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SAT0332 Disease Activity Trajectories in Early Axial Spondyloarthritis: Results from the Desir Cohort
  1. A. Moltό1,2,
  2. S. Tezenas du Montcel3,
  3. D. Wendling4,
  4. M. Dougados2,
  5. A. Vanier3,
  6. L. Gossec1,5
  1. 1GRC-UPMC 08 (EEMOIS), UPMC Université Paris 06
  2. 2Paris Descartes University, Rheumatology Department, Cochin Hospital. INSERM (U1153): Clinical Epidemiology and Biostatistics, APHP-PRES Sorbonne Paris-Cité
  3. 3Department of Biostatistics Public Health and Medical Informatics, UPMC Université Paris 06. AP-HP, Pitié Salpêtrière Hospital, Paris
  4. 4Université de Franche-Comté; Rheumatology Department, CHRU de Besançon, Besançon
  5. 5Rheumatology Department, AP-HP, Pitié Salpêtrière Hospital, Paris, France

Abstract

Background Disease activity over time in axial spondyloarthritis (axSpA) can be heterogeneous, and studies aiming to identify patterns of disease activity are sparse. In other disciplines, trajectory modelling have been applied to identify patterns of behaviour (trajectories) but no studies have attempted to distinguish subgroups with common disease activity over time in axSpA. ASDAS-CRP is a validated tool to measure disease activity, and seems a logical criterion to define the trajectories of disease activity in this setting.

Objectives To identify the disease activity trajectories in patients with early axSpA over a 3-years follow-up period,the baseline predisposing factors to develop such trajectories and the outcomes associated with each trajectory in terms of sick leave and work disability

Methods Prospective, multi-centre study (DESIR cohort) of patients with early inflammatory back pain (<3 years duration) suggestive of axSpA. Only patients fulfilling the ASAS criteria and for whom ≥3 ASDAS values were available over the 3 years of follow-up were analysed. Statistical analysis: Trajectories were estimated by Group Based Trajectory Modelling; predisposing factors were identified by multinomial regression and days of sick leave/work disability were compared in the trajectories by linear/logistic regression.

Results In total, 370 patients were included in the analysis. Five distinctive trajectories of disease activity over 3 years were determined: traj 1 (n=134 (35%) persistent moderate disease activity), traj 2 (n=66 (18%) persistent inactive disease), traj 3 (n=29 (9%) very high disease activity at inclusion but reaching inactive disease after 12 months), traj 4 (n=126 (33%) persistent high disease activity) and trajectory 5 (n=15 (6%) persistent very high disease activity): Figure.

Traj 1 was set as the reference trajectory for the multinomial regression: a whitecollar job was found to be predictive of developing trajectory 2 (OR=2.2 [1.1-5.0]).Male gender (OR=8.8 [2.2-34.5]), high degree of education (OR=4.7 [1.1-22.0]) and past peripheral involvement (OR=7.0 [1.6-30.1]) were predictive factors to develop traj 3. A high degree of education was found to be protective (OR=0.5 [0.2-0.9]) for developing traj 4.

Traj 5 was significantly associated with sick leave over follow-up (p<0.001).Trajectories 4 (OR=6.2 [1.7-41.3]) and 5 (OR=22.7 [2.2-259.1]) were significantly associated with work disability.

Conclusions This study identified 5 trajectories of ASDAS-CRP. Gender, degree of education and profession were the main baseline factors determining the trajectories. Higher disease activity trajectories were significantly associated withmore days of sick leave and work disability over follow-up. Further analysis including treatments as time-changing covariables will allow us to continue exploring these trajectories.

Disclosure of Interest None declared

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