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Clinical Characteristics of Psoriasis for Initiation of Biologic Therapy: A Cluster Analysis

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 s...

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Autores principales: Kim, Yul Hee, Kim, Seung Il, Park, Bumhee, Lee, Eun-So
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Dermatological Association; The Korean Society for Investigative Dermatology 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10112364/
https://www.ncbi.nlm.nih.gov/pubmed/37041707
http://dx.doi.org/10.5021/ad.22.148
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author Kim, Yul Hee
Kim, Seung Il
Park, Bumhee
Lee, Eun-So
author_facet Kim, Yul Hee
Kim, Seung Il
Park, Bumhee
Lee, Eun-So
author_sort Kim, Yul Hee
collection PubMed
description 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.
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spelling pubmed-101123642023-04-19 Clinical Characteristics of Psoriasis for Initiation of Biologic Therapy: A Cluster Analysis Kim, Yul Hee Kim, Seung Il Park, Bumhee Lee, Eun-So Ann Dermatol Original Article 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. The Korean Dermatological Association; The Korean Society for Investigative Dermatology 2023-04 2023-03-09 /pmc/articles/PMC10112364/ /pubmed/37041707 http://dx.doi.org/10.5021/ad.22.148 Text en Copyright © The Korean Dermatological Association and The Korean Society for Investigative Dermatology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Kim, Yul Hee
Kim, Seung Il
Park, Bumhee
Lee, Eun-So
Clinical Characteristics of Psoriasis for Initiation of Biologic Therapy: A Cluster Analysis
title Clinical Characteristics of Psoriasis for Initiation of Biologic Therapy: A Cluster Analysis
title_full Clinical Characteristics of Psoriasis for Initiation of Biologic Therapy: A Cluster Analysis
title_fullStr Clinical Characteristics of Psoriasis for Initiation of Biologic Therapy: A Cluster Analysis
title_full_unstemmed Clinical Characteristics of Psoriasis for Initiation of Biologic Therapy: A Cluster Analysis
title_short Clinical Characteristics of Psoriasis for Initiation of Biologic Therapy: A Cluster Analysis
title_sort clinical characteristics of psoriasis for initiation of biologic therapy: a cluster analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10112364/
https://www.ncbi.nlm.nih.gov/pubmed/37041707
http://dx.doi.org/10.5021/ad.22.148
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