Dataset Information

CAPTURE-JIA: a consensus-derived core dataset to improve clinical care for children and young people with juvenile idiopathic arthritis.

ABSTRACT: OBJECTIVES:Data collected during routine clinic visits are key to driving successful quality improvement in clinical services and enabling integration of research into routine care. The purpose of this study was to develop a standardized core dataset for juvenile idiopathic arthritis (JIA) (termed CAPTURE-JIA), enabling routine clinical collection of research-quality patient data useful to all relevant stakeholder groups (clinicians, service-providers, researchers, health service planners and patients/families) and including outcomes of relevance to patients/families. METHODS:Collaborative consensus-based approaches (including Delphi and World Café methodologies) were employed. The study was divided into discrete phases, including collaborative working with other groups developing relevant core datasets and a two-stage Delphi process, with the aim of rationalizing the initially long data item list to a clinically feasible size. RESULTS:The initial stage of the process identified collection of 297 discrete data items by one or more of fifteen NHS paediatric rheumatology centres. Following the two-stage Delphi process, culminating in a consensus workshop (May 2015), the final approved CAPTURE-JIA dataset consists of 62 discrete and defined clinical data items including novel JIA-specific patient-reported outcome and experience measures. CONCLUSIONS:CAPTURE-JIA is the first 'JIA core dataset' to include data items considered essential by key stakeholder groups engaged with leading and improving the clinical care of children and young people with JIA. Collecting essential patient information in a standard way is a major step towards improving the quality and consistency of clinical services, facilitating collaborative and effective working, benchmarking clinical services against quality indicators and aligning treatment strategies and clinical research opportunities.

SUBMITTER: McErlane F

PROVIDER: S-EPMC6909903 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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CAPTURE-JIA: a consensus-derived core dataset to improve clinical care for children and young people with juvenile idiopathic arthritis.

McErlane Flora F Armitt Gillian G Cobb Joanna J Bailey Kathryn K Cleary Gavin G Douglas Sharon S Lunt Laura L Rashid Amir A Sampath Sunil S Shoop-Worrall Stephanie S Smith Nicola N Foster Helen H Thomson Wendy W

Rheumatology (Oxford, England) 20200101 1

<h4>Objectives</h4>Data collected during routine clinic visits are key to driving successful quality improvement in clinical services and enabling integration of research into routine care. The purpose of this study was to develop a standardized core dataset for juvenile idiopathic arthritis (JIA) (termed CAPTURE-JIA), enabling routine clinical collection of research-quality patient data useful to all relevant stakeholder groups (clinicians, service-providers, researchers, health service planner ...[more]

PMID: 31243450

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Project description:BackgroundA significant proportion of children and young people with juvenile idiopathic arthritis (JIA) do not achieve inactive disease during the first two years following diagnosis. Refinements to clinical care pathways have the potential to improve clinical outcomes but a lack of consistent and contemporaneous clinical data presently precludes standard setting and implementation of meaningful quality improvement programmes. This study was the first to pilot clinical data collection and analysis using the CAPTURE-JIA dataset, and to explore patient and clinician-reported feasibility and acceptability data.MethodsA multiphase mixed-methods approach enabled prospective collection of quantitative data to examine the feasibility and efficacy of dataset collection and of qualitative data informing the context and processes of implementation. An initial paper pilot informed the design of a bespoke electronic data collection system (the Agileware system), with a subsequent electronic pilot informing the final CAPTURE-JIA data collection tool.ResultsPaper collection of patient data was feasible but time-consuming in the clinical setting. Phase 1 paper pilot data (121 patients) identified three themes: problematic data items (14/62 data items received >40% missing data), formatting of data collection forms and a clinician-highlighted need for digital data collection, informing Phase 2 electronic data collection tool development. Patients and families were universally supportive of the collection and analysis of anonymised patient data to inform clinical care. No apparent preference for paper / electronic data collection was reported by families. Phase 3 electronic pilot data (38 patients) appeared complete and the system reported to be easy to use. Analysis of the study dataset and a dummy longitudinal dataset confirmed that all eleven JIA national audit questions can be answered using the electronic system.ConclusionsMulticentre CAPTURE-JIA data collection is feasible and acceptable, with a bespoke data collection system highlighted as the most satisfactory solution. The study is informing ongoing work towards a streamlined and flexible national paediatric data collection system to drive quality improvement in clinical care.

Project description:BackgroundThe cytokine IL-10 and its family members have been implicated in autoimmune diseases and we have previously reported that genetic variants in IL-10 were associated with a rare group of diseases called juvenile idiopathic arthritis (JIA). The aim of this study was to fine map genetic variants within the IL-10 cytokine family cluster on chromosome 1 using linkage disequilibrium (LD)-tagging single nucleotide polymorphisms (tSNPs) approach with imputation and conditional analysis to test for disease associations.Methodology/principal findingsFifty-three tSNPs were tested for association between Caucasian paediatric cohorts [219 systemic JIA (sJIA), 187 persistent oligoarticular JIA (pOJIA), and 139 extended OJIA (eOJIA) patients], and controls (Wellcome Trust control cohort, WTCCC2). Significant association with sJIA was detected at rs1400986 in the promoter of IL-20 (odds ratio 1.53; 95% CI 1.21-1.93; p?=?0.0004), but in no other subtypes. Imputation analysis identified additional associated SNPs for pOJIA at IL-20 and IL-24, including a rare, functional, missense variant at IL-24 with a p?=?0.0002. Penalised logistic regression analysis with HyperLasso and conditional analysis identified several further associations with JIA subtypes. In particular, haplotype analysis refined the sJIA association, with a joint effect at rs1400986 and rs4129024 in intron 1 of MAPKAPK2 (p?=?3.2E-5). For pOJIA, a 3-SNP haplotype including rs1878672 in intron 3 of IL-10 showed evidence for association (p?=?0.0018). In eOJIA, rs10863962 (3'UTR of FCAMR) and rs12409577 (intron of IL-19) haplotype showed some evidence of association (p?=?0.0003).ConclusionsThis study supports previous association of IL-20 with sJIA. Haplotype analyses provided stronger association signals than single point analyses, while a penalised logistic regression approach also suggested multiple independent association signals. Replication studies are required to confirm or refute these findings. The results indicate that combined effects with unknown/rare variants remain to be characterised in JIA, and represent a possible example of synthetic association in this region.

Dataset Information

CAPTURE-JIA: a consensus-derived core dataset to improve clinical care for children and young people with juvenile idiopathic arthritis.

Publications

CAPTURE-JIA: a consensus-derived core dataset to improve clinical care for children and young people with juvenile idiopathic arthritis.

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