Common Functional Ability Score for Young People With Juvenile Idiopathic Arthritis

From Wiley via Jisc Publications RouterHistory: received 2019-12-13, accepted 2020-03-31, pub-electronic 2021-06-04Article version: VoRPublication status: PublishedFunder: Medical Research Council; Id: http://dx.doi.org/10.13039/501100000265; Grant(s): UK Grant number: MR/K501311/1Funder: Versus Arthritis; Id: http://dx.doi.org/10.13039/501100012041; Grant(s): 20380, 20542Funder: NIHR Manchester Biomedical Research Centre; Id: http://dx.doi.org/10.13039/100014653Objective: As young people enter adulthood, the interchangeable use of child and adult outcome measures may inaccurately capture changes over time. This study aimed to use item response theory (IRT) to model a continuous score for functional ability that can be used no matter which questionnaire is completed. Methods: Adolescents (ages 11–17 years) in the UK Childhood Arthritis Prospective Study (CAPS) self‐completed an adolescent Childhood Health Assessment Questionnaire (CHAQ) and a Health Assessment Questionnaire (HAQ). Their parents answered the proxy‐completed CHAQ. Those children with at least 2 simultaneously completed questionnaires at initial presentation or 1 year were included. Psychometric properties of item responses within each questionnaire were tested using Mokken analyses to assess the applicability of IRT modeling. A previously developed IRT model from the Pharmachild‐NL registry from The Netherlands was validated in CAPS participants. Agreement and correlations between IRT‐scaled functional ability scores were tested using intraclass correlations and Wilcoxon’s signed rank tests. Results: In 303 adolescents, the median age at diagnosis was 13 years, and 61% were female. CHAQ scores consistently exceeded HAQ scores. Mokken analyses demonstrated high scalability, monotonicity, and the fact that each questionnaire yielded reliable scores. There was little difference in item response characteristics between adolescents enrolled in CAPS and Pharmachild‐NL (maximum item residual 0.08). Significant differences were no longer evident between IRT‐scaled HAQ and CHAQ scores. Conclusion: IRT modeling allows the direct comparison of function scores regardless of different questionnaires being completed by different people over time. IRT modeling facilitates the ongoing assessment of function as adolescents transfer from pediatric clinics to adult services

Towards Stratified Treatment of JIA: Machine Learning Identifies Subtypes in Response to Methotrexate from Four UK Cohorts

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 Pedi30/90 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 to 0.71. Singular ACR Pedi30/90 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. <br/

Nothing about us without us: involving patient collaborators for machine learning applications in rheumatology.

From PubMed via Jisc Publications RouterHistory: received 2021-03-29, accepted 2021-06-22Publication status: aheadofprintNovel machine learning methods open the door to advances in rheumatology through application to complex, high-dimensional data, otherwise difficult to analyse. Results from such efforts could provide better classification of disease, decision support for therapy selection, and automated interpretation of clinical images. Nevertheless, such data-driven approaches could potentially model noise, or miss true clinical phenomena. One proposed solution to ensure clinically meaningful machine learning models is to involve primary stakeholders in their development and interpretation. Including patient and health care professionals' input and priorities, in combination with statistical fit measures, allows for any resulting models to be well fit, meaningful, and fit for practice in the wider rheumatological community. Here we describe outputs from workshops that involved healthcare professionals, and young people from the Your Rheum Young Person's Advisory Group, in the development of complex machine learning models. These were developed to better describe trajectory of early juvenile idiopathic arthritis disease, as part of the CLUSTER consortium. We further provide key instructions for reproducibility of this process.Involving people living with, and managing, a disease investigated using machine learning techniques, is feasible, impactful and empowering for all those involved. [Abstract copyright: © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.

COVID-19-RELATED ANXIETY TRAJECTORIES IN CHILDREN, YOUNG PEOPLE AND ADULTS WITH RHEUMATIC DISEASES

OBJECTIVES: Uncertainty regarding the risk of coronavirus disease 2019 (COVID-19), its complications and the safety of immunosuppressive therapies may drive anxiety among adults and parents of children and young people (CYP) with rheumatic diseases. This study explored trajectories of COVID-related anxiety in adults and parents of CYP with rheumatic diseases. METHODS: Adults and parents of CYP participating in the international COVID-19 European Patient Registry were included in the current study if they had enrolled in the 4 weeks following 24 March 2020. COVID-related anxiety scores (0–10) were collected weekly for up to 28 weeks. Group-based trajectory models explored COVID-related anxiety clusters in adult and parent populations, with optimal models chosen based on model fit, parsimony and clinical plausibility. Demographic, clinical and COVID-19 mitigation behaviours were compared between identified clusters using univariable statistics. RESULTS: In 498 parents of CYP and 2640 adults, four common trajectory groups of COVID-related anxiety were identified in each cohort: persistent extreme anxiety (32% and 17%), persistent high anxiety (43% and 41%), improving high anxiety (25% and 32%) and improving moderate anxiety (11% and 10%), respectively. Few characteristics distinguished the clusters in the parent cohort. Higher and more persistent anxiety clusters in the adult cohort were associated with higher levels of respiratory comorbidities, use of immunosuppressive therapies, older age and greater self-isolation. CONCLUSIONS: COVID-19-related anxiety in the rheumatic disease community was high and persistent during the COVID-19 pandemic, with four common patterns identified. In the adult cohort, higher COVID-related anxiety was related to perceived risk factors for COVID-19 morbidity and mortality