Development of a nomogram to predict risk factors for orchiectomy after testicular torsion in children

Testicular torsion (TT) in children is a common urological emergency, and timely and accurate management is crucial for prognosis. Orchiectomy is one of the severe complications of this condition, and accurately predicting its risk is of great significance for clinical decision-making. This study aims to develop a nomogram to predict risk factors for orchiectomy after TT in children. This study retrospectively collected clinical data from 327 cases of TT at the Children's Hospital of Fudan University and 141 cases at Anhui Provincial Children's Hospital, which were classified into the training and validation cohorts, respectively. Multivariate logistic regression analysis was performed to identify independent predictors of orchiectomy in TT patients, and a nomogram was constructed. The model's effectiveness in both the training and validation cohorts was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). A total of 468 cases were included, of which 230 (49.15%) ultimately underwent orchiectomy. Duration of symptoms, neutrophil count, eosinophil count, degree of torsion, undescended testis(UDT), Testicular Workup for Ischemia and Suspected Torsion(TWIST) score grading, and ultrasound blood flow signal were identified as independent risk factors for orchiectomy in children with TT and were used to construct the nomogram. The AUCs of the nomogram were 0.93 (95% CI: 0.91-0.96) in the training cohort and 0.86 (95% CI: 0.79-0.92) in the validation cohort. The calibration curves demonstrated good agreement between predicted and observed values, and DCA indicated that the constructed nomogram had a high clinical net benefit. The nomogram developed in this study effectively predicts the risk of orchiectomy after TT in children, providing clinicians with a valuable decision-making tool. Future multicenter clinical studies are needed to optimize and validate the model's effectiveness.