基于体表大数据构建中青年下肢冠状面力线不良预测模型

左席珍; 刘力铭; 雷凯; 杨鹏飞; 傅德杰; 郭林

doi:10.16016/j.2097-0927.202312116

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基于体表大数据构建中青年下肢冠状面力线不良预测模型

临床医学 | 更新时间：2026-01-09

- 基于体表大数据构建中青年下肢冠状面力线不良预测模型
- A prediction model for coronal malalignment of the lower extremity in middle-aged and young people based on body surface big data
- 陆军军医大学学报 2024年46卷第8期页码：868-877
- 作者机构：
  
  1.400038 重庆，陆军军医大学(第三军医大学)第一附属医院：运动医学中心
  2.400038 重庆，陆军军医大学(第三军医大学)第一附属医院：创伤关节外科
- 作者简介：
  
  [郭林, E-mail: guolin6212@163.comGUO Lin, E-mail: guolin6212@163.com
- 基金信息：
  
  重庆市科卫联合医学科研项目(2022MSXM007)
- DOI：10.16016/j.2097-0927.202312116
  中图分类号： R195.4;R319;R658.3
- 收稿：2023-12-06，
  
  修回：2024-01-18，
  
  纸质出版：2024-04-30
- 稿件说明：
移动端阅览
左席珍, 刘力铭, 雷凯, 等. 基于体表大数据构建中青年下肢冠状面力线不良预测模型[J]. 陆军军医大学学报, 2024,46(8):868-877.

Xizhen ZUO, Liming LIU, Kai LEI, et al. A prediction model for coronal malalignment of the lower extremity in middle-aged and young people based on body surface big data[J]. Journal of Army Medical University, 2024, 46(8): 868-877.
左席珍, 刘力铭, 雷凯, 等. 基于体表大数据构建中青年下肢冠状面力线不良预测模型[J]. 陆军军医大学学报, 2024,46(8):868-877. DOI： 10.16016/j.2097-0927.202312116.

Xizhen ZUO, Liming LIU, Kai LEI, et al. A prediction model for coronal malalignment of the lower extremity in middle-aged and young people based on body surface big data[J]. Journal of Army Medical University, 2024, 46(8): 868-877. DOI： 10.16016/j.2097-0927.202312116.

摘要

目的

基于体表大数据构建我国中青年下肢冠状面力线不良预测模型，为临床工作提供更加快速、准确的下肢冠状面力不良预测方法。

方法

采用横断面研究设计方案，筛选陆军军医大学第一附属医院运动医学中心2022年5月至2023年12月收治的915例膝关节半月板撕裂患者的病历资料，测量其下肢冠状面力线(简称下肢力线)。按照下肢力线分级标准将915例患者分中立位下肢力线及下肢力线不良两组，按照7∶3随机分为训练集和验证集。分析训练集性别、年龄及体表大数据(包括BMI、下肢长、双膝距、双踝距、皮下脂肪厚度)的7项指标用于预测下肢力线不良的价值，建立Logistic回归模型并使用列线图对模型进行可视化，使用校准曲线、ROC曲线和DCA曲线评估模型对下肢力线不良的诊断效能。

结果

训练集640例，其中男性299例，女性341例，中位年龄41.5岁；验证集275例，其中男性128例，女性147例，中位年龄41.0岁。训练集中中立位下肢力线和下肢力线不良两组间的性别、年龄、BMI等7项指标之间的差异均有统计学意义(

0.01)。多因素Logistic回归构建了含BMI[(24.31±3.58) kg/m

；

=1.12，95%

：1.06~1.19，

0.001

]

、下肢长[82.00(78.00~87.00)cm；

=0.95，95%

：0.92~0.98，

=0.002

]

、双膝距[30.00(16.00~45.25)mm；

=1.06，95%

：1.05~1.07，

0.001

]

、双踝距[23.00(18.00~30.00)mm；

=0.98，95%

：0.96~1.00，

=0.078

]

、性别[男性299(46.72%)；

=0.70，95%

：0.46~1.06，

=0.089

]

的预测模型，模型预测下肢力线不良的受试者操作曲线下面积(area under the subject curve，AUC)在训练集和验证集中分别为0.808、0.770。

结论

基于体表大数据，初步构建我国中青年下肢力线不良预测模型，其对于下肢力线不良的诊断效能较高。

Abstract

Objective

To construct a prediction model for coronal malalignment of lower limb in middle-aged and young people in China based on body surface big data in order to provide a faster and more accurate tool for predicting the malalignment in clinical practice.

Methods

A cross-sectional trial was adopted on 915 patients with knee meniscus tears admitted to the Sports Medical Center of our hospital from May 2022 to December 2023. The coronal force line of lower limb was measured

and according to the lower limb force line grading standards

the patients were divided into neutral force line group and malalignment lower limb group

and assigned randomly into training set and validation set in a ratio of 7∶3. Seven indicators

such as gender

age

and body surface big data (including BMI

lower limb length

distance between both knee joints

distance between both ankle joints

and subcutaneous fat thickness) were used to analyze the training set to predict the value of malalignment force line. Logistic regression model and nomogram model were constructed to visualize our prediction model. Then calibration curves

receiver operating characteristic (ROC) curve

and decision curve analysis (DCA) were applied to evaluate the diagnostic efficacy of the constructed model.

Results

In the training set of 640 cases

there were 299 males and 341 females

with a median age of 41.5 years old

and for the validation set

there are 275 patients

including 128 males and 147 females

with a median age of 41.0 years old. Significant differences were observed in above mentioned 7 indicators between the 2 groups in the training set (

0.01). Based on the results of multiple logistic regression analysis

a prediction model for malalignment of lower limb was constructed

including BMI (24.31±3.58 kg/m

=1.12

95%

: 1.06~1.19

0.001)

lower limb length [82.00 (78.00~87.00) cm

=0.95

95%

: 0.92~0.98

=0.002

]

distance between both knee joints [30.00 (16.00~45.25) cm

=1.06

95%

: 1.05~1.07

0.001

]

distance between both ankle joint [23.00 (8.00~30.00) mm

=0.98

95%

: 0.96~1.00

=0.078

]

and gender [man 299 (46.72%)

=0.70

95%

: 0.46~1.06

=0.089

]

. The area under the subject curve (AUC) value of our constructed model for predicting malalignment of lower limb was 0.808 and 0.770

respectively

in the training and validation sets.

Conclusion

Based on body surface big data

we primarily construct a prediction model for malalignment of lower limb for middle-aged and young people in China

which shows a good diagnostic performance on malalignment of lower limb.

关键词

Keywords

references

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