Ab0634 Predictors of Cdai Trajectories in Rheumatoid Arthritis Patients Treated With Abatacept: A Real-World Study

Background: While analysis of disease severity at specific time points is informative, the trajectory of rheumatic arthritis (RA) treatment response over time may be more relevant. Identification of predictors of different RA disease trajectory patterns may be useful for clinical decision-making. Objectives: To describe the 12-month clinical disease activity index (CDAI) trajectory patterns and identify determinants of rapid trajectory in patients (pts) treated for RA with abatacept. Methods: This was a post hoc analysis of 290 adult RA pts who were initiated on treatment with abatacept in the Abatacept Best Care (ABC) study (NCT03274141)1. A growth mixture model (GMM) identified CDAI trajectory groups over 12 months. Bivariate analyses identified potential predictors of trajectories among baseline patient and disease characteristics. Variables with an important difference (PResults: The GMM identified rapid responders (RR: n = 210 72.4%) with sustained decline in CDAI by 3 months, non-rapid responders (NRR: n = 75 25.9%) and non-classifiable (NC: n = 5 1.7%). NC pts were excluded from the final analysis. For the overall analysis population (n = 285), mean (SD) age was 60.2 (11.7) years and 75 (26.3%) were male without differences between response groups. Table 1 describes the significant between-group differences on bivariate analyses. In multivariate analysis, shorter disease duration and lower comorbidity index, TJC, patient pain, and fatigue at baseline were significant independent predictors of having a rapid CDAI response trajectory over 12 months (Table 2). Conclusion: In this real-world study, the majority of RA pts treated with abatacept showed a rapid trajectory in clinical improvement. Shorter disease duration, fewer comorbidities, and lower patient-driven outcomes (TJC, pain, fatigue) at baseline, but not physician-reported measures, were identified as predictors of rapid response. Trajectory-based analyses may have implications for clinical practice and research by informing therapeutic expectations. REFERENCES: 1 Bessette, L. et al. Effectiveness of a treat-to-target strategy in patients with moderate to severely active rheumatoid arthritis treated with abatacept. Arthritis Res Ther25, 183 (2023). https://doi.org:10.1186/s13075-023-03151-2 Acknowledgements: NIL. Disclosure of Interests: Louis Bessette speaker for Amgen, BMS, Janssen, UCB, AbbVie, Pfizer, Lilly, Novartis, Sanofi, Sandoz, Fresenius Kabi, Teva, Organon and JAMP Pharma, consultant for Amgen, BMS, Janssen, UCB, AbbVie, Pfizer, Lilly, Novartis, Sanofi, Sandoz, Fresenius Kabi, Teva, Organon and JAMP Pharma, research support from Amgen, BMS, Janssen, UCB, AbbVie, Pfizer, Celgene, Sanofi, Lilly, Novartis, Gilead and JAMP Pharma, John Sampalis employee of JSS Medical Research, the contract research organization that managed the study, Boulos Haraoui advisory board member and research grants from Abbvie, Amgen, BMS, Fresenius Kabi, Lilly, Pfizer and UCB, Emmanouil Rampakakis employee of JSS Medical Research, the contract research organization that managed the study, Dylan Keating employee of JSS Medical Research, the contract research organization that managed the study, Marc Olivier Trepanier employee of Bristol Myers Squibb and may hold stock or stock options, employee of Bristol Myers Squibb and may hold stock or stock options, Janet Pope consultant for AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Gilead, Janssen, Medexus, MSD, Novartis, Pfizer, Roche, Sandoz, Sanofi, Teva and UCB, grant/research support from AbbVie, Bristol Myers Squibb, MSD, Pfizer, Roche, Seattle Genetics and UCB.