Can serum biomarkers predict the outcome of systemic immunosuppressive therapy in adult atopic dermatitis patients?
Journal
Skin health and disease
ISSN: 2690-442X
Titre abrégé: Skin Health Dis
Pays: England
ID NLM: 9918227353706676
Informations de publication
Date de publication:
Mar 2022
Mar 2022
Historique:
received:
22
09
2021
revised:
03
11
2021
accepted:
04
11
2021
entrez:
6
6
2022
pubmed:
7
6
2022
medline:
7
6
2022
Statut:
epublish
Résumé
Atopic dermatitis (AD or eczema) is a most common chronic skin disease. Designing personalised treatment strategies for AD based on patient stratification is of high clinical relevance, given a considerable variation in the clinical phenotype and responses to treatments among patients. It has been hypothesised that the measurement of biomarkers could help predict therapeutic responses for individual patients. We aim to assess whether serum biomarkers can predict the outcome of systemic immunosuppressive therapy in adult AD patients. We developed a statistical machine learning model using the data of an already published longitudinal study of 42 patients who received azathioprine or methotrexate for over 24 weeks. The data contained 26 serum cytokines and chemokines measured before the therapy. The model described the dynamic evolution of the latent disease severity and measurement errors to predict AD severity scores (Eczema Area and Severity Index, (o)SCORing of AD and Patient Oriented Eczema Measure) two-weeks ahead. We conducted feature selection to identify the most important biomarkers for the prediction of AD severity scores. We validated our model in a forward chaining setting and confirmed that it outperformed standard time-series forecasting models. Adding biomarkers did not improve predictive performance. In this study, biomarkers had a negligible and non-significant effect for predicting the future AD severity scores and the outcome of the systemic therapy.
Sections du résumé
Background
UNASSIGNED
Atopic dermatitis (AD or eczema) is a most common chronic skin disease. Designing personalised treatment strategies for AD based on patient stratification is of high clinical relevance, given a considerable variation in the clinical phenotype and responses to treatments among patients. It has been hypothesised that the measurement of biomarkers could help predict therapeutic responses for individual patients.
Objective
UNASSIGNED
We aim to assess whether serum biomarkers can predict the outcome of systemic immunosuppressive therapy in adult AD patients.
Methods
UNASSIGNED
We developed a statistical machine learning model using the data of an already published longitudinal study of 42 patients who received azathioprine or methotrexate for over 24 weeks. The data contained 26 serum cytokines and chemokines measured before the therapy. The model described the dynamic evolution of the latent disease severity and measurement errors to predict AD severity scores (Eczema Area and Severity Index, (o)SCORing of AD and Patient Oriented Eczema Measure) two-weeks ahead. We conducted feature selection to identify the most important biomarkers for the prediction of AD severity scores.
Results
UNASSIGNED
We validated our model in a forward chaining setting and confirmed that it outperformed standard time-series forecasting models. Adding biomarkers did not improve predictive performance.
Conclusions
UNASSIGNED
In this study, biomarkers had a negligible and non-significant effect for predicting the future AD severity scores and the outcome of the systemic therapy.
Identifiants
pubmed: 35665204
doi: 10.1002/ski2.77
pii: SKI277
pmc: PMC9060148
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e77Informations de copyright
© 2021 The Authors. Skin Health and Disease published by John Wiley & Sons Ltd on behalf of British Association of Dermatologists.
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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