Background While the effect of disease modifying antirheumatic drugs (DMARDs) in rheumatoid arthritis (RA) is generally presented using population means, the response to DMARDs is heterogeneous. As anti-rheumatic therapy aims to become more personalized, identifying common treatment response trajectories to DMARDs is becoming more important and has major implications for clinical practice.

Objectives To identify different types of trajectories in RA disease activity (DAS28) following the initiation of abatacept (ABA), a T cell costimulation inhibitor, and to examine the determinants of responders' type.

Methods In this pooled analysis of nine RA registries (ARTIS, ATTRA, RHEUMADATA, DANBIO, GISEA, NOR-DMARD, ORA, SCQM, Reuma.pt), inclusion criteria were: diagnosis of RA, initiation of ABA treatment, and at least two assessments of DAS28. Patients were followed until end of ABA treatment. We used growth mixture models to identify groups of patients with similar courses of treatment response and derive empirically based trajectory subgroups. We then examined the association of these groups with socio-demographic-, disease-, and treatment-related predictors.

Results 3898 patients were on ABA (mean DAS28 assessments: 5.2). Trajectory analysis identified three types of treatment response trajectories (Figure). The largest group (3576 patients, 91.7%) can be considered as “classic responders” (CR), with a mean DAS28 at baseline of 3.9 and a progressive improvement over time. The second group (219 patients, 5.6%) can be described as “rapid responders” (RR), with high DAS28 values at baseline (mean 6.4), and a marked improvement in the first two years. The third group (103 patients, 2.6%) seemed composed of “non-responders” (NR) with low DAS28 values at baseline (mean 3.8), a deterioration during the first 2 years, followed by improvement, generally related to a change in therapy.

The three groups were similar in age, sex, BMI, disease duration, and comorbidities (ie. cardiovascular, infectious, metabolic disease, smoking and cancer). However, “classic responders” group had better function at baseline (mean HAQ score: CR: 1.1; RR: 1.7; NR: 1.3, p<0.001), and less previous biologics failures (median [Interquartile range] previous bDMARDs: CR: 1 [1; 2]; RR: 2 [1; 3]; NR: 2 [1; 3.5], p<0.001).

There were more ABA discontinuation in the “non-responder” group (CR: 55.4%, RR: 61.6%, NR: 77.6%, p<0.001). Finally, the proportion of patients with low disease activity (DAS28<3.2) at 6 months were higher among classic responders (CR: 20.5%, RR: 3.7%, NR: 1.9%, p=0.04).

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Figure 1

Conclusions There are different types of responders to Abatacept treatment. However, the available baseline information did not allow determination of which trajectory the patient will follow after ABA initiation.

Acknowledgement The study is investigator initiated and supported by an unrestricted research grant from BMS.

Disclosure of Interest D. Courvoisier Grant/research support from: unrestricted research grant from BMS, J.-E. Gottenberg Grant/research support from: Abbvie, BMS, MSD, Pfizer, Roche, Consultant for: Abbvie, BMS, MSD, Pfizer, Roche, M. Hernandez: None declared, F. Iannone Consultant for: Pfizer, Abbvie, MSD, BMS, Actelion, E. Lie Consultant for: Abbvie, UCB, hospira, BMS, Pfizer, H. Canhao Consultant for: Abbvie, MSD, Pfizer, Roche, UCB, K. Pavelka Consultant for: Abbvie, BMS, MSD, Pfizer, Roche, Amgen, UCB, M. Hetland Consultant for: BMS, MSD, Pfizer, Abbott, UCB, Roche, C. Turesson Consultant for: Abbvie, BMS, Janssen, MSD, Pfizer, Roche, UCB, X. Mariette: None declared, D. Choquette: None declared, A. Finckh Consultant for: Abbvie, BMS, MSD, Pfizer, Roche