Development and validation of a predictive model for failure of ureteral access sheath placement in patients with ureteral calculi

Objective The Ureteral Access Sheath (UAS) has notable benefits but may fail to traverse the ureter in some cases. Our objective was to develop and validate a dynamic online nomogram for patients with ureteral stones who experienced UAS placement failure during retrograde intrarenal surgery (RIRS). Methods This study is a retrospective cohort analysis using medical records from the Second Hospital of Tianjin Medical University. We reviewed the records of patients with ureteral stones who underwent RIRS in 2022 to identify risk factors associated with UAS placement failure. Lasso combined logistic regression was utilized to identify independent risk factors associated with unsuccessful UAS placement in individuals with ureteral stones. Subsequently, a nomogram model was developed to predict the likelihood of failed UAS placement in this patient cohort. The model's performance was assessed through Receiver Operating Characteristic Curve (ROC) analysis, calibration curve assessment, and Decision Curve Analysis (DCA). Results Significant independent risk factors for unsuccessful UAS placement in patients with ureteral stones included age (OR = 0.95, P < 0.001), male gender (OR = 2.15, P = 0.017), body mass index (BMI) (OR = 1.12, P < 0.001), history of stone evacuation (OR = 0.35, P = 0.014), and ureteral stone diameter (OR = 0.23, P < 0.001). A nomogram was constructed based on these variables. Model validation demonstrated an area under the ROC curve of 0.789, indicating good discrimination. The calibration curve exhibited strong agreement, and the decision curve analysis revealed a favorable net clinical benefit for the model. Conclusions Young age, male sex, high BMI, no history of stone evacuation, and small diameter of ureteral stones were independent risk factors for failure of UAS placement in patients with ureteral stones, and the dynamic nomogram established with these 5 factors was clinically effective in predicting the outcome of UAS placement.