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Transecting or non-transecting: Developing and validating a prediction model for short-segment bulbar urethral
Yubo Gu1,2, Lei Chen3, Jiong Zhang1,2
1Department of Urology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Objective:
Excision and primary anastomosis is a standard approach for managing short-segment bulbar urethral strictures. However, the choice between transecting and non-transecting anastomoses is typically determined during surgery based on intraoperative findings. This study aimed to develop a predictive model for patients with short-segment bulbar urethral strictures, forecasting the likelihood of undergoing a non-transecting procedure through preoperative assessments.
Methods:
Retrospective data collection was conducted for patients diagnosed with short-segment bulbar urethral strictures at our institution from June 1, 2016, to May 31, 2022. The patients were categorized into the transecting and non-transecting groups for the training set. Factors displaying significant differences, as identified through univariate analysis between groups, were integrated into logistic regression for a clinical-radiographic nomogram. External validation utilized prospective cohort data from June 1, 2022, to November 30, 2022.
Results:
The training set comprised 81 patients (44 transecting and 37 non-transecting), and the validation set had 33 (22 transecting and 11 non-transecting). The final model incorporated two factors: the stricture's etiology and ventral scar thickness measured by ultrasound. The model demonstrated an area under the curve of 0.91 (95% CI: 0.85-0.98) for the training dataset and 0.90 (95% CI: 0.79-1.00) for the validation dataset. The validation metrics demonstrated the model's good discrimination and calibration.
Conclusion:
This model enables preoperative prediction of the anastomosis strategy for patients with short-segment bulbar urethral strictures, considering etiology (traumatic or non-traumatic) and ultrasonographic measurements (ventral scar thickness). Having successfully undergone internal and external validation, the model demonstrates robust discrimination and calibration, offering crucial support for healthcare professionals in preoperative decision-making.
