POS0747 DIFFERENCES AND SIMILARITIES IN CARDIOVASCULAR RISK ESTIMATION WITH FIVE DIFFERENT PREDICTION MODELS IN RHEUMATOID ARTHRITIS: DATA FROM THE CARDIOVASCULAR OBESITY AND RHEUMATIC DISEASE STUDY GROUP OF THE ITALIAN SOCIETY OF RHEUMATOLOGY
Authors: F. Cacciapaglia, E. Bartoloni, G. L. Erre, M. Piga, G. Sakellariou, O. Viapiana, A. Manfredi, E. Gremese, F. R. Spinelli, F. Atzeni
Journal: Annals of the Rheumatic Diseases (2024)

<h3>Background:</h3> To manage cardiovascular disease (CVD) risk in patients with rheumatoid arthritis (RA) EULAR recommends estimating individual risk at least once every 5 years or when major changes in antirheumatic therapy occur, according to national guidelines or using the SCORE CVD risk prediction model if no national guidelines are available [1]. For the general population, several validated 10-year CV risk estimation tools are widely used in Europe and the United States (US), and some algorithms include RA as an independent CV risk item. However, it has not been ascertained which is the most appropriate CV risk prediction model to be used in daily clinical practice [2], above all in RA patients. <h3>Objectives:</h3> To test whether differences exist using five different algorithms for estimating 10-year CVD risk in a large cohort of RA patients. <h3>Methods:</h3> A cross-sectional cohort of RA patients, fulfilling the 2010 ACR/EULAR classification criteria, free from previous CV events, and included in a multicentre Italian data set, was evaluated. All available variables for the 10-year CV risk estimation by the 2021 updated SCORE-2, the Progetto Cuore (PGC) validated in the Italian population, the atherosclerotic CV disease (ASCVD) risk estimator and the Reynolds Risk Score (RRS) derived from the US population, and the Expanded Risk Score for RA (ERS-RA) resulting from the Consortium of Rheumatology Researchers of North America Registry Study data [3], were collected. Individual CV risk was estimated in all patients aged between 35 and 69 years, as the PGC is the model with the narrowest validation in that age group only. Multiple comparisons and correlations among the different algorithms were performed, also stratifying patients into low, intermediate, or high-risk conditions, as specified. All data were analyzed using the GraphPad Prism package (v.9.5.1) with appropriate statistical tests. <h3>Results:</h3> Data from 810 RA patients [78.3% female; mean age 57±8.2 years; median disease duration of 120 (95%CI 108-120) months] were analyzed. Statistically significant differences in the 10-year CV risk estimation using the different algorithms in the same RA population were detected: the ASCVD risk estimated the highest median CV risk (8.8 %; 95%CI 7.8-9.8) and the PGC the lowest (3.2%; 95%CI 2.9-3.5) (p&lt;0.0001); the SCORE-2 and RRS algorithms did not differ in their estimation of CV risk (5.5%; 95%CI 5.0-5.5 vs 4.8%; 95%CI 4.0-5.5, respectively; p=0,26); while the median 10-year CV risk estimated by the ERS-RA algorithm was 7.3% (95%CI 6.8-7.8) [Figure 1]. The highest Spearman correlation was observed between SCORE-2 and ASCVD risk score (r=0.94 - 95%CI 0.93-0.95; p&lt;0.0001), then with the PGC (r=0.89 - 95%CI 0.88-0.91; p&lt;0.0001), followed by the ERS-RA (r=0.52 - 95%CI 0.46-0.57; p&lt;0.0001), and the lowest correlation was with the RRS (r=0.45 - 95%CI 0.39-0.51; p&lt;0.0001). Categorizing the RA cohort by CV risk class, the PGC significantly classified more patients at low CV risk, while the SCORE-2 classified the highest proportion of patients at intermediate CV risk, and the ERS-RA identified the lowest percentage of patients at intermediate CV risk and the highest percentage of patients at high CV-risk [Figure 2]. <h3>Conclusion:</h3> Our study demonstrated that five different prediction models used in an Italian cohort of RA patients estimate different probabilities of having a CV event within 10 years, with significant differences in the risk stratification of the same population. Most of the discrepancies may be due to the different variables included in each algorithm and their relative weights in the various models with different population-derived characteristics. However, a good correlation among these five algorithms emerged from this study. Rheumatologists should consider these differences and be confident in using validated and practical models to stratify the CV risk in RA patients. <h3>REFERENCES:</h3> [1] Agca R, et al. Ann Rheum Dis. 2017; 76: 17-28. [2] Tunstall-Pedoe H. Heart 2011; 97:442-444. [3] Solomon DH, et al. Arthritis Rheumatol. 2015; 67:1995-2003. <h3>Acknowledgements:</h3> <b>NIL.</b> <h3>Disclosure of Interests:</h3> <b>None declared.</b>