- Poster presentation
- Open Access
Characterization of active joint count trajectories in juvenile idiopathic arthritis
© Berard et al; licensee BioMed Central Ltd. 2012
- Published: 13 July 2012
To describe the longitudinal active joint count (AJC) trajectories in juvenile idiopathic arthritis (JIA) and to examine the association of baseline characteristics with these trajectories.
A retrospective cohort study at two Canadian centres was performed. The longitudinal trajectories of AJC were described using latent growth curve modeling (GCM). Latent GCM is a novel technique that aims to classify individuals into statistically distinct groups based on individual response patterns so that individuals within a group are more similar than individuals between groups. The trajectory classes are each defined by a longitudinal growth curve. The association of baseline characteristics stratified by trajectory group was examined by univariate methods.
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.
Using a novel longitudinal statistical method we were able to classify patients with JIA based on their pattern of AJC over time. These results need to be interpreted in light of clinical significance. The trajectory classes will need to be examined for their predictive ability for distal outcomes and 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.
Roberta A. Berard: None; George Tomlinson: None; Xiuying Li: None; Kiem G. Oen: None; Alan M. Rosenberg: None; Brian M. Feldman: None; Rae S.M. Yeung: None; Claire Bombardier: None.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.