A prediction model for coronal malalignment of the lower extremity in middle-aged and young people based on body surface big data
10.16016/j.2097-0927.202312116
- VernacularTitle: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
- Author:
Xizhen ZUO
1
;
Liming LIU
;
Kai LEI
;
Pengfei YANG
;
Dejie FU
;
Lin GUO
Author Information
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
- Keywords:
malalignment of lower limb;
body surface big data;
prediction model;
logistic regression
- From:
Journal of Army Medical University
2024;46(8):868-877
- CountryChina
- Language:Chinese
-
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 (P<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/m2,OR=1 .12,95％CI:1 .06～1 .19,P<0.001 ),lower limb length[82.00 (78.00～87.00)cm,OR=0.95,95％CI:0.92～0.98,P=0.002],distance between both knee joints[30.00 (16.00～45.25)cm,OR=1 .06,95％CI:1 .05～1 .07,P<0.001],distance between both ankle joint[23.00 (8.00～30.00)mm,OR=0.98,95％CI:0.96～1 .00,P=0.078]and gender[man 299 (46.72％),OR=0.70,95％CI:0.46～1 .06,P=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.
