黄丽, 彭欢欢, 石春红, 王维箭, 欧永强. 经右侧桡动脉入路行冠状动脉造影术失败风险列线图模型构建与验证[J]. 蚌埠医科大学学报, 2023, 48(11): 1552-1556. DOI: 10.13898/j.cnki.issn.1000-2200.2023.11.016
    引用本文: 黄丽, 彭欢欢, 石春红, 王维箭, 欧永强. 经右侧桡动脉入路行冠状动脉造影术失败风险列线图模型构建与验证[J]. 蚌埠医科大学学报, 2023, 48(11): 1552-1556. DOI: 10.13898/j.cnki.issn.1000-2200.2023.11.016
    HUANG Li, PENG Huan-huan, SHI Chun-hong, WANG Wei-jian, OU Yong-qiang. Construction and verification of the failure risk nomogram model of coronary angiography via right radial approach[J]. Journal of Bengbu Medical University, 2023, 48(11): 1552-1556. DOI: 10.13898/j.cnki.issn.1000-2200.2023.11.016
    Citation: HUANG Li, PENG Huan-huan, SHI Chun-hong, WANG Wei-jian, OU Yong-qiang. Construction and verification of the failure risk nomogram model of coronary angiography via right radial approach[J]. Journal of Bengbu Medical University, 2023, 48(11): 1552-1556. DOI: 10.13898/j.cnki.issn.1000-2200.2023.11.016

    经右侧桡动脉入路行冠状动脉造影术失败风险列线图模型构建与验证

    Construction and verification of the failure risk nomogram model of coronary angiography via right radial approach

    • 摘要:
      目的探讨经右侧桡动脉入路行冠状动脉造影术(CAG)失败的风险因素,并建立风险列线图模型。
      方法采用回顾性病例对照研究的方法选取住院且经右侧桡动脉行CAG失败病人92例作为失败组,以1∶3的比例选取同期住院且经右侧桡动脉行CAG成功病人276例作为对照组。收集2组病人的临床资料。采用单因素和多因素logistic回归分析经右侧桡动脉入路行CAG失败的可能危险因素,将危险因素纳入R软件建立经右侧桡动脉入路行CAG失败的列线图预测模型并进行验证。
      结果多因素logistic回归分析结果显示,女性、吸烟、桡动脉痉挛、桡动脉直径/鞘管外径比值≤1、术者操作水平每年 < 300例手术者、LDL-C是经右侧桡动脉入路行CAG失败的独立危险因素(P < 0.01);将多因素分析结果进行可视化处理,构建经右侧桡动脉入路行CAG失败的风险列线图模型;在RStudio软件中对所构建模型进行验证,ROC曲线结果显示,该模型在预测经右侧桡动脉入路行CAG失败风险的曲线面积为0.816(95%CI:0.764~0.868),灵敏度为0.804,特异度为0.710;校准曲线分析结果显示,列线图预测模型的预测概率与实际发生概率之间具有良好的一致性,校准曲线的平均绝对误差为0.012;Hosmer-Lemeshow检验提示模型不存在过度拟合现象(χ2=10.18,P>0.05)。
      结论女性、吸烟、桡动脉痉挛、桡动脉直径/鞘管外径≤1、术者操作水平每年 < 300例手术者、LDL-C是经右侧桡动脉入路行CAG失败的独立危险因素,基于上述危险因素建立经右侧桡动脉入路行CAG失败的风险列线图预测模型,具有良好的区分度、校准度及预测价值。

       

      Abstract:
      ObjectiveTo investigate the risk factors of failure of coronary angiography(CAG) via right radial approach, and establish a risk nomogram model.
      MethodsNinety-two hospitalized patients with CAG failure via the right radial approach were selected as the failure group using the retrospective case-control study, and 276 hospitalized patients with CAG success via the right radial approach were selected as the control group in a ratio of 1:3.The clinical data of two groups were collected.The possible risk factors of CAG failure via right radial approach were analyzed by univariate and multivariate logistic regression, and the risk factors were incorporated into R software to establish and verify the nomogram prediction model of CAG failure via right radial approach.
      ResultsThe results of multivariate logistic regression analysis showed that the female, smoking, radial artery spasm, ratio of radial artery diameter to sheath diameter ≤ 1, operative level < 300 patients per year and LDL-C were the independent risk factors of CAG via right radial approach (P < 0.01).The results of multi-factor analysis were visualized, the risk nomogram model of CAG failure via right radial artery approach was constructed, and the model was validated using RStudio software.The results of ROC curve showed that the area under the curve of model in predicting the risk of CAG failure via right radial approach was 0.816(95%CI: 0.764-0.868), the sensitivity was 0.804, and the specificity was 0.710.The results of calibration curve analysis show that the prediction probability of nomogram prediction model was good agree with the actual occurrence probability, and the mean absolute error of calibration curve was 0.012.Hosmer-Lemeshow test showed that there was no overfitting phenomenon in the model (χ2=10.18, P>0.05).
      ConclusionsThe female, smoking, radial artery spasm, radial artery diameter/sheath diameter ≤ 1, operator's operating level < 300 patients per year and LDL-C are independent risk factors of CAG failure via right radial approach.Based on the above risk factors, the risk nomogram prediction model of CAG failure via right radial approach is established, which has good differentiation, calibration and prediction value.

       

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