Qi Yuanjiong, Yang Shushuai, Li Jingxian, Xing Haonan, Su Qiang, Wang Siyuan, Chen Yue, Qi Shiyong
Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.
Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China -
Minerva Urol Nephrol. 2024 Dec;76(6):736-747. doi: 10.23736/S2724-6051.24.05856-7. Epub 2024 Aug 2.
To develop and evaluate a nomogram for predicting impacted ureteral stones using some simple and easily available clinical features.
From June 2019 to July 2022, 480 patients who underwent ureteroscopic lithotripsy (URSL) for ureteral calculi were enrolled in the study. From the eligible study population between June 2019 and December 2020, a training and validation set was randomly generated in a 7:3 ratio. To further evaluate the generalization performance of the nomogram, we performed an additional validation using the data from January 2021 to July 2022. Lasso regression analysis was used to identify the most useful predictive features. Subsequently, a multivariate logistic regression algorithm was applied to select independent predictive features. The predictive performance of the nomogram was assessed using Receiver Operating Characteristic (ROC) curves, calibration curves and decision Curve Analysis (DCA). The Hosmer-Lemeshow Test was utilized to evaluate the overall goodness of fit of the nomogram.
Multivariate logistic regression analysis showed that flank pain, hydronephrosis, stone length/width, HU below (Hounsfield unit density of the ureter center below the stone), HU above/below (HU above divided by HU below) and UWT (ureteral wall thickness) were ascertained as independent predictors of impacted ureteral stones. The nomogram showed outstanding performance within the training dataset, with the area under the curve (AUC) of 0.907. Moreover, the AUC was 0.874 in the validation dataset. The ROC curve, calibration curve, DCA curve and Hosmer-Lemeshow Test suggested that the nomogram maintains excellent clinical applicability and demonstrates commendable performance. Similar results were achieved in the test dataset as well.
We established a nomogram that can be effectively used for preoperative diagnosis of impacted ureteral stones, which is of great significance for the treatment of this disease.
利用一些简单且易于获取的临床特征来开发并评估用于预测嵌顿性输尿管结石的列线图。
2019年6月至2022年7月，480例行输尿管镜碎石术（URSL）治疗输尿管结石的患者纳入本研究。在2019年6月至2020年12月符合条件的研究人群中，按7:3的比例随机生成训练集和验证集。为进一步评估列线图的泛化性能，我们使用2021年1月至2022年7月的数据进行了额外验证。采用Lasso回归分析确定最有用的预测特征。随后，应用多因素逻辑回归算法选择独立预测特征。使用受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估列线图的预测性能。采用Hosmer-Lemeshow检验评估列线图的整体拟合优度。
多因素逻辑回归分析显示，胁腹痛、肾积水、结石长径/短径、结石下方输尿管中心的Hounsfield单位密度（HU下方）、结石上方与下方的HU比值（HU上方/HU下方）以及输尿管壁厚度（UWT）被确定为嵌顿性输尿管结石的独立预测因素。列线图在训练数据集中表现出色，曲线下面积（AUC）为0.907。此外，验证数据集中的AUC为0.874。ROC曲线、校准曲线、DCA曲线和Hosmer-Lemeshow检验表明，列线图具有良好的临床适用性和出色的性能。测试数据集中也得到了类似结果。
我们建立了一种可有效用于嵌顿性输尿管结石术前诊断的列线图，这对该疾病的治疗具有重要意义。
