Development of a consensus core dataset in juvenile dermatomyositis for clinical use to inform research. Issue 2 (30th October 2017) - McCann, Liza J; Pilkington, Clarissa A; Huber, Adam M; Ravelli, Angelo; Appelbe, Duncan; Kirkham, Jamie J; Williamson, Paula R; Aggarwal, Amita; Christopher-Stine, Lisa; Constantin, Tamas; Feldman, Brian M; Lundberg, Ingrid; et al.
Abstract : Objectives: This study aimed to develop consensus on an internationally agreed dataset for juvenile dermatomyositis (JDM), designed for clinical use, to enhance collaborative research and allow integration of data between centres. Methods: A prototype dataset was developed through a formal process that included analysing items within existing databases of patients with idiopathic inflammatory myopathies. This template was used to aid a structured multistage consensus process. Exploiting Delphi methodology, two web-based questionnaires were distributed to healthcare professionals caring for patients with JDM identified through email distribution lists of international paediatric rheumatology and myositis research groups. A separate questionnaire was sent to parents of children with JDM and patients with JDM, identified through established research networks and patient support groups. The results of these parallel processes informed a face-to-face nominal group consensus meeting of international myositis experts, tasked with defining the content of the dataset. This developed dataset was tested in routine clinical practice before review and finalisation. Results: A dataset containing 123 items was formulated with an accompanying glossary. Demographic and diagnostic data are contained within form A collected at baseline visit only, disease activity measures are included within form B collected at every visit and disease damage items within form C collected at baseline and annual visits thereafter. Conclusions: Through a robust international process, a consensus dataset for JDM has been formulated that can capture disease activity and damage over time. This dataset can be incorporated into national and international collaborative efforts, including existing clinical research databases.