Development and validation of a nomogram to predict impacted ureteral stones via machine learning.

IF 4.9 2区 医学 Q1 UROLOGY & NEPHROLOGY
Yuanjiong Qi, Shushuai Yang, Jingxian Li, Haonan Xing, Qiang Su, Siyuan Wang, Yue Chen, Shiyong Qi
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引用次数: 0

Abstract

Background: To develop and evaluate a nomogram for predicting impacted ureteral stones using some simple and easily available clinical features.

Methods: 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.

Results: 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.

Conclusions: 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.

开发并验证通过机器学习预测输尿管结石的提名图。
背景:利用一些简单易得的临床特征,开发一种预测输尿管结石的提名图:利用一些简单易得的临床特征,开发并评估用于预测输尿管结石的提名图:2019年6月至2022年7月,480名因输尿管结石而接受输尿管镜碎石术(URSL)的患者被纳入研究。在 2019 年 6 月至 2020 年 12 月期间,从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。为进一步评估提名图的泛化性能,我们使用 2021 年 1 月至 2022 年 7 月的数据进行了额外验证。我们使用拉索回归分析来确定最有用的预测特征。随后,我们采用多元逻辑回归算法来选择独立的预测特征。使用接收者操作特征曲线(ROC)、校准曲线和决策曲线分析(DCA)评估了提名图的预测性能。霍斯默-勒梅绍检验(Hosmer-Lemeshow Test)用于评估提名图的总体拟合优度:多变量逻辑回归分析表明,侧腹疼痛、肾积水、结石长度/宽度、下方 HU(结石下方输尿管中心的 Hounsfield 单位密度)、上方 HU/下方 HU(上方 HU 除以下方 HU)和 UWT(输尿管壁厚度)是影响输尿管结石的独立预测因素。在训练数据集中,提名图显示出卓越的性能,曲线下面积(AUC)为 0.907。此外,在验证数据集中,曲线下面积(AUC)为 0.874。ROC曲线、校准曲线、DCA曲线和Hosmer-Lemeshow检验表明,提名图具有良好的临床适用性和值得称道的性能。测试数据集也取得了类似的结果:我们建立的提名图可有效用于冲击性输尿管结石的术前诊断,对该疾病的治疗具有重要意义。
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来源期刊
Minerva Urology and Nephrology
Minerva Urology and Nephrology UROLOGY & NEPHROLOGY-
CiteScore
8.50
自引率
32.70%
发文量
237
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