Prediction of cardiovascular risk in rheumatoid arthritis: performance of original and adapted SCORE algorithms.

OBJECTIVES: Predictive performance of cardiovascular disease (CVD) risk calculators appears suboptimal in rheumatoid arthritis (RA). A disease-specific CVD risk algorithm may improve CVD risk prediction in RA. The objectives of this study are to adapt the Systematic COronary Risk Evaluation (SCORE) algorithm with determinants of CVD risk in RA and to assess the accuracy of CVD risk prediction calculated with the adapted SCORE algorithm. METHODS: Data from the Nijmegen early RA inception cohort were used. The primary outcome was first CVD events. The SCORE algorithm was recalibrated by reweighing included traditional CVD risk factors and adapted by adding other potential predictors of CVD. Predictive performance of the recalibrated and adapted SCORE algorithms was assessed and the adapted SCORE was externally validated. RESULTS: Of the 1016 included patients with RA, 103 patients experienced a CVD event. Discriminatory ability was comparable across the original, recalibrated and adapted SCORE algorithms. The Hosmer-Lemeshow test results indicated that all three algorithms provided poor model fit (p<0.05) for the Nijmegen and external validation cohort. The adapted SCORE algorithm mainly improves CVD risk estimation in non-event cases and does not show a clear advantage in reclassifying patients with RA who develop CVD (event cases) into more appropriate risk groups. CONCLUSIONS: This study demonstrates for the first time that adaptations of the SCORE algorithm do not provide sufficient improvement in risk prediction of future CVD in RA to serve as an appropriate alternative to the original SCORE. Risk assessment using the original SCORE algorithm may underestimate CVD risk in patients with RA.

Performance of four current risk algorithms in predicting cardiovascular events in patients with early rheumatoid arthritis.

OBJECTIVE: This study was undertaken to assess the predictive ability of 4 established cardiovascular (CV) risk models for the 10-year risk of fatal and non-fatal CV diseases in European patients with rheumatoid arthritis. METHODS: Prospectively collected data from the Nijmegen early rheumatoid arthritis (RA) inception cohort was used. Discriminatory ability for CV risk prediction was estimated by the area under the receiver operating characteristic curve. Calibration was assessed by comparing the observed versus expected number of events using Hosmer-Lemeshov tests and calibration plots. Sensitivity and specificity were calculated for the cut-off values of 10% and 20% predicted risk. RESULTS: Areas under the receiver operating characteristic curve were 0.78-0.80, indicating moderate to good discrimination between patients with and without a CV event. The CV risk models Systematic Coronary Risk Evaluation (SCORE), Framingham risk score (FRS) and Reynolds risk score (RRS) primarily underestimated CV risk at low and middle observed risk levels, and mostly overestimated CV risk at higher observed risk levels. The QRisk II primarily overestimated observed CV risk. For the 10% and 20% cut-off values used as indicators for CV preventive treatment, sensitivity ranged from 68-87% and 40-65%, respectively and specificity ranged from 55-76% and 77-88%, respectively. Depending on the model, up to 32% of observed CV events occurred in patients with RA who were classified as low risk (<10%) for CV disease. CONCLUSIONS: Established risk models generally underestimate (Systematic Coronary Risk Evaluation score, Framingham Risk Score, Reynolds risk score) or overestimate (QRisk II) CV risk in patients with RA.