ArticleRheumatology advances in practice2026
A stratified approach to juvenile idiopathic arthritis: leveraging machine learning to uncover subgroups of patients following treatment with etanercept.
Article in Rheumatology advances in practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objectives: To facilitate a stratified approach to treatment of JIA and uncover trajectory-based patterns of key clinical outcomes following etanercept (ETN) initiation in children and young people (CYP). Methods: ETN-naïve CYP with non-systemic JIA recruited to one of three UK prospective multicentre cohorts were selected if they started ETN between 2001 and 2019. JADAS71 (71-joint Juvenile Arthritis Disease Activity) components [active joint count, physician global assessment (PGA), parent global evaluation (PGE) and ESR] were collected in the year following ETN initiation. Clusters of CYP based on JADAS71 component trajectory patterns following ETN initiation were identified using multivariate group-based trajectory models, adjusting for year of ETN initiation. Multinomial logistic regression identified independent predictors of outcome clusters. In a subset of CYP with previously determined MTX trajectories, response clusters were compared descriptively. Results: In 1003 CYP, five clusters were identified following ETN initiation. In three clusters, JADAS components changed in parallel: fast improvers (16%), slow improvers (10%) and persistent disease (37%). In two, all components except for one improved over time: persistent PGA (7%) and persistent PGE (30%). Where both MTX and ETN trajectories were available ( Conclusion: Across multiple DMARDs, JIA disease activity does not always improve to similar degrees across the different features of disease. A majority of CYP initially experiencing persistent disease with MTX fall into a response group following ETN.
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