Aim: To describe the longitudinal active joint count (AJC) trajectories in juvenile idiopathic arthritis (JIA). Methods: A retrospective cohort study at two Canadian centres was performed. The longitudinal trajectories of AJC were described using latent growth curve modeling. The association of baseline characteristics stratified by trajectory group was examined by univariate methods. Results: Data were analyzed for 659 children diagnosed with JIA between 1990/03-2009/09. A maximum of 10 years of follow-up data were included in the analysis. Participants were classified into 5 statistically and clinically distinct AJC trajectories by latent GCM. Conclusions: Using a novel longitudinal statistical method we were able to classify patients with JIA based on their pattern of AJC over time. The trajectory classes need to be examined for their relationship to important genetic and biological predictors. Identification of patterns of disease course is important in working towards the development of a clinically relevant outcome-based classification system in JIA.

Aim: To describe the longitudinal active joint count (AJC) trajectories in juvenile idiopathic arthritis (JIA). Methods: A retrospective cohort study at two Canadian centres was performed. The longitudinal trajectories of AJC were described using latent growth curve modeling. The association of baseline characteristics stratified by trajectory group was examined by univariate methods. Results: Data were analyzed for 659 children diagnosed with JIA between 1990/03-2009/09. A maximum of 10 years of follow-up data were included in the analysis. Participants were classified into 5 statistically and clinically distinct AJC trajectories by latent GCM. Conclusions: Using a novel longitudinal statistical method we were able to classify patients with JIA based on their pattern of AJC over time. The trajectory classes need to be examined for their relationship to important genetic and biological predictors. Identification of patterns of disease course is important in working towards the development of a clinically relevant outcome-based classification system in JIA.