Published in

BMJ Publishing Group, Annals of the Rheumatic Diseases, 9(78), p. 1160-1166, 2019

DOI: 10.1136/annrheumdis-2018-214755

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2018 EULAR recommendations for a core data set to support observational research and clinical care in giant cell arteritis

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

Giant cell arteritis (GCA) represents the most common form of primary systemic vasculitis and is frequently associated with comorbidities related to the disease itself or induced by the treatment. Systematically collected data on disease course, treatment and outcomes of GCA remain scarce. The aim of this EULAR Task Force was to identify a core set of items which can easily be collected by experienced clinicians, in order to facilitate collaborative research into the course and outcomes of GCA. A multidisciplinary EULAR task force group of 20 experts including rheumatologists, internists, epidemiologists and patient representatives was assembled. During a 1-day meeting, breakout groups discussed items from a previously compiled collection of parameters describing GCA status and disease course. Feedback from breakout groups was further discussed. Final consensus was achieved by means of several rounds of email discussions after the meeting. A three-round Delphi survey was conducted to determine a core set of parameters including the level of agreement. 117 parameters were regarded as relevant. Potential items were subdivided into the following categories: General, demographics, GCA-related signs and symptoms, other medical conditions and treatment. Possible instruments and assessment intervals were proposed for documentation of each item. To facilitate implementation of the recommendations in clinical care and clinical research, a minimum core set of 50 parameters was agreed. This proposed core set intends to ensure that relevant items from different GCA registries and databases can be compared for the dual purposes of facilitating clinical research and improving clinical care.