RT Journal Article
SR Electronic
T1 Description of Active Joint Count Trajectories in Juvenile Idiopathic Arthritis
JF The Journal of Rheumatology
JO J Rheumatol
FD The Journal of Rheumatology
SP jrheum.130835
DO 10.3899/jrheum.130835
A1 Roberta A.  Berard
A1 George Tomlinson
A1 Xiuying Li
A1 Kiem Oen
A1 Alan M.  Rosenberg
A1 Brian M.  Feldman
A1 Rae S.M.  Yeung
A1 Claire Bombardier
YR 2014
UL http://www.jrheum.org/content/early/2014/09/25/jrheum.130835.abstract
AB Objective To describe the trajectories of longitudinal joint disease activity in juvenile idiopathic arthritis (JIA), and to examine associations of clinical and laboratory characteristics with the identified trajectories. Methods A retrospective cohort study at 2 Canadian centers was performed. The longitudinal trajectories of active joint counts were described in a proof-of-concept study using a latent growth curve analysis. Baseline patient characteristics were compared across trajectory groups. Results Data were analyzed on 659 children diagnosed with JIA between March 1980 and September 2009. The median age at diagnosis was 10.0 years (interquartile range 3.7–13.4) and 61% (402/659) were female. The International League of Associations for Rheumatology (ILAR) diagnoses were as follows: oligoarthritis (36%), enthesitis-related arthritis (20%), rheumatoid factor (RF)-negative polyarthritis (13%), undifferentiated arthritis (12%), psoriatic arthritis (8%), systemic arthritis (7%), and RF-positive polyarthritis (4%). Based on the trajectories of their active joint counts, the 659 patients were each classified in 1 of 5 latent classes (which can be described as high decreasing, moderate increasing, persistent moderate, persistent low, and minimal joint activity). These latent classes were clinically and statistically distinct from the ILAR categories. Conclusion In this proof-of-concept study, in which we used an analytic methodology in a novel way, we identified 5 clinically and statistically distinct trajectories of disease course. The subsets of patients within each class were different from those described by the ILAR classification criteria. This successful application of this method supports its use in a chronic disease with a fluctuating course such as JIA. These methods should be expanded for the purposes of predictive modeling.