Novel scoring system for predicting stone-free rate after flexible ureteroscopy lithotripsy.

This study aims to investigate the factors affecting the stone-free rate (SFR) of flexible ureteroscopy and laser lithotripsy (fURSL) for renal stones and establish predictive models by identifying their prognostic factors. We retrospectively examined 252 patients with renal stones who were treated with fURSL between July 2020 and April 2022. We analyzed the relationship between the patient's clinical data (sex, age, and body mass index), stone status (side, size, location, stone/transverse process pixel ratio [STPR], and the CT value of stone [SCTV]), and SFR to determine the relevant factors and analyze their influence. Additionally, a nomogram was constructed based on these prediction results. A total of 252 patients were enrolled based on the inclusion and exclusion criteria. They were reviewed 7, 30, and 90 days postoperatively, with 46, 23, and 10 patients failing to discharge stones, respectively. Univariate Cox proportional hazard regression results revealed that the SFR was correlated with stone location, diameter (D1, diameter of stone measured by computed tomography [CT]; D2, diameter of stone measured by kidney-ureter-bladder abdominal radiography), SCTV, STPR, and operation time. Multivariate Cox proportional hazard regression was used to develop 2 predictive models for the SFR. The influencing factors of model 1 included D1, location, and SCTV, whereas those of model 2 were D2, location, and STPR. The results are shown in the nomogram. Receiver operating characteristic curves showed no significant difference between models 1 and 2 (P = .498), indicating that the nomogram was highly predictive. After 1000 resamples and internal self-validation, the C-indices of models 1 and 2 were 0.924 and 0.895, respectively, showing that the stone clearance predicted by the nomogram matched the actual situation. Stone location, size, and density (SCTV and STPR) were significant predictors of SFR after fURSL. The scoring system based on these factors may be used to guide optimal treatment strategy selection.