Derivation and internal validation of a multi-biomarker-based cardiovascular disease risk prediction score for rheumatoid arthritis patients
Open Access
- 4 December 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in Arthritis Research & Therapy
- Vol. 22 (1), 1-16
- https://doi.org/10.1186/s13075-020-02355-0
Abstract
Rheumatoid arthritis (RA) patients have increased risk for cardiovascular disease (CVD). Accurate CVD risk prediction could improve care for RA patients. Our goal is to develop and validate a biomarker-based model for predicting CVD risk in RA patients. Medicare claims data were linked to multi-biomarker disease activity (MBDA) test results to create an RA patient cohort with age ≥ 40 years that was split 2:1 for training and internal validation. Clinical and RA-related variables, MBDA score, and its 12 biomarkers were evaluated as predictors of a composite CVD outcome: myocardial infarction (MI), stroke, or fatal CVD within 3 years. Model building used Cox proportional hazard regression with backward elimination. The final MBDA-based CVD risk score was internally validated and compared to four clinical CVD risk prediction models. 30,751 RA patients (904 CVD events) were analyzed. Covariates in the final MBDA-based CVD risk score were age, diabetes, hypertension, tobacco use, history of CVD (excluding MI/stroke), MBDA score, leptin, MMP-3 and TNF-R1. In internal validation, the MBDA-based CVD risk score was a strong predictor of 3-year risk for a CVD event, with hazard ratio (95% CI) of 2.89 (2.46–3.41). The predicted 3-year CVD risk was low for 9.4% of patients, borderline for 10.2%, intermediate for 52.2%, and high for 28.2%. Model fit was good, with mean predicted versus observed 3-year CVD risks of 4.5% versus 4.4%. The MBDA-based CVD risk score significantly improved risk discrimination by the likelihood ratio test, compared to four clinical models. The risk score also improved prediction, reclassifying 42% of patients versus the simplest clinical model (age + sex), with a net reclassification index (NRI) (95% CI) of 0.19 (0.10–0.27); and 28% of patients versus the most comprehensive clinical model (age + sex + diabetes + hypertension + tobacco use + history of CVD + CRP), with an NRI of 0.07 (0.001–0.13). C-index was 0.715 versus 0.661 to 0.696 for the four clinical models. A prognostic score has been developed to predict 3-year CVD risk for RA patients by using clinical data, three serum biomarkers and the MBDA score. In internal validation, it had good accuracy and outperformed clinical models with and without CRP. The MBDA-based CVD risk prediction score may improve RA patient care by offering a risk stratification tool that incorporates the effect of RA inflammation.Funding Information
- Myriad Genetics, Inc.
This publication has 68 references indexed in Scilit:
- Development of a Multi-Biomarker Disease Activity Test for Rheumatoid ArthritisPLOS ONE, 2013
- An evaluation of molecular and clinical remission in rheumatoid arthritis by assessing radiographic progressionRheumatology, 2013
- Validation of a novel multibiomarker test to assess rheumatoid arthritis disease activityArthritis Care & Research, 2012
- Systematic Review and Meta-Analysis of Methotrexate Use and Risk of Cardiovascular DiseaseThe American Journal of Cardiology, 2011
- On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival dataStatistics in Medicine, 2011
- Lipid paradox in rheumatoid arthritis: the impact of serum lipid measures and systemic inflammation on the risk of cardiovascular diseaseAnnals Of The Rheumatic Diseases, 2011
- Validation of rheumatoid arthritis diagnoses in health care utilization dataArthritis Research & Therapy, 2011
- Extensions of net reclassification improvement calculations to measure usefulness of new biomarkersStatistics in Medicine, 2010
- Explaining the cardiovascular risk associated with rheumatoid arthritis: traditional risk factors versus markers of rheumatoid arthritis severityAnnals Of The Rheumatic Diseases, 2010
- Validating Administrative Data in Stroke ResearchStroke, 2002