Predictive Factors for the Long-Term Clinical Course in Patients with Rheumatoid Arthritis Receiving Second-Line Anti-Rheumatic Drugs in Real-World Practice: An Analysis Using Disease Activity Trajectory-Based Clustering Approach

Abstract

Background: The purpose of this study was to stratify patients with rheumatoid arthritis (RA) according to the trend of disease activity by trajectory-based clustering and to identify the predictive factors for treatment response and the switching patterns of biologics according to trajectory groups. Methods: We analysed the data from a nationwide RA cohort from the Korean College of Rheumatology Biologics and Targeted Therapy (KOBIO) registry. Patients treated with second-line disease-modifying anti-rheumatic drugs (DMARDs) were included. Trajectory modeling for clustering was used to group the disease activity trend. The predictive factors and switching patterns of biologics for each trajectory were investigated.Results: The trends in the disease activity of 688 RA patients were clustered into 4 groups: rapid decrease and stable disease activity (group 1, N = 319), rapid decrease followed by an increase (group 2, N = 36), slow and continued decrease (group 3, N = 290), and no decrease in disease activity (group 4, N = 43). In the multivariable analysis for predictive factors, current smoking (OR, 7.845; 95% CI 2.158–28.220), low hemoglobin (OR 0.694; 95% CI, 0.532–0.901), and high initial disease activity score according to the 28-joint assessment (DAS28) (OR, 2.397; 95% CI, 1.638–3.586) were significantly associated with group 4 compared with group 1. Group 1 had a higher proportion of patients who had never had switching (86.5%) and who were initially treated with non-TNF inhibitors (44.2%) compared with groups 2 (52.8% and 25%), 3 (50.3% and 23.4%), and 4 (25.6% and 18.6%).Conclusions: The trajectory-based approach was useful for clustering the disease activity in longitudinal data in patients with RA. Among the four trajectories, the group with sustained high disease activity was associated with current smoking, low hemoglobin, high initial DAS28, and frequent switching of biologics.