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

IF 4.9 2区 医学 Q1 UROLOGY & NEPHROLOGY Minerva Urology and Nephrology Pub Date : 2024-08-02 DOI:10.23736/S2724-6051.24.05856-7
Yuanjiong Qi, Shushuai Yang, Jingxian Li, Haonan Xing, Qiang Su, Siyuan Wang, Yue Chen, Shiyong Qi
{"title":"Development and validation of a nomogram to predict impacted ureteral stones via machine learning.","authors":"Yuanjiong Qi, Shushuai Yang, Jingxian Li, Haonan Xing, Qiang Su, Siyuan Wang, Yue Chen, Shiyong Qi","doi":"10.23736/S2724-6051.24.05856-7","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>To develop and evaluate a nomogram for predicting impacted ureteral stones using some simple and easily available clinical features.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":53228,"journal":{"name":"Minerva Urology and Nephrology","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Minerva Urology and Nephrology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.23736/S2724-6051.24.05856-7","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
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
期刊最新文献
Lower PHI, [-2]proPSA/fPSA and testosterone/estradiol ratios in healthy black men: preliminary results and potential implications in prostate cancer clinical management. Transforming UTUC diagnostics: the potential of bladder Epicheck® in clinical practice. Comparative efficacy and safety of intelligent pressure-controlled versus flexible vacuum-assisted ureteral access sheath for 2-4 cm renal calculi. Development and validation of a nomogram to predict impacted ureteral stones via machine learning. Extraperitoneal robot-assisted radical prostatectomy by the da Vinci and Versius System: first comparative analysis.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1