PMID- 38407422
OWN - NLM
STAT- Publisher
LR  - 20240226
IS  - 1468-3083 (Electronic)
IS  - 0926-9959 (Linking)
DP  - 2024 Feb 26
TI  - Residual facial erythema in atopic dermatitis patients treated with dupilumab 
      stratified by machine learning.
LID - 10.1111/jdv.19909 [doi]
AB  - BACKGROUND: Persistent facial erythema represents a significant complication in 
      atopic dermatitis (AD) patients undergoing treatment with dupilumab. Stratifying 
      patients based on the erythema course is crucial for elucidating heterogeneous 
      phenotypes and facilitating advanced drug efficacy predictions. OBJECTIVES: This 
      study aimed to identify factors associated with facial erythema severity in 
      dupilumab-treated AD patients and to establish a prediction model for drug 
      response based on the identified factors. METHODS: Data from a retrospective 
      study conducted between July 2018 and July 2021 were collected and analysed. 
      Patients were categorized into three groups via hierarchical clustering based on 
      the course of facial erythema: early remission, low remission and persistent 
      residual. LightGBM, a supervised gradient boosting decision tree algorithm, was 
      employed to discern group differences and construct a prediction model. The model 
      incorporated patient demographic and clinical profiles, including pre- and 
      post-treatment examinations. The model's performance was evaluated using accuracy 
      and the area under the receiver operating characteristic curve (AUC). RESULTS: 
      The binary classification model demonstrated an accuracy of 89.10% and an AUC of 
      0.862 when distinguishing between early remission and persistent residual 
      patients. The eight prominent factors associated with facial erythema severity 
      included age, sex, lactate dehydrogenase (LDH), immunoglobulin E (IgE), 
      eosinophil count, white blood cell count, Alnus allergy and cedar allergy. 
      CONCLUSIONS: This study has two main significances: first, three clusters were 
      identified through unsupervised learning; second, a classification model was 
      constructed that proved more accurate than random prediction. The stratification 
      and identification of crucial factors associated with residual facial erythema in 
      dupilumab-treated AD patients lay the foundation for AI-powered prognostic 
      models. This groundwork provides a substantial basis for enhancing future medical 
      AI support in AD treatment selection, potentially improving personalized 
      treatment approaches and outcomes.
CI  - (c) 2024 European Academy of Dermatology and Venereology.
FAU - Ashizaki, Koichi
AU  - Ashizaki K
AUID- ORCID: 0000-0003-2292-9112
AD  - Nomura Dermatology Clinic, Yokohama, Japan.
AD  - Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, 
      RIKEN, Yokohama, Japan.
FAU - Ishikawa, Tetsuo
AU  - Ishikawa T
AUID- ORCID: 0000-0001-5418-5387
AD  - Nomura Dermatology Clinic, Yokohama, Japan.
AD  - Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, 
      RIKEN, Yokohama, Japan.
AD  - Collective Intelligence Research Laboratory, Graduate School of Arts and 
      Sciences, The University of Tokyo, Tokyo, Japan.
AD  - Department of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, 
      Keio University School of Medicine, Tokyo, Japan.
FAU - Nomura, Yuko
AU  - Nomura Y
AUID- ORCID: 0000-0002-5145-223X
AD  - Nomura Dermatology Clinic, Yokohama, Japan.
LA  - eng
GR  - 10.13039/501100001691/Japan Society for the Promotion of Science (JSPS) KAKENHI/
GR  - JP23ek0410090/Japan Agency for Medical Research and Development (AMED)/
GR  - Grant for Encouragement of Clinical Research on Atopic Dermatitis, Presentation 
      Award at the 121st Annual Meeting of the Japanese Dermatological Association/
PT  - Journal Article
DEP - 20240226
PL  - England
TA  - J Eur Acad Dermatol Venereol
JT  - Journal of the European Academy of Dermatology and Venereology : JEADV
JID - 9216037
SB  - IM
EDAT- 2024/02/26 12:43
MHDA- 2024/02/26 12:43
CRDT- 2024/02/26 11:20
PHST- 2023/07/28 00:00 [received]
PHST- 2024/01/25 00:00 [accepted]
PHST- 2024/02/26 12:43 [medline]
PHST- 2024/02/26 12:43 [pubmed]
PHST- 2024/02/26 11:20 [entrez]
AID - 10.1111/jdv.19909 [doi]
PST - aheadofprint
SO  - J Eur Acad Dermatol Venereol. 2024 Feb 26. doi: 10.1111/jdv.19909.