Ahmed Faisal, Al-Kohlany Khaled, Al-Naggar Khalil, Alnadhari Ibrahim, Altam Abdulfattah Yahya, Badheeb Mohamed
Department of Urology, School of Medicine, Ibb University, Ibb, Yemen.
Department of Urology, College of Medicine, Sana'a University, Sana'a, Yemen.
Res Rep Urol. 2025 May 1;17:139-152. doi: 10.2147/RRU.S515846. eCollection 2025.
The lack of reliable predictive tools for outcomes following ureteral lithotripsy (ULT) presents significant challenges in clinical decision-making. This study evaluates the efficacy of the S.T.O.N.E. score-an assessment incorporating Size, Topography, Obstruction, Number, and Hounsfield units (HU)-in predicting the likelihood of achieving a stone-free rate (SFR) in patients undergoing semirigid pneumatic ULT.
This retrospective analysis involved 266 patients with ureteral stones who underwent ULT at IBB University Hospitals from April 2021 to September 2023. The S.T.O.N.E. score was derived from preoperative CT scans, and a nomogram was created to predict SFR failure. Discrimination and calibration were assessed using the area under the receiver operating characteristic curve (AUC) and calibration curve, while decision curve analysis (DCA) evaluated clinical utility.
The cohort's mean age was 47.7 ± 15 years, with a predominance of males (72.2%). The mean S.T.O.N.E. score was 7.8 ± 1.8. The overall SFR of 85.3% and residual stones were detected in 39 patients (14.7%). Multivariate analysis identified higher HU (AOR: 1.01; 95% CI: 1.00-1.01; P < 0.001), proximal stone location (AOR: 15.13; 95% CI: 1.52-51.13; P = 0.020), moderate (AOR: 34.23; 95% CI: 8.28-141.45; P < 0.001) and severe hydronephrosis (AOR: 33.75; 95% CI: 4.55-250.36; P = 0.0006), and larger stone size (AOR: 1.51; 95% CI: 1.30-1.75; P < 0.0001) as significant predictors of SFR failure. The S.T.O.N.E. score effectively predicts SFR failure, with an optimal threshold of > 8 achieving 85.0% accuracy. The model demonstrated 72.0% sensitivity, 81.0% specificity, and strong calibration. DCA indicated clinical utility, differentiating between low- and high-risk patients based on their S.T.O.N.E. scores.
The S.T.O.N.E. score is a valuable tool for predicting post-ULT SFR, aiding preoperative decision-making and potentially improving surgical outcomes by identifying high-risk patients. Further validation in diverse populations is needed to confirm its clinical utility.
输尿管碎石术(ULT)后缺乏可靠的预后预测工具给临床决策带来了重大挑战。本研究评估了S.T.O.N.E.评分(一种综合结石大小、位置、梗阻情况、数量和Hounsfield单位(HU)的评估方法)在预测接受半硬性气压弹道ULT患者实现无石率(SFR)可能性方面的有效性。
这项回顾性分析纳入了2021年4月至2023年9月在IBB大学医院接受ULT的266例输尿管结石患者。S.T.O.N.E.评分来自术前CT扫描,并创建了列线图以预测SFR失败情况。使用受试者工作特征曲线(AUC)下面积和校准曲线评估辨别力和校准情况,而决策曲线分析(DCA)评估临床实用性。
该队列的平均年龄为47.7±15岁,男性占多数(72.2%)。平均S.T.O.N.E.评分为7.8±1.8。总体SFR为85.3%,39例患者(14.7%)检测到残留结石。多因素分析确定较高的HU(优势比[AOR]:1.01;95%置信区间[CI]:1.00 - 1.01;P<0.001)、近端结石位置(AOR:15.13;95%CI:1.52 - 51.13;P = 0.020)、中度(AOR:34.23;95%CI:8.28 - 141.45;P<0.001)和重度肾积水(AOR:33.75;95%CI:4.55 - 250.36;P = 0.0006)以及较大的结石大小(AOR:1.51;95%CI:1.30 - 1.75;P<0.0001)是SFR失败的显著预测因素。S.T.O.N.E.评分能有效预测SFR失败,最佳阈值>8时准确率达85.0%。该模型显示出72.0%的敏感性、81.0%的特异性和良好的校准性。DCA表明该模型具有临床实用性,可根据患者的S.T.O.N.E.评分区分低风险和高风险患者。
S.T.O.N.E.评分是预测ULT后SFR的有价值工具,有助于术前决策,并通过识别高风险患者可能改善手术结果。需要在不同人群中进一步验证以确认其临床实用性。