Login / Signup

European expert consensus statement on the systemic treatment of alopecia areata.

Lidia RudnickaMonika ArenbergerovaRamon GrimaltDemetrios IoannidesAlexander C KatoulisElisabeth LazaridouMałgorzata OlszewskaY S OvcharenkoBianca Maria PiracciniA ProhicAdriana RakowskaPascal ReygagneMarie Aleth RichardR O SoaresMichela Valeria Rita StaraceSergio Vano-GalvanAnna Waśkiel-Burnat
Published in: Journal of the European Academy of Dermatology and Venereology : JEADV (2024)
Alopecia areata is an autoimmune form of non-scarring hair loss. It is usually characterized by limited areas of hair loss. However, the disease may progress to complete scalp and body hair loss (alopecia totalis, alopecia universalis). In patients with alopecia areata hair loss significantly impacts the quality of life. Children and adolescents with alopecia areata often experience bullying, including physical aggression. The disease severity evaluation tools used in clinical practice are: the Severity of Alopecia Tool (SALT) score and the Alopecia Areata Scale (AAS). A SALT score equal to or greater than 20 constitutes a commonly accepted indication for systemic therapy in alopecia areata. When using the AAS, moderate to severe alopecia areata should be considered a medical indication for systemic treatment. Currently, the only two EMA-approved medications for alopecia areata are baricitinib (JAK 1/2 inhibitor) for adults and ritlecitinib (JAK 3/TEC inhibitor) for individuals aged 12 and older. Both are EMA-approved for patients with severe alopecia areata. Other systemic medications used off-label in alopecia areata include glucocorticosteroids, cyclosporine, methotrexate and azathioprine. Oral minoxidil is considered an adjuvant therapy with limited data confirming its possible efficacy. This consensus statement is to outline a systemic treatment algorithm for alopecia areata, indications for systemic treatment, available therapeutic options, their efficacy and safety, as well as the duration of the therapy.
Keyphrases
  • clinical practice
  • healthcare
  • physical activity
  • machine learning
  • multiple sclerosis
  • combination therapy
  • deep learning
  • mental health
  • mesenchymal stem cells