UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Towards stratified treatment of JIA: machine learning identifies subtypes in response to methotrexate from four UK cohorts

Shoop-Worrall, SJW; Lawson-Tovey, S; Wedderburn, LR; Hyrich, KL; Geifman, N; Kimonyo, A; McNeece, A; ... Wanstall, Z; + view all (2024) Towards stratified treatment of JIA: machine learning identifies subtypes in response to methotrexate from four UK cohorts. eBioMedicine , 100 , Article 104946. 10.1016/j.ebiom.2023.104946. Green open access

[thumbnail of Wedderburn_Towards stratified treatment of JIA- machine learning identifies subtypes in response to methotrexate from four UK cohorts_VoR.pdf]
Preview
Text
Wedderburn_Towards stratified treatment of JIA- machine learning identifies subtypes in response to methotrexate from four UK cohorts_VoR.pdf

Download (1MB) | Preview

Abstract

BACKGROUND: Methotrexate (MTX) is the gold-standard first-line disease-modifying anti-rheumatic drug for juvenile idiopathic arthritis (JIA), despite only being either effective or tolerated in half of children and young people (CYP). To facilitate stratified treatment of early JIA, novel methods in machine learning were used to i) identify clusters with distinct disease patterns following MTX initiation; ii) predict cluster membership; and iii) compare clusters to existing treatment response measures. METHODS: Discovery and verification cohorts included CYP who first initiated MTX before January 2018 in one of four UK multicentre prospective cohorts of JIA within the CLUSTER consortium. JADAS components (active joint count, physician (PGA) and parental (PGE) global assessments, ESR) were recorded at MTX start and over the following year. Clusters of MTX ‘response’ were uncovered using multivariate group-based trajectory modelling separately in discovery and verification cohorts. Clusters were compared descriptively to ACR Pedi 30/90 scores, and multivariate logistic regression models predicted cluster-group assignment. FINDINGS: The discovery cohorts included 657 CYP and verification cohorts 1241 CYP. Six clusters were identified: Fast improvers (11%), Slow Improvers (16%), Improve-Relapse (7%), Persistent Disease (44%), Persistent PGA (8%) and Persistent PGE (13%), the latter two characterised by improvement in all features except one. Factors associated with clusters included ethnicity, ILAR category, age, PGE, and ESR scores at MTX start, with predictive model area under the curve values of 0.65–0.71. Singular ACR Pedi 30/90 scores at 6 and 12 months could not capture speeds of improvement, relapsing courses or diverging disease patterns. INTERPRETATION: Six distinct patterns following initiation of MTX have been identified using methods in artificial intelligence. These clusters demonstrate the limitations in traditional yes/no treatment response assessment (e.g., ACRPedi30) and can form the basis of a stratified medicine programme in early JIA. FUNDING: Medical Research Council, Versus Arthritis, Great Ormond Street Hospital Children's Charity, Olivia’s Vision, and the National Institute for Health Research.

Type: Article
Title: Towards stratified treatment of JIA: machine learning identifies subtypes in response to methotrexate from four UK cohorts
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.ebiom.2023.104946
Publisher version: http://dx.doi.org/10.1016/j.ebiom.2023.104946
Language: English
Additional information: © 2024 The Authors. Published by Elsevier B.V. under a Creative Commons license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Juvenile idiopathic arthritis, Machine learning, Treatment outcome, Epidemiology, Methotrexate
URI: https://discovery.ucl.ac.uk/id/eprint/10185919
Downloads since deposit
14Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item