A simple predictive model with internal validation for assessment of stone-left after ureteroscopic lithotripsy in upper ureteral stones

BACKGROUND: Stone free rate in upper ureteral stones is not as high. We sought to identify easily accessible risk factors attributing to stones left in the ureteroscopy in the treatment of upper ureteral calculi, and to build a simple and reliable predictive model. METHODS: Patients treating only for upper ureteral stones in 2018 were retrospectively analyzed. Correlations between factors and the stone free rate were analyzed using bidirectional stepwise regression, curve fitting and binary logistic regression. Stone shape was judged by the gap between length and width in the two-dimensional section. A predictive nomogram model was built based on those selected variables (P<0.05). The area under the receiver operator characteristic curve (AUC) and calibration curve were used to access its discrimination and calibration. Decision curve analysis (DCA) was conducted to test the clinical usefulness. RESULTS: Totally, 275 patients with 284 stones were enrolled in this research. Bidirectional stepwise regression showed that stone length had a significant effect on stone free, instead of width or burden. Stone shapes were also found playing a big role. Curve fitting showed that quasi-circular stones had a high risk of retropulsion, and eventually led to stone left. Finally, stone length, shape, modality, and the distance of stones to the ureteropelvic junction were enrolled in the model. Among them, the distance of the stone to the ureteropelvic junction showed a noticeable impact on stone left. AUC was 0.803 (95% CI: 0.730–0.876), and the calibration curve showed good calibration of the model (concordance index, 0.792). DCA indicated the model added net benefit to patients. CONCLUSIONS: The present predictive model based on those factors, stones length, shape, modality, and distance of the stone to the ureteropelvic junction was easy, reliable and useful.