实用医学杂志 ›› 2025, Vol. 41 ›› Issue (13): 1979-1986.doi: 10.3969/j.issn.1006-5725.2025.13.006

• 专题报道:肾病 • 上一篇    

输尿管软镜治疗肾下盏结石术后清石率的预测模型构建及验证

廖黄峻清1,曹家栋2,王志超2,张秋红2,周建甫2,向松涛2()   

  1. 1.广州中医药大学第二临床医学院 (广东 广州 510405 )
    2.广东省中医院泌尿外科 (广东 广州 510370 )
  • 收稿日期:2025-03-19 出版日期:2025-07-10 发布日期:2025-07-18
  • 通讯作者: 向松涛 E-mail:tonyxst@163.com
  • 基金资助:
    广东省中医药局科研项目(20232030);广东省中医院院内专项(YN2023QN01)

Establishment and validation of a predictive model for the stone⁃free rateafter flexible ureteroscopy for lower pole calculi

Huangjunqing LIAO1,Jiadong CAO2,Zhichao WANG2,Qiuhong ZHANG2,Jianfu ZHOU2,Songtao XIANG2()   

  1. The Second Clinical Medical College of Guangzhou University of Chinese Medicine,Guangzhou 510405,Guangdong,China
  • Received:2025-03-19 Online:2025-07-10 Published:2025-07-18
  • Contact: Songtao XIANG E-mail:tonyxst@163.com

摘要:

目的 探究影响输尿管软镜(flexible ureteroscopy, FURL)治疗肾下盏结石术后结石清除率的危险因素并建立预测模型,并对模型进行验证和评估。 方法 回顾性分析2020—2024年1月间于广东省中医院行输尿管软镜治疗的154例肾下盏结石患者,根据术后结石清除情况分为结石清除组和结石残留组。采用单因素分析筛选危险因素,通过Pearson相关系数和方差膨胀因子(VIF)评估变量间共线性,筛选AUC最优的指标;结合多因素logistic回归确定独立预测因素,构建列线图模型,并用bootstrap法进行内部验证。采用受试者工作特征曲线(ROC曲线)、临床决策曲线(DCA)评估预测模型的预测价值和临床应用价值。 结果 单因素分析显示,最大结石直径、累计结石直径、结石体积、结石表面积、平均结石密度及肾盂漏斗角与肾下盏结石输尿管软镜术后结石清除率显著相关(P < 0.05)。共线性分析后选择最大结石直径(AUC = 0.724)作为结石负荷指标。多因素回归确定最大结石直径、平均结石密度及肾盂漏斗角为独立危险因素。列线图模型曲线下面积(AUC)为0.786,敏感度为79.6%,特异度为71.0%,内部验证AUC为0.792,DCA显示阈值概率为4% ~ 75%时,模型具临床净收益。 结论 本研究构建的列线图模型整合结石特征与肾脏解剖参数,可有效预测输尿管软镜治疗肾下盏结石的结石清除率(SFR),其预测效能稳定且临床适用性高,为术前个性化决策提供了可靠工具。

关键词: 肾下盏结石, 输尿管软镜, 结石清除率, 预测模型, logistic回归

Abstract:

Objective To explore the risk factors of the stone-free rate after flexible ureteroscopy for lower pole calculi, develop a predictive model based on these identified factors, with subsequent validation and evaluation of the established model. Methods A retrospective analysis was conducted on 154 patients with lower pole renal calculi who underwent flexible ureteroscopy (FURS) at Guangdong Provincial Hospital of Traditional Chinese Medicine from 2020 to 2024. Based on postoperative stone clearance status, patients were categorized into a stone-free group and a residual stone group. Univariate analysis was performed to screen potential risk factors, while Pearson correlation coefficients and variance inflation factor (VIF) were employed to assess multicollinearity, retaining the indicator with the highest area under the curve (AUC). Independent predictors were identified through multivariate logistic regression analysis, and a nomogram model was constructed. Internal validation was conducd using the bootstrap resampling method. The predictive performance and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Results Univariate analysis demonstrated that largest stone diameter (LSD), cumulative stone diameter (CSD), stone volume (SV), stone surface area (SA), mean stone density (MSDE), and infundibular pelvic angle (IPA) were significantly associated with stone-free rate (SFR) following flexible ureteroscopy (FURS) for lower pole calculi (all P < 0.05). After collinearity diagnostics, LSD (area under the curve [AUC]= 0.724) was retained as the optimal stone burden parameter. Multivariate logistic regression analysis confirmed LSD, MSDE, and IPA as independent predictors of postoperative SFR. The nomogram model exhibited robust predictive performance with an AUC of 0.786 (sensitivity: 79.6%, specificity: 71.0%). Internal validation via 1000 bootstrap resamples yielded an AUC of 0.792, and decision curve analysis (DCA) confirmed clinical utility with net benefit across threshold probabilities of 4% — 75%. Conclusions The nomogram model developed in this study, which incorporates both stone characteristics and renal anatomical parameters, demonstrates effective prediction of stone-free rate (SFR) following flexible ureteroscopy for lower pole calyceal stones. With stable predictive performance and high clinical applicability, this model provides a reliable tool for preoperative personalized decision-making in endourological management.

Key words: lower pole calculi, flexible ureteroscopy, stone-free rate, predictive model, logistic regression

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