated using metrics such as the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, the Shapley Additive Explanations (SHAP) method was applied to assess and rank the importance of individual features. The Extra Trees (ET) model incorporating eight key features demonstrated the best discriminative ability, with an AUC of 0.90. It accurately predicted sepsis in both internal (AUC = 0.87) and external validation (AUC = 0.81). In this study, we developed an Extra Trees (ET) machine learning model to predict sepsis risk following fURL, which demonstrated high accuracy in predicting sepsis in both the internal and external validation cohorts. This model, equipped with SHAP-driven interpretability and deployed as an accessible web application, has the potential to serve as a clinical tool for patient risk stratification following fURL.