Person:

Huang, Kathie

The risk of skin cancer in patients with alopecia areata (AA) is unknown. While the risk of skin cancer in chronic inflammatory alopecias may be elevated, AA shares many characteristics with vitiligo, an autoimmune illness associated with decreased risk of melanoma and non-melanoma skin cancers. In this retrospective cohort study, we determined the risk of developing skin cancer among patients with AA in a validated cohort relative to matched controls at two tertiary care hospitals in Massachusetts. There was a significantly decreased risk of NMSC in AA patients than controls (OR = 0.63, 95% CI = 0.48–0.81). There was a trend towards a protective effect of AA associated with melanoma (OR = 0.65, 95% CI = 0.39–1.09). There was no difference in anatomic distribution of skin cancer between patients with AA and controls. Our study demonstrates a decreased risk of nonmelanoma skin cancer and a trend towards reduced risk of melanoma in patients with AA.

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.