One of the primary surgical techniques for upper urinary calculi is retrograde intrarenal surgery (RIRS). Urosepsis is a severe complication of RIRS and poses significant risks to patients and challenges clinicians. Machine learning (ML) is a unique, proven model used to identify the high-risk patient population and enhance clinical decisions.

To predict postoperative Urosepsis after retrograding intrarenal surgery, this study set out to develop a machine learning model. The dataset was obtained from 261 patients who had RIRS, and it included demographic, clinical, and procedural variables. Urosepsis occurrence was the target variable estimated based on the supervised machine learning algorithms, which include Random Forest, Logistic Regression, XGBoost, Decision Tree Classifier, LDA Classifier, and Support Vector Machine. The models were evaluated based on parameters like accuracy, precision, recall, and Area Under the Receiver Operating Characteristic curve (AUROC).

Specific factors were also found to have predictive value; these were the patient's age, intraoperative complications, and inflammation markers after surgery. The clinical significance of feature importance analysis was ascertained for risk classification of Urosepsis. The SVM classifier's accuracy was evaluated as higher, with 92% and recall and precision scores of 0.92 and 0.93. Thus, it is a promising instrument for predicting dependability.

This work captures the possibility of identifying and preventing the occurrence of Urosepsis among RIRS patients and developing appropriate care plans using machine learning models. Directions for future research include testing these models in a real-world setting and ascertaining whether the superior performance of the model is sustainable across various groups of patients.