Prediction model of knee osteoarthritis based on ultrasound score,MRI score,and serum TGF-β1 and Cat D levels
10.12007/j.issn.0258-4646.2025.09.007
- VernacularTitle:基于超声评分、MRI评分以及血清TGF-β1、Cat D水平构建膝关节骨关节炎进展预测模型
- Author:
Zhili WANG
1
;
Danfeng XU
1
;
Nan LI
1
;
Yan JIAO
1
;
Ruisong SHANG
1
Author Information
1. 衡水市人民医院影像中心,河北 衡水 053000
- Publication Type:Journal Article
- Keywords:
ultrasound score;
magnetic resonance imaging;
knee osteoarthritis
- From:
Journal of China Medical University
2025;54(9):802-807
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a prediction model for the progression of knee osteoarthritis(KOA)based on ultrasound score,magnetic resonance imaging(MRI)score,and serum levels of transforming growth factor-β1(TGF-β1)and cathepsin D(Cat D).Methods Clinical data from 270 patients with KOA in Hengshui People's hospital from December 2022 to June 2024 were retrospec-tively analyzed.The patients were randomly divided into a modeling set(n=189)and validation set(n=81)at a ratio of 7∶3.The patients in the modeling set were categorized into mild-to-moderate and severe groups based on the degree of disease progression.Mul-tivariate logistic regression analysis was used to identify factors influencing KOA progression,and a prediction model was constructed using R software.Results Multivariate logistic regression analysis showed that body mass index,knee injury history,ultrasound score,WORMS score,TGF-β1,and Cat D were significant predictors of KOA progression(P＜0.05).A nomogram-based prediction model was developed using these variables.The areas under the curve(AUC)of the nomograms for predicting disease progression in the modeling and validation sets were 0.889 and 0.860,respectively.The calibration curves showed that the predicted probability was in good agreement with the actual probability.Conclusion The prediction model developed in this study is effective in identifying patients at high-risk of KOA progression and may servce as a valuable tool for clinical assessment and decision making.
