Abstract
The residual stone fragment is a tremendous issue after ureteroscopic lithotripsy and requires urologists to evaluate the condition of patients comprehensively. Our study aimed to construct a nomogram to make a personalized prediction of postoperative residual stone rate (RSR). We implemented a retrospective cohort study in the Department of Urology, Shanghai General Hospital. A total of 277 patients undergoing ureteroscopy (URS) were enrolled in our study. Among them, 186 patients were included in the training group and the remaining 91 patients comprised the testing group. We utilized stepwise forward algorithm and logistic regression analysis to build predictive models and selected the best model based on Akaike's information criterion (AIC). The model was assessed by receiver operating characteristic (ROC) curves and the Hosmer-Lemeshow (HL) test. We also conducted decision curve analysis (DCA) to demonstrate the net benefit of the model. The independent testing group was used to validate the practicability of the nomogram. The severity of hydronephrosis, stone location, the transverse diameter of stone, hypertension, and white blood cell (WBC) were found to be significant predictive variables for RSR after URS. The area under the curve (AUC) of the training group was 0.7203 and that of the testing group was 0.7280. Besides, the nomogram also presented great calibration and accepted net benefit in a wide range of probabilities. Our study achieved a predictive nomogram with excellent application value for urologists to assess RSR and make personalized treatment decisions.
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