Disease activity trajectories in rheumatoid arthritis: a tool for prediction of outcome

Abstract

Objective: Predicting treatment response and disease progression in rheumatoid arthritis (RA) remains an elusive endeavour. Identifying subgroups of patients with similar progression is essential for understanding what hinders improvement. However, this cannot be achieved with response criteria based on current versus previous Disease Activity Scores, as they lack the time component. We propose a longitudinal approach that identifies subgroups of patients while capturing their evolution across several clinical outcomes simultaneously (multi-trajectories). Method: For exploration, the RA cohort BARFOT (n = 2829) was used to identify 24 month post-diagnosis simultaneous trajectories of 28-joint Disease Activity Score and its components. Measurements were available at inclusion (0), 3, 6, 12, 24, and 60 months. Multi-trajectories were found with latent class growth modelling. For validation, the TIRA-2 cohort (n = 504) was used. Radiographic changes, assessed by the modified Sharp van der Heijde score, were correlated with trajectory membership. Results: Three multi-trajectories were identified, with 39.6% of the patients in the lowest and 18.9% in the highest (worst) trajectory. Patients in the worst trajectory had on average eight tender and six swollen joints after 24 months. Radiographic changes at 24 and 60 months were significantly increased from the lowest to the highest trajectory. Conclusion: Multi-trajectories constitute a powerful tool for identifying subgroups of RA patients and could be used in future studies searching for predictive biomarkers for disease progression. The evolution and shape of the trajectories in TIRA-2 were very similar to those in BARFOT, even though TIRA-2 is a newer cohort.