Performance of Cardiovascular Risk Prediction Models in Korean Patients With New‐Onset Rheumatoid Arthritis: National Cohort Study

Author:

Kim Jae Hyun1ORCID,Lee Gaeun2ORCID,Hwang Jinseub2ORCID,Kim Ji‐Won3,Kwon Jin‐Won4ORCID,Song Yun‐Kyoung5ORCID

Affiliation:

1. School of Pharmacy and Institute of New Drug Development Jeonbuk National University Jeonju Republic of Korea

2. Department of Statistics Daegu University Gyeongbuk Republic of Korea

3. Division of Rheumatology, Department of Internal Medicine Daegu Catholic University School of Medicine Daegu Republic of Korea

4. BK21 FOUR Community‐Based Intelligent Novel Drug Discovery Education Unit, College of Pharmacy and Research Institute of Pharmaceutical Sciences Kyungpook National University Daegu Republic of Korea

5. College of Pharmacy Daegu Catholic University Gyeongbuk Republic of Korea

Abstract

Background This study aimed to compare the performance of established cardiovascular risk algorithms in Korean patients with new‐onset rheumatoid arthritis. Methods and Results This retrospective cohort study identified patients newly diagnosed with rheumatoid arthritis without a history of cardiovascular diseases 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 event. General cardiovascular risk prediction algorithms, such as the systematic coronary risk evaluation model, the Korean risk prediction model for atherosclerotic cardiovascular diseases, the American College of Cardiology/American Heart Association pooled equations, and the Framingham Risk Score, were used. The discrimination and calibration of cardiovascular risk prediction models were evaluated. Hazard ratios were estimated using Cox proportional hazards regression. A total of 611 patients among 24 889 patients experienced a major adverse cardiovascular event during follow‐up. The median 10‐year atherosclerotic cardiovascular diseases risk score was significantly higher in patients with major adverse cardiovascular events 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 major adverse cardiovascular events increased significantly in the intermediate‐ and high‐risk groups for all algorithms. However, the risk predictions calculated from all algorithms overestimated the observed cardiovascular risk in the middle to high deciles, and only the systematic coronary risk evaluation algorithm showed comparable observed and predicted event rates in the low‐intermediate deciles with the highest sensitivity. Conclusions The systematic coronary risk evaluation model algorithm and the general risk prediction models discriminated patients with rheumatoid arthritis appropriately. However, overestimation should be considered when applying the cardiovascular risk prediction model in Korean patients.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine

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