影像科学与光化学 ›› 2022, Vol. 40 ›› Issue (6): 1628-1632.DOI: 10.7517/issn.1674-0475.220901

• 综述与论文 • 上一篇    

基于CT参数的列线图预测运动员桡骨远端骨折风险

李铁心1, 刘长坯2   

  1. 1. 郑州工业应用技术学院体育学院, 河南 新郑 451150;
    2. 濮阳市中医医院关节及运动医学科, 河南 濮阳 457001
  • 收稿日期:2022-09-19 出版日期:2022-11-23 发布日期:2022-11-15
  • 通讯作者: 李铁心
  • 基金资助:
    湖北省休闲体育发展研究中心2020年度开放基金课题(2020Y024).

Nomogram Based on CT Parameters to Perdict the Risk of Distalradius Fracture in Athletes

LI Tiexin1, LIU Changpi2   

  1. 1. College of Physical Education, Zhengzhou University of Industrial Technology, Xinzheng 451150, Henan, P. R. China;
    2. Department of Joint and Sports Medicine, Chinese Medicine Hospital of Puyang, Puyang 457001, Henan, P. R. China
  • Received:2022-09-19 Online:2022-11-23 Published:2022-11-15

摘要: 本文探讨基于定量CT参数构建体操运动员桡骨远端骨折发生风险。招募135名疑似桡骨远端骨折的前臂外伤体操运动员作为研究对象,根据是否发生桡骨远端骨折分为发生组(n=20)和未发生组(n=115)。统计两组一般资料、定量CT参数[骨折端力学强度指数(BSI)、皮质骨骨密度(cBMD)]及影像特征,Logistic回归方程分析体操运动员骨折桡骨远端骨折发生影响因素,构建列线图预测模型,并验证预测效果。结果发现,运动前准备活动、佩戴防护措施、BSI、cBMD、背侧骨块厚度是体操运动员桡骨远端骨折发生风险的影响因素(P<0.05);列线图模型共纳入5个影响因素,C-index为0.899,Bootstrap自抽样法显示预测值与实际观测值基本一致;ROC曲线显示,列线图预测模型的AUC为0.815(95%CI:0.638~0.863)。由此可见,基于定量CT参数构建体操运动员桡骨远端骨折发生风险的列线图模型具有良好预测效能,有利于指导临床诊治。

关键词: 体操运动员, 桡骨远端骨折, 定量CT参数, 列线图模型

Abstract: This paper explored the risk of distal radius fracture in gymnasts based on quantitative CT parameters. A total of 135 forearm trauma gymnasts with suspected distal radius fractures were recruited as the research objects,and divided into the occurrence group (n=20) and the non-occurrence group (n=115) according to whether the distal radius fracture occurred or not. The general data, quantitative CT parameters [bone scan index (BSI), cortical bone mineral density (cBMD)] and imaging characteristics of the two groups were counted, and the Logistic regression equation was used to analyze the influencing factors of distal radius fractures in gymnasts, and a nomogram was constructed. Predict the model and verify the prediction effect. The results showed that pre-exercise preparation activities, wearing protective measures, BSI, cBMD, and dorsal bone fragment thickness were the influencing factors of the risk of distal radius fractures in gymnasts (P<0.05). A total of 5 influencing factors were included in the nomogram model, and the C-index was 0.899. The Bootstrap self-sampling method showed that the predicted value was basically consistent with the actual observed value; the ROC curve showed that the AUC of the nomogram prediction model was 0.815 (95%CI: 0.638-0.863). It is concluded that the construction of a nomogram model for the risk of distal radius fractures in gymnasts based on quantitative CT parameters has good predictive performance, which is beneficial to guiding clinical diagnosis and treatment.

Key words: gymnast, distal radius fracture, quantitative CT parameters, nomogram model