Construction of a nomogram to predict urethral stricture after transurethral resection of the prostate: A retrospective cohort study

• Published in: PloS one Vol. 20; no. 2; p. e0313557
• Main Authors: Wan Mokhter, Wan Mokhzani, Duan, Xiaoping, Yang, Jin, Mohamed Daud, Mohamed Ashraf
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 12.02.2025; Public Library of Science (PLoS)
• Subjects: Aged; Aged, 80 and over; Analysis; Biology and Life Sciences; Bladder; Blood; Calibration; Care and treatment; Catheters; Cohort analysis; Data analysis; Data collection; Diabetes; Diagnosis; Engineering and Technology; Group dynamics; Health risks; Hemoglobin; Hospitals; Humans; Hyperplasia; Male; Medical instruments; Medical records; Medicine and Health Sciences; Middle Aged; Multivariate analysis; Nomograms; Nomography (Mathematics); Patient outcomes; Patients; Physical Sciences; Postoperative Complications - etiology; Prediction models; Prostate; Prostate - pathology; Prostate - surgery; Prostate cancer; Prostatectomy, Transurethral; Prostatic Hyperplasia - surgery; Regression analysis; Regression models; Research and Analysis Methods; Retrospective Studies; Risk Factors; ROC Curve; Stricture; Surgery; Transurethral Resection of Prostate - adverse effects; Ultrasonic imaging; Urethral Stricture - diagnosis; Urethral Stricture - etiology; Urinary tract diseases; Urinary tract infections; Urogenital system; United States; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0313557

To investigate the risk factors for urethral stricture (US) in patients with benign prostatic hyperplasia (BPH) after transurethral resection of the prostate (TURP) and to construct a nomogram model with predictive features. Clinical data of 400 patients with BPH who underwent TURP between June 2020 and June 2023 at Chengdu University Hospital were retrospectively collected. The data were divided into US group and no US group. Univariate and multivariate logistic regression analyses were performed sequentially to identify independent risk factors associated with US. Based on the results of the multivariate analysis, a nomogram model predicting the risk of US was constructed. We assessed the discriminatory power and calibration of the models using the C index, ROC curves, and calibration plots. In addition, we performed a decision curve analysis to validate the clinical utility of the model. Data from a total of 400 patients were included in this study, and 35 (8.75%) were diagnosed with US. The results of univariate and multivariate analyses indicated that the following five factors age, prostate size, Preoperative indwelling catheter, Preoperative urethral dilation, Postoperative indwelling catheter time were independent influences on the risk of US. Nomogram model of US was constructed using these independent influences. The area under the curve (AUC) of the subject's operating characteristic was 0.916 (95% CI: 0.868-0.959), and after internal validation, the corrected C-index remained at 0.916. This further validates the accuracy and reliability of the predictive model. Calibration plots and decision curve analyses demonstrated the good clinical value of the column-line diagram model. The nomogram model we constructed can have some guidance in clinical work.