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Development and pilot-testing of the Alopecia Areata Assessment Tool (ALTO)
BACKGROUND: Alopecia areata (AA) is an autoimmune disease characterized by non-scarring hair loss. The lack of a definitive biomarker or formal diagnostic criteria for AA limits our ability to define the epidemiology of the disease. In this study, we developed and tested the Alopecia Areata Assessme...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5991373/ https://www.ncbi.nlm.nih.gov/pubmed/29874239 http://dx.doi.org/10.1371/journal.pone.0196517 |
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author | Li, David G. Huang, Kathie P. Xia, Fan Di Joyce, Cara Scott, Deborah A. Qureshi, Abrar A. Mostaghimi, Arash |
author_facet | Li, David G. Huang, Kathie P. Xia, Fan Di Joyce, Cara Scott, Deborah A. Qureshi, Abrar A. Mostaghimi, Arash |
author_sort | Li, David G. |
collection | PubMed |
description | BACKGROUND: Alopecia areata (AA) is an autoimmune disease characterized by non-scarring hair loss. The lack of a definitive biomarker or formal diagnostic criteria for AA limits our ability to define the epidemiology of the disease. In this study, we developed and tested the Alopecia Areata Assessment Tool (ALTO) in an academic medical center to validate the ability of this questionnaire in identifying AA cases. METHODS: The ALTO is a novel, self-administered questionnaire consisting of 8 closed-ended questions derived by the Delphi method. This prospective pilot study was administered during a 1-year period in outpatient dermatology clinics. Eligible patients (18 years or older with chief concern of hair loss) were recruited consecutively. No patients declined to participate. The patient’s hair loss diagnosis was determined by a board-certified dermatologist. Nine scoring algorithms were created and used to evaluate the accuracy of the ALTO in identifying AA. RESULTS: 239 patients (59 AA cases and 180 non-AA cases) completed the ALTO and were included for analysis. Algorithm 5 demonstrated the highest sensitivity (89.8%) while algorithm 3 demonstrated the highest specificity (97.8%). Select questions were also effective in clarifying disease phenotype. CONCLUSION: In this study. we have successfully demonstrated that ALTO is a simple tool capable of discriminating AA from other types of hair loss. The ALTO may be useful to identify individuals with AA within large populations. |
format | Online Article Text |
id | pubmed-5991373 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-59913732018-06-08 Development and pilot-testing of the Alopecia Areata Assessment Tool (ALTO) Li, David G. Huang, Kathie P. Xia, Fan Di Joyce, Cara Scott, Deborah A. Qureshi, Abrar A. Mostaghimi, Arash PLoS One Research Article BACKGROUND: Alopecia areata (AA) is an autoimmune disease characterized by non-scarring hair loss. The lack of a definitive biomarker or formal diagnostic criteria for AA limits our ability to define the epidemiology of the disease. In this study, we developed and tested the Alopecia Areata Assessment Tool (ALTO) in an academic medical center to validate the ability of this questionnaire in identifying AA cases. METHODS: The ALTO is a novel, self-administered questionnaire consisting of 8 closed-ended questions derived by the Delphi method. This prospective pilot study was administered during a 1-year period in outpatient dermatology clinics. Eligible patients (18 years or older with chief concern of hair loss) were recruited consecutively. No patients declined to participate. The patient’s hair loss diagnosis was determined by a board-certified dermatologist. Nine scoring algorithms were created and used to evaluate the accuracy of the ALTO in identifying AA. RESULTS: 239 patients (59 AA cases and 180 non-AA cases) completed the ALTO and were included for analysis. Algorithm 5 demonstrated the highest sensitivity (89.8%) while algorithm 3 demonstrated the highest specificity (97.8%). Select questions were also effective in clarifying disease phenotype. CONCLUSION: In this study. we have successfully demonstrated that ALTO is a simple tool capable of discriminating AA from other types of hair loss. The ALTO may be useful to identify individuals with AA within large populations. Public Library of Science 2018-06-06 /pmc/articles/PMC5991373/ /pubmed/29874239 http://dx.doi.org/10.1371/journal.pone.0196517 Text en © 2018 Li et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Li, David G. Huang, Kathie P. Xia, Fan Di Joyce, Cara Scott, Deborah A. Qureshi, Abrar A. Mostaghimi, Arash Development and pilot-testing of the Alopecia Areata Assessment Tool (ALTO) |
title | Development and pilot-testing of the Alopecia Areata Assessment Tool (ALTO) |
title_full | Development and pilot-testing of the Alopecia Areata Assessment Tool (ALTO) |
title_fullStr | Development and pilot-testing of the Alopecia Areata Assessment Tool (ALTO) |
title_full_unstemmed | Development and pilot-testing of the Alopecia Areata Assessment Tool (ALTO) |
title_short | Development and pilot-testing of the Alopecia Areata Assessment Tool (ALTO) |
title_sort | development and pilot-testing of the alopecia areata assessment tool (alto) |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5991373/ https://www.ncbi.nlm.nih.gov/pubmed/29874239 http://dx.doi.org/10.1371/journal.pone.0196517 |
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