Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values.

ABSTRACT: This study aims to develop a machine learning model utilizing Computed Tomography (CT) values to predict systemic inflammatory response syndrome (SIRS) after endoscopic surgery for kidney stones. The goal is to identify high-risk patients early and provide valuable guidance for urologists in the early diagnosis and intervention of post-operative urosepsis. This study included 833 patients who underwent retrograde intrarenal surgery (RIRS) or percutaneous nephrolithotomy (PCNL) for kidney stones. Five machine learning algorithms and ten preoperative or intraoperative variables were used to develop a predictive model for SIRS. The SHapley Additive exPlanations (SHAP) method was used to explain the distribution of feature importance in the model's predictions. Among the 833 patients, 126 (15.

SUBMITTER: Li J 

PROVIDER: S-EPMC11799361 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature