Original Research
Monday, November 13, 2023
01:00 PM–02:30 PM
Abstract
Introduction: Evidence is scarce regarding the validity of these cardiovascular (CV) risk prediction models to accurately predict the risk in patients with rheumatoid arthritis (RA), especially in Asia, despite the large ethnic differences in CV risk in patients with RA.
Research Question or Hypothesis: This study aimed to compare the performance of established CV risk algorithms in East Asian patients with new-onset RA.
Study Design: Retrospective cohort study
Methods: This study identified patients newly diagnosed with RA without a history of CV diseases (CVDs) between 2013 and 2019 using the National Health Insurance Service database. The cohort was followed up until 2020 for the development of the first major adverse cardiovascular events (MACEs). General CV risk prediction algorithms, such as the systematic coronary risk evaluation (SCORE) model, the Korean risk prediction model for atherosclerotic CVDs (ASCVDs), the ACC/AHA pooled equations, and the Framingham risk score, were used. The discrimination and calibration of CV risk prediction models were evaluated. Hazard ratios were estimated using Cox proportional hazards regression.
Results: Total 611 patients among 24,889 patients experienced MACE during follow-up. The median 10-year ASCVD risk score was significantly higher in patients with MACEs than those without. The c-statistics of risk algorithms ranged between 0.72 and 0.74. Compared with the low-risk group, the actual risk of developing MACEs increased significantly in the intermediate- and high-risk groups for all algorithms. However, the risk predictions calculated from all algorithms overestimated the observed CV risk in the middle to high deciles, and only the SCORE algorithm showed comparable observed and predicted event rates in the low-intermediate deciles with the highest sensitivity.
Conclusion: The SCORE algorithm and the general risk prediction models discriminated RA patients appropriately. However, overestimation should be considered when applying the CV risk prediction model in Korean patients.
Presenting Author
Yun-Kyoung Song Ph.DCollege of Pharmacy, Seoul National University
Authors
Jae Hyun Kim Ph.D
Jeonbuk National University
Jin Won Kwon Ph.D
Kyungpook National University
Gaeun Lee MS
Daegu University
Jinseub Hwang Ph.D
Daegu University