Àá½Ã¸¸ ±â´Ù·Á ÁÖ¼¼¿ä. ·ÎµùÁßÀÔ´Ï´Ù.
KMID : 0032220230350020132
Annals of Dermatology
2023 Volume.35 No. 2 p.132 ~ p.139
Clinical Characteristics of Psoriasis for Initiation of Biologic Therapy: A Cluster Analysis
Kim Yul-Hee

Kim Seung-Il
Park Bum-Hee
Lee Eun-So
Abstract
Background : Psoriasis is a complex and heterogeneous disease that widely affects a patient¡¯s life. Biological therapy is usually prescribed in patients with severe psoriasis that do not respond to conventional treatment. However, data on the specific patient characteristics receiving biologics are still unavailable.

Objective: To classify patients with psoriasis into subgroups with distinct phenotypes through cluster analysis, and to evaluate the differences between the clusters to predict disease prognosis by examining the response to biological therapy.

Methods: The clinical characteristics of the patients with psoriasis were investigated and categorized using hierarchical cluster analysis. After clustering, the clinical characteristics of the patients were compared and the initiation of treatment with biologics according to the clusters were evaluated.

Results: A total of 361 patients with psoriasis were classified into two clusters using 16 distinct clinical phenotypes. Group 1 (n=202) consisted of male smokers and alcohol users with higher psoriasis area and severity index (PASI), older age of onset, higher body mass index, and comorbidities including psoriatic arthritis, hypertension, and diabetes when compared to group 2 (n=159). Group 1 had a significantly higher probability of biological treatment initiation than group 2 (p=0.039). The measured risk factors for the initiation of biologics compared were PASI (p<0.001) and nail involvement (p=0.022).

Conclusion: Cluster analysis classified patients with psoriasis into two subgroups according to their clinical characteristics. Predicting the disease prognosis using a combination of specific clinical parameters may aid in the management of the disease.
KEYWORD
Biological therapy, Cluster analysis, Psoriasis
FullTexts / Linksout information
 
Listed journal information
SCI(E) ÇмúÁøÈïÀç´Ü(KCI) KoreaMed ´ëÇÑÀÇÇÐȸ ȸ¿ø