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Updated: Nov 27, 2025
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Derivation and internal validation of a multi-biomarker-based cardiovascular disease risk prediction score for
Jeffrey R Curtis1, Fenglong Xie2, Cynthia S Crowson3
1University of Alabama at Birmingham, Birmingham, AL, USA. jrcurtis@uabmc.edu.
Insights
A new biomarker-based score accurately predicts cardiovascular disease (CVD) risk in rheumatoid arthritis (RA) patients. This tool, incorporating the multi-biomarker disease activity (MBDA) score, aids in better RA patient care and risk stratification.
Area of Science:
- Rheumatology
- Cardiology
- Biomarker Discovery
Background:
- Rheumatoid arthritis (RA) patients face a higher risk of cardiovascular disease (CVD).
- Accurate CVD risk prediction is crucial for managing RA patients.
- Developing a novel biomarker-based model can enhance CVD risk assessment in RA.
Purpose of the Study:
- To develop and validate a biomarker-based model for predicting CVD risk in RA patients.
- To assess the performance of the multi-biomarker disease activity (MBDA) score and its components in CVD risk prediction.
- To compare the novel model against existing clinical CVD risk prediction tools.
Main Methods:
- Linked Medicare claims data with MBDA test results for an RA patient cohort (age ≥ 40).
- Utilized Cox proportional hazard regression with backward elimination to build the model.
- Internally validated the MBDA-based CVD risk score and compared it with four clinical models.
Main Results:
- The final model included age, diabetes, hypertension, tobacco use, CVD history, MBDA score, leptin, MMP-3, and TNF-R1.
- The MBDA-based CVD risk score demonstrated strong prediction of 3-year CVD events (HR 2.89).
- The score significantly improved risk discrimination and reclassification compared to clinical models (C-index 0.715).
Conclusions:
- A prognostic score using clinical data, three serum biomarkers, and the MBDA score was developed.
- The MBDA-based CVD risk score showed good accuracy and outperformed clinical models in internal validation.
- This score offers a valuable tool for RA patient risk stratification, incorporating RA inflammation.
Background:
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.
Methods:
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.
Results:
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.
Conclusion:
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.
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