Machine learning models established to distinguish OA and RA based on immune factors in the knee joint fluid.
- Author:
Qin LIANG
1
,
2
;
Lingzhi ZHAO
3
;
Yan LU
4
;
Rui ZHANG
5
;
Qiaolin YANG
3
;
Hui FU
3
;
Haiping LIU
5
;
Lei ZHANG
5
;
Guoduo LI
3
Author Information
1. Laboratory Medicine Center, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou 730050, China. *Corresponding author, E-mail: gslzliangq@
2. com.
3. Laboratory Medicine Center, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou 730050, China.
4. Laboratory Medicine Center, Gansu Provincial Hospital, Lanzhou 730000, China.
5. Department of Orthopedic Joint, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou 730050, China.
- Publication Type:Journal Article
- MeSH:
Humans;
Arthritis, Rheumatoid/metabolism*;
Machine Learning;
Male;
Female;
Middle Aged;
Aged;
Synovial Fluid/immunology*;
Osteoarthritis, Knee/metabolism*;
Knee Joint/metabolism*;
ROC Curve;
Diagnosis, Differential
- From:
Chinese Journal of Cellular and Molecular Immunology
2025;41(4):331-338
- CountryChina
- Language:Chinese
-
Abstract:
Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Methods 100 OA and 40 RA patients scheduled for total knee arthroplasty were enrolled respectively. Each patient's knee joint fluid was collected preoperatively. Nucleated cells were counted and classified. The expression levels of immune factors, including tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, IL-8, IL-15, matrix metalloproteinase 3 (MMP3), MMP9, MMP13, rheumatoid factor (RF), serum amyloid A (SAA), C-reactive protein (CRP), and others were measured. Smears and microscopic classification of all the immune factors were performed. Independent influencing factors for OA or RA were identified using univariate binary logistic regression, Lasso regression, and multivariate binary logistic regression. Based on the independent influencing factors, three machine learning models were constructed which are logistic regression, random forest, and support vector machine. Receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to evaluate and compare the models. Results A total of 5 indicators in the knee joint fluid were screened out to distinguish OA and RA, which were IL-1β(odds ratio(OR)=10.512, 95× confidence interval (95×CI) was 1.048-105.42, P=0.045), IL-6 (OR=1.007, 95×CI was 1.001-1.014, P=0.022), MMP9 (OR=3.202, 95×CI was 1.235-8.305, P=0.017), MMP13 (OR=1.002, 95× CI was 1-1.004, P=0.049), and RF (OR=1.091, 95×CI was 1.01-1.179, P=0.026). According to the results of ROC, calibration curve and DCA, the accuracy (0.979), sensitivity (0.98) and area under the curve (AUC, 0.996, 95×CI was 0.991-1) of the random forest model were the highest. It has good validity and feasibility, and its distinguishing ability is better than the other two models. Conclusion The machine learning model based on immune factors in the knee joint fluid holds significant value in distinguishing OA and RA. It provides an important reference for the clinical early differential diagnosis, prevention and treatment of OA and RA.
