ArticleArthritis & rheumatology (Hoboken, N.J.)2014
The biologic basis of clinical heterogeneity in juvenile idiopathic arthritis.
Article in Arthritis & rheumatology (Hoboken, N.J.), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Treatment Withdrawal Following Remission in Juvenile Idiopathic Arthritis: A Systematic Review of the Literature.Paediatric drugs · 2019Pooled it
- Review of biomarkers in systemic juvenile idiopathic arthritis: helpful tools or just playing tricks?Arthritis research & therapy · 2016Pooled it
- Juvenile arthritis damage index predicts poor response to biological treatment: A prospective cohort study.World journal of clinical pediatrics · 2025Article
- HLA-DRB1*01 predicts treatment outcome in juvenile idiopathic arthritis: A retrospective-prospective cohort study.Biomolecules & biomedicine · 2025Article
- Blood molecular subtypes to guide precision treatment strategies in systemic juvenile idiopathic arthritis.Arthritis research & therapy · 2025Article
- The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence.Rheumatology international · 2024Review
- Greater DMARD use, improved clinical but not patient-reported outcomes in juvenile idiopathic arthritis: what are we missing?Rheumatology (Oxford, England) · 2024Article
- Peripheral blood regulatory T cells and disease activity, quality of life, and outcomes in children with juvenile idiopathic arthritis.Pediatric rheumatology online journal · 2024Article
- Patterns and determinants of response to novel therapies in juvenile and adult-onset polyarthritis.Rheumatology (Oxford, England) · 2024Review
- Transcriptomic signatures of classical monocytes reveal pro-inflammatory modules and heterogeneity in polyarticular juvenile idiopathic arthritis.Frontiers in immunology · 2024Article
- Diagnosing growing pains in children by using machine learning: a cross-sectional multicenter study.Medical & biological engineering & computing · 2022Article
- Synovial fluid mesenchymal progenitor cells from patients with juvenile idiopathic arthritis demonstrate limited self-renewal and chondrogenesis.Scientific reports · 2022Article
- Latent classes of early response trajectories to biologics initiation in juvenile idiopathic arthritis: an analysis of four trials.Pediatric rheumatology online journal · 2022Article
- Increased Development of Th1, Th17, and Th1.17 Cells Under T1 Polarizing Conditions in Juvenile Idiopathic Arthritis.Frontiers in immunology · 2022Article
- Juvenile idiopathic arthritis: from aetiopathogenesis to therapeutic approaches.Pediatric rheumatology online journal · 2021Review
- Biological classification of childhood arthritis: roadmap to a molecular nomenclature.Nature reviews. Rheumatology · 2021Review
- Different Chronic Disorders That Fall within the Term Juvenile Idiopathic Arthritis.Life (Basel, Switzerland) · 2021Review
- Data-Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women's Rheumatoid Arthritis Sequential Study Registry.Arthritis care & research · 2021Observational
- Characterisation, identification, clustering, and classification of disease.Scientific reports · 2021Article
- Machine Learning in Rheumatic Diseases.Clinical reviews in allergy & immunology · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveChildhood arthritis encompasses a heterogeneous family of diseases. Significant variation in clinical presentation remains despite consensus-driven diagnostic classifications. Developments in data analysis provide powerful tools for interrogating large heterogeneous data sets. We report a novel approach to integrating biologic and clinical data toward a new classification for childhood arthritis, using computational biology for data-driven pattern recognition.
methodsProbabilistic principal components analysis was used to transform a large set of data into 4 interpretable indicators or composite variables on which patients were grouped by cluster analysis. Sensitivity analysis was conducted to determine key variables in determining indicators and cluster assignment. Results were validated against an independent validation cohort.
resultsMeaningful biologic and clinical characteristics, including levels of proinflammatory cytokines and measures of disease activity, defined axes/indicators that identified homogeneous patient subgroups by cluster analysis. The new patient classifications resolved major differences between patient subpopulations better than International League of Associations for Rheumatology subtypes. Fourteen variables were identified by sensitivity analysis to crucially determine indicators and clusters. This new schema was conserved in an independent validation cohort.
conclusionData-driven unsupervised machine learning is a powerful approach for interrogating clinical and biologic data toward disease classification, providing insight into the biology underlying clinical heterogeneity in childhood arthritis. Our analytical framework enabled the recovery of unique patterns from small cohorts and addresses a major challenge, patient numbers, in studying rare diseases.
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