The success of retrograde intrarenal surgery (RIRS) largely depends on the successful placement of a ureteral access sheath (UAS). This study aimed to develop and validate a Logistic regression-based predictive model to assess the risk of UAS insertion failure during primary RIRS, providing a preoperative reference for surgical decision-making.

The data of patients undergoing primary RIRS at the Urology Department of Huadu District People’s Hospital, Guangzhou, from January 2023 to September 2024 were retrospectively analyzed. Patients were divided into success (396 cases, 83.19%) and failure (80 cases, 16.81%) groups based on UAS placement outcomes. Independent risk factors for UAS failure were identified using univariate and multivariate Logistic regression, and a dynamic online nomogram model was constructed. Model performance was evaluated via receiver operating characteristic (ROC) curve analysis, calibration curve, and decision curve analysis (DCA), with internal validation using the Bootstrap method.

Male gender (OR=4.793, P<0.001), younger age (OR=0.963, P=0.004), higher BMI (OR=1.129, P=0.011), absence of ipsilateral ureteral surgical history (OR=0.071, P=0.010), and smaller maximum stone diameter on the operative side (OR=0.890, P<0.001) were identified as independent risk factors for UAS failure. The model demonstrated strong discrimination (AUC=0.811, 95%CI: 0.764-0.859) and calibration (Hosmer-Lemeshow test: χ²=2.871, P=0.942). DCA confirmed its clinical utility, and Bootstrap validation yielded an AUC of 0.804 (95%CI: 0.758-0.850).

Age, gender, BMI, ipsilateral ureteral surgical history, and stone size are associated with UAS failure during primary RIRS. The predictive model constructed based on these factors demonstrates good discrimination, calibration, and net clinical benefit. It can serve as a valuable reference for preoperative surgical plan selection.