A stratified approach to juvenile idiopathic arthritis: leveraging machine learning to uncover subgroups of patients following treatment with etanercept

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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 (n = 139), 69% of CYP with persistent disease following MTX were assigned to other response clusters following ETN initiation. There was high consistency (73%) of CYP having a persistent PGE pattern following both drugs. 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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