Construction and verification of atherosclerosis risk prediction model for rheumatoid arthritis patients
- VernacularTitle:类风湿关节炎患者动脉粥样硬化风险预测模型的构建与验证
- Author:
Jing LYU
1
;
Fangying ZHU
1
;
Kai ZHU
1
;
Yun LI
1
;
Na YANG
1
;
Shuyun WEN
1
;
Miqian ZHONG
1
Author Information
- Publication Type:Journal Article
- Keywords: arthritis,rheumatoid; atherosclerosis; risk factors; prediction model; Lasso Logistic regression
- From: Tianjin Medical Journal 2025;53(10):1043-1047
- CountryChina
- Language:Chinese
- Abstract: Objective To construct a risk prediction model for atherosclerosis(AS)in patients with rheumatoid arthritis(RA)based on Lasso-Logistic regression analysis and provide a scientific basis for individualized clinical intervention.Methods The retrospective clinical data were collected from 344 RA patients,including 86 patients with AS(RA+AS group)and 258 patients with without AS(RA group).The clinical characteristics and initial laboratory test results were compared between the two groups.Lasso regression was used to screen the key predictive variables,and Logistic regression was combined to construct the prediction mode.The discrimination of the model was evaluated through the receiver operating characteristic(ROC)curve and the area under the curve(AUC).The Hosmer-Lemeshow test was used to assess the calibration,and decision curve analysis was used to verify the clinical applicability of the model.Results Seven predictive variables were identified including RA disease duration,DAS28 score,C-reactive protein(CRP),triglycerides(TG),high-density lipoprotein cholesterol(HDL-C),fasting blood glucose(FBG)and hypertension.The risk prediction model for AS in RA patients was:Logit(P)=-2.674+0.605×RA disease duration+0.393×DAS28 score+0.310×CRP+1.346×TG-2.289×HDL-C+0.679×FBG+0.711×hypertension.The AUC of the model was 0.965(95%CI:0.943-0.987),and the Hosmer-Lemeshow test showed χ2=0.547,P=1.000,indicating good discrimination and calibration.Clinical decision curve analysis showed that the probability threshold ranged from 7%to 92%,demonstrating high clinical applicability.Conclusion The AS risk prediction model constructed in this study for RA patients can effectively identify high-risk individuals,supporting the development of personalized prevention and treatment strategies.
