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Updated: Jan 11, 2026

Unilateral Ureteral Obstruction Model for Investigating Kidney Interstitial Fibrosis
Published on: April 25, 2025
Risk factors and prediction model for stone mucosa adhesion before retrograde intrarenal surgery
Hongliang Wei1, Xiaowei Wang1, Shuo Zheng1
1Department of Urology, First Hospital of Hebei Medical University, Shijiazhuang, China.
Introduction:
Preoperative assessment of ureteral adhesions related to stones is crucial for choosing the right surgical approach.
Aim:
This study aimed to evaluate clinically significant factors for predicting the development of adhesions between the calculus and the mucosa in patients with ureteral stones undergoing retrograde intrarenal surgery.
Materials And Methods:
The study included 173 patients. Ureteroscopy was performed to accurately identify the presence of adhesions between the calculus and the mucosa. Univariate and multivariable logistic regression analyses were performed to identify independent factors predicting adhesions. An alignment diagram model was developed utilizing the independent factors identified. Discrimination and calibration of the model were evaluated via the receiver operating characteristic curve and further examined via calibration curves, decision curve analysis, and cumulative intervention curves.
Result:
Adhesions between the calculus and the mucosa were confirmed on ureteroscopy in 48 patients (27.7%). Multivariable logistic regression analyses showed that ureteral wall thickness (UWT), hydronephrosis severity, perirenal fat stranding (PFS), and pain intensity were independent risk predictors of adhesions (P <0.001; P <0.001; P = 0.02; P = 0.02, respectively). The estimated area under the curve in the group with and without adhesions was 0.849 (95% CI, 0.742-0.832) and 0.888 (95% CI, 0.829-0.833), respectively, demonstrating an excellent predictive performance of the model.
Conclusion:
UWT, PFS, hydronephrosis severity, and pain intensity are independent risk factors for the development of adhesions between the calculus and the mucosa. Our predictive model exhibited outstanding performance, and it may help clinicians choose the most appropriate surgical method.
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