ArticleBMC urology2024
Development and validation of a predictive model for failure of ureteral access sheath placement in patients with ureteral calculi.
Article in BMC urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- The impact of preoperative CT-based 3D visualization model on ureteric access sheath placement failure without pre-stenting.BMC urology · 2026Article
- Ureteral wall thickness as an imaging-based predictor of difficult ureter during ureteroscopic lithotripsy: a retrospective analysis.BMC urology · 2026Article
- Machine learning to predict ureteral access sheath placement failure in flexible ureteroscopy: development and prospective validation.BMC urology · 2026Article
- Clinical risk factors and machine learning-assisted interpretation of ureteral access sheath insertion failure: a multicenter study and meta-analysis.Frontiers in surgery · 2026Article
- The role of preoperative ureteral diameter measurements in predicting difficult access during retrograde ıntrarenal surgery: a retrospective analysis of 234 patients.Urolithiasis · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
objectiveThe 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).
methodsThis 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).
resultsSignificant 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.
conclusionsYoung 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.
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