Urolithiasis最新文献

This study aimed to compare the predictive performance of traditional stone scoring systems with a large language model based on ChatGPT in estimating stone-free rates following percutaneous nephrolithotomy. A total of 340 patients who underwent the procedure between 2019 and 2025 were retrospectively analyzed. Preoperative stone complexity was evaluated using four established scoring systems-Guy's Stone Score, the CROES nomogram, the S.T.O.N.E. nephrolithometry score, and the Seoul National University Renal Stone Complexity score-and each case was additionally processed through a ChatGPT-based prediction model. The predicted outcomes of each method were compared with actual postoperative results using correlation analysis and multivariate regression. The overall stone-free rate was 60.9%. Patients who achieved stone-free status had significantly lower Guy's Stone Score, S.T.O.N.E., and S-ReSC values than those with residual stones (all p < 0.001). In contrast, neither the CROES nomogram (p = 0.19) nor the ChatGPT-based predicted stone-free probability (p = 0.549) differed significantly between the two groups. Univariate analysis revealed that higher values in Guy's Stone Score, S.T.O.N.E., and S-ReSC scores were associated with stone-free failure. Multivariate analysis identified Guy's Stone Score and S.T.O.N.E. score as independent predictors of surgical success. In contrast, the ChatGPT-based model showed limited predictive performance and failed to provide reliable estimates for stone-free rates in our study. These findings support the continued clinical utility of conventional scoring systems while emphasizing the need for further development and validation of artificial intelligence models. Large language models must be trained on structured clinical datasets and externally validated before their integration into surgical decision-making processes in endourology.

To quantitatively evaluate high-grade encrustation in double-J (DJ) stents and identify its risk factors in patients undergoing ureteroscopy and laser lithotripsy. This retrospective study analyzed 56 encrusted DJ stents from patients post-ureteroscopy and laser lithotripsy. Encrustation severity was classified using the FECal (Forgotten, Encrusted, Calcified) grading system. Clinical, laboratory, and stent characteristic data were collected. Composition and extent of encrustation were assessed via inductively coupled plasma mass spectrometry (ICP-MS) and scanning electron microscopy (SEM). Logistic regression analysis was performed to identify risk factors for high-grade encrustation. Among the 56 encrusted stents, FECal grades were: I (n = 23), II (n = 12), III (n = 18), IV (n = 1), and V (n = 2). High-grade encrustation (Grades III-V) was significantly associated with female gender (66.7% vs. 34.3%, p = 0.019), urological stone history (33.3% vs. 5.7%, p = 0.010), and indwelling time > 4 weeks (52.4% vs. 25.7%, p = 0.044). Multivariate analysis confirmed indwelling time > 4 weeks as an independent risk factor (OR = 3.9, p = 0.049). Calcium oxalate was the main component, with encrustation initiating on black-ring areas and near side holes and gradually extending to cross-sectional regions. The FECal grading system effectively reflects encrustation severity. Indwelling time > 4 weeks is a key predictor of high-grade encrustation. Side holes and the black marker ring are high-risk areas for mineral deposition. Developing stents with improved biocompatibility and anti-encrustation coatings could mitigate encrustation risks.