A Global Edelphi Exercise To Identify Core Domains And Domain Items For The Development Of A Global Registry Of Alopecia Areata Disease Severity And Treatment Safety (Grass)

JAMA DERMATOLOGY(2021)

引用 13|浏览18
暂无评分
摘要
IMPORTANCE A recent expert consensus exercise emphasized the importance of developing a global network of patient registries for alopecia areata to redress the paucity of comparable, real-world data regarding the effectiveness and safety of existing and emerging therapies for alopecia areata.OBJECTIVE To generate core domains and domain items for a global network of alopecia areata patient registries.EVIDENCE REVIEW Sixty-six participants, representing physicians, patient organizations, scientists, the pharmaceutical industry, and pharmacoeconomic experts, participated in a 3-round eDelphi process, culminating in a face-to-face meeting at the World Congress of Dermatology, Milan, Italy, June 14, 2019.FINDINGS Ninety-two core data items, across 25 domains, achieved consensus agreement. Twenty further noncore items were retained to facilitate data harmonization in centers that wish to record them. Broad representation across multiple stakeholder groups was sought; however, the opinion of physicians was overrepresented.CONCLUSIONS AND RELEVANCE This study identifies the domains and domain items required to develop a global network of alopecia areata registries. These domains will facilitate a standardized approach that will enable the recording of a comprehensive, comparable data set required to oversee the introduction of new therapies and harness real-world evidence from existing therapies at a time when the alopecia areata treatment paradigm is being radically and positively disrupted. Reuse of similar, existing frameworks in atopic dermatitis, produced by the Treatment of Atopic Eczema (TREAT) Registry Taskforce, increases the potential to reuse existing resources, creates opportunities for comparison of data across dermatology subspecialty disease areas, and supports the concept of data harmonization.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要