Does Adding Single‐Nucleotide Polymorphisms to Risk Algorithms Improve Cardiovascular Disease Risk Prediction in Rheumatoid Arthritis? An Internal and External Validation of a Clinical Risk Score
Authors' conclusion
Several SNPs were associated with CVD in RA. Risk prediction slightly improved after adding SNPs to the models, but the clinical relevance is debatable.
Study context
- Population
- Patients with rheumatoid arthritis (RA) genotyped for sixty single-nucleotide polymorphisms (SNPs)99 with cardiovascular disease (CVD)
- Participants
- 353 patients
- Comparison
- Clinical CVD risk prediction model vs model including SNPs
- Outcome
- New CVD; additional predictive value of SNPs for CVD risk prediction
Methodological detailsinternal validation, external validation, logistic and Cox regression
- Internal validation
- External validation
- Logistic and Cox regression
Performance
As reported in the abstract
CVD risk prediction (AUC)
| Test or model | AUC | AUC, internal validation |
|---|---|---|
| Clinical model | 0.74 (P = 0.03) | 0.76 (P < 0.01) |
| SNP model | 0.77 (P < 0.01) | — not stated in the abstract |
AUC
Clinical model0.74 (P = 0.03)
SNP model0.77 (P < 0.01)
AUC, internal validation
Clinical model0.76 (P < 0.01)
SNP model— not stated in the abstract
— not stated in the abstract
Further findings
SNPs associated with new CVD
- rs3184504, rs4773144, rs12190287 and rs445925 significantly associated with new CVD
External validation (separate cohort, n = 297)
- Good fit for the clinical model, poor fit for the SNP model
Original publication
Does Adding Single‐Nucleotide Polymorphisms to Risk Algorithms Improve Cardiovascular Disease Risk Prediction in Rheumatoid Arthritis? An Internal and External Validation of a Clinical Risk Score. Arthritis Care Res (Hoboken). Published 23 July 2024. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →