JOURNAL ARTICLE

Global Guidelines in Dermatology Mapping Project (GUIDEMAP): a systematic review of alopecia areata clinical practice guidelines.

  • Published In: Clinical & Experimental Dermatology, 2023, v. 48, n. 2. P. 100 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Asfour, Leila; Brito, Marianne De; Al-Janabi, Ali; Haw, William W Y; Johnson, Amy; Flohr, Carsten; Yiu, Zenas Zee Ngai 3 of 3

Abstract

This article systematically reviews and critically appraises the quality of clinical practice guidelines (CPGs) for alopecia areata (AA), a common immune-mediated nonscarring hair loss condition with significant psychosocial impact. The review identified six AA CPGs published globally between 2014 and 2021, primarily from high sociodemographic index countries, with under-representation from Asia, South America, and Africa. Using three validated appraisal tools—the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument, Lenzer's red flags, and the United States Institute of Medicine's (IOM) criteria of trustworthiness—the study found that all AA CPGs exhibited substantial methodological deficiencies, particularly in stakeholder involvement, rigour of development, applicability, external review, and transparency. The authors recommend that future guideline development groups enhance methodological rigor by including diverse stakeholders (notably patients and methodologists), conducting external reviews, and improving the applicability of recommendations through clear implementation strategies and audit standards.

Additional Information

  • Source:Clinical & Experimental Dermatology. 2023/02, Vol. 48, Issue 2, p100
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2023
  • ISSN:0307-6938
  • DOI:10.1093/ced/llac025
  • Accession Number:162330238
  • Copyright Statement:Copyright of Clinical & Experimental Dermatology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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