Development and validation of a preoperative nomogram for predicting patients with impacted ureteral stone: a retrospective analysis

Springer Science and Business Media LLC - Tập 21 - Trang 1-10 - 2021
Chenglu Wang1, Lu Jin2, Xinyang Zhao2, Boxin Xue2, Min Zheng1
1Reproductive Medicine Center, Department of Reproductive Endocrinology, Affiliated People’s Hospital, Zhejiang Provincial People’s Hospital, Hangzhou Medical College, Hangzhou, People’s Republic of China
2Department of Urology, Second Affiliated Hospital of Soochow University, Suzhou, People’s Republic of China

Tóm tắt

To develop and validate a practical nomogram for predicting the probability of patients with impacted ureteral stone. Between June 2020 to March 2021, 214 single ureteral stones received ureteroscopy lithotripsy (URSL) were selected in development group. While 82 single ureteral stones received URSL between April 2021 to May 2021 were included in validation group. Independent factors for predicting impacted ureteral stone were screened by univariate and multivariate logistic regression analysis. The relationship between preoperative factors and stone impaction was modeled according to the regression coefficients. Discrimination and calibration were estimated by area under the receiver operating characteristic (AUROC) curve and calibration curve respectively. Clinical usefulness of the nomogram was evaluated by decision curve analysis. Age, ipsilateral stone treatment history, hydronephrosis and maximum ureteral wall thickness (UWTmax) at the portion of stone were identified as independent predictors for impacted stone. The AUROC curve of development and validation group were 0.915 and 0.882 respectively. Calibration curve of two groups showed strong concordance between the predicted and actual probabilities. Decision curve analysis showed that the predictive nomogram had a superior net benefit than UWTmax for all examined probabilities. We developed and validated an individualized model to predict impacted ureteral stone prior to surgery. Through this prediction model, urologists can select an optimal treatment method and decrease intraoperative and postoperative complications for patients with impacted ureteral calculus.

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