Cross-sectional stroke risk identification in rheumatoid arthritis: integrating traditional and disease-specific factors.

OBJECTIVES: Patients with rheumatoid arthritis (RA) face significantly elevated stroke risk, yet existing cardiovascular risk assessment tools perform poorly in this population. This study aimed to develop and validate a cross-sectional stroke risk identification model integrating traditional and RA-specific clinical characteristics. METHODS: A two-step modeling approach was employed using NHANES 2011-2020 data (n = 1,366) and a Beijing Tiantan Hospital RA cohort (n = 774). LASSO regression identified traditional stroke risk factors to establish a basic model, which was then externally validated in the RA cohort. RA-specific factors DAS28-CRP score, anti-CCP antibody, rheumatoid factor, methotrexate use, and disease duration, were incorporated to develop an enhanced model. RESULTS: Eleven traditional risk factors were identified, with neutrophil count, hypertension, and coronary heart disease showing the strongest associations. The basic model achieved an AUC of 0.712 (95%CI: 0.665-0.759), with external validation AUC of 0.716 (95%CI: 0.674-0.758). After incorporating five RA-specific indicators, the enhanced model AUC improved to 0.829 (95%CI: 0.794-0.864, P < 0.001), with sensitivity 70.0%, specificity 81.6%, and accuracy 78.9%. Both likelihood ratio test (χ²=137.26, P < 0.001) and DeLong test (Z = -5.751, P < 0.001) confirmed superiority over the external validation model. The enhanced model also outperformed the Framingham Risk Score (AUC 0.611; DeLong Z = 8.210, P < 0.001). CONCLUSION: Integration of RA-specific factors significantly improved stroke risk identification, demonstrating superior discrimination over traditional cardiovascular risk assessment tools. This model provides a practical tool for stroke risk stratification and identification of high-risk individuals among RA patients, warranting further prospective validation.