Cross-sectional stroke risk identification in RA: integrating traditional and disease-specific factors
Authors' conclusion
Integration of RA-specific factors significantly improved stroke risk identification, demonstrating superior discrimination over traditional cardiovascular risk assessment tools.
Study context
- Population
- NHANES 2011–2020 data and a Beijing Tiantan Hospital RA cohortNHANES 2011-2020 data 1,366 · a Beijing Tiantan Hospital RA cohort 774
- Exposure
- Enhanced model (traditional + RA-specific factors) vs basic model, external validation model and Framingham risk score
- Outcome
- Stroke risk identification (discrimination, AUC)
Methodological detailstwo-step modelling approach, LASSO regression, external validation
- Two-step modelling approach
- LASSO regression
- External validation
Main finding
As reported in the abstract
Stroke risk identification, AUC
Likelihood ratio test χ2
137.26
P < 0.001
Enhanced model vs External validation model
DeLong test Z
−5.751
P < 0.001
Enhanced model vs External validation model
DeLong Z
8.210
P < 0.001
Enhanced model vs Framingham risk score
Basic model
- AUC
- 0.712
- 95% CI
- 0.665-0.759
External validation model
- AUC
- 0.716
- 95% CI
- 0.674-0.758
Enhanced model
- AUC
- 0.829
- 95% CI
- 0.794-0.864
- Sensitivity
- 70.0%
- Specificity
- 81.6%
- Accuracy
- 78.9%
Framingham risk score
0.611
AUC
Observational data: associations, not effects.
Further findings
Traditional stroke risk factors identified
- Traditional risk factors identified; strongest associations: neutrophil count, hypertension and coronary heart disease
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Original publication
Cross-sectional stroke risk identification in RA: integrating traditional and disease-specific factors. Rheumatology (Oxford). Published 31 August 2026. doi:10.
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