Department of Dermatology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Loyola University, Chicago, Illinois, United States of America.
PLoS One. 2018 Jun 6;13(6):e0196517. doi: 10.1371/journal.pone.0196517. eCollection 2018.
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.
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.
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.
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.
斑秃(AA)是一种自身免疫性疾病,其特征是非瘢痕性脱发。由于缺乏明确的生物标志物或正式的 AA 诊断标准,我们无法定义该疾病的流行病学。在这项研究中,我们在学术医疗中心开发并测试了斑秃评估工具(ALTO),以验证该问卷识别 AA 病例的能力。
ALTO 是一种新颖的自我管理问卷,由通过 Delphi 方法得出的 8 个封闭式问题组成。这项前瞻性试点研究在皮肤科门诊诊所进行了为期 1 年的时间。符合条件的患者(18 岁或以上,主要关注脱发)连续招募。没有患者拒绝参与。患者的脱发诊断由经过董事会认证的皮肤科医生确定。创建了 9 种评分算法,并用于评估 ALTO 在识别 AA 中的准确性。
239 名患者(59 例 AA 病例和 180 例非 AA 病例)完成了 ALTO 并被纳入分析。算法 5 表现出最高的敏感性(89.8%),而算法 3 表现出最高的特异性(97.8%)。一些精选问题也有助于明确疾病表型。
在这项研究中,我们成功地证明了 ALTO 是一种能够区分 AA 和其他类型脱发的简单工具。ALTO 可能有助于在大型人群中识别 AA 个体。