ASSESSING LONG-TERM SERUM SAMPLE VIABILITY FOR CARDIOVASCULAR RISK PREDICTION IN RHEUMATOID ARTHRITIS

Abstract

A precise risk prediction model is necessary for successful intervention and management of patients with rheumatoid arthritis (RA), as they have an elevated risk of cardiovascular disease (CVD). Examining the stability of important biomarkers over a period of 10 to 20 years, this study explores the feasibility of using long-term blood samples to predict cardiovascular risk in RA populations. We evaluate the quality of serum samples and biomarker stability using cutting-edge biobanking methods, with an emphasis on lipid profiles and inflammatory indicators. Furthermore, traditional risk variables as well as RA-specific markers like disease activity are combined in predictive models specifically designed and validated for RA populations. Cardiovascular outcomes and disease activity indices are examined over time using longitudinal data analysis. In order to improve risk assessment and patient monitoring, we also include state-of-the-art technology including wearables, telemedicine platforms, and omics data. This work intends to increase cardiovascular risk prediction in RA, enabling tailored therapies and better patient outcomes. It does this by resolving research gaps through comparative analysis and putting a focus on clinical translation.