EczemaPred: A computational framework for personalised prediction of eczema severity dynamics.
Bayesian model
PO-SCORAD
atopic dermatitis
machine learning
prediction
Journal
Clinical and translational allergy
ISSN: 2045-7022
Titre abrégé: Clin Transl Allergy
Pays: England
ID NLM: 101576043
Informations de publication
Date de publication:
Mar 2022
Mar 2022
Historique:
revised:
12
03
2022
received:
04
11
2021
accepted:
14
03
2022
entrez:
28
3
2022
pubmed:
29
3
2022
medline:
29
3
2022
Statut:
ppublish
Résumé
Atopic dermatitis (AD) is a chronic inflammatory skin disease leading to substantial quality of life impairment with heterogeneous treatment responses. People with AD would benefit from personalised treatment strategies, whose design requires predicting how AD severity evolves for each individual. This study aims to develop a computational framework for personalised prediction of AD severity dynamics. We introduced EczemaPred, a computational framework to predict patient-dependent dynamic evolution of AD severity using Bayesian state-space models that describe latent dynamics of AD severity items and how they are measured. We used EczemaPred to predict the dynamic evolution of validated patient-oriented scoring atopic dermatitis (PO-SCORAD) by combining predictions from the models for the nine severity items of PO-SCORAD (six intensity signs, extent of eczema, and two subjective symptoms). We validated this approach using longitudinal data from two independent studies: a published clinical study in which PO-SCORAD was measured twice weekly for 347 AD patients over 17 weeks, and another one in which PO-SCORAD was recorded daily by 16 AD patients for 12 weeks. EczemaPred achieved good performance for personalised predictions of PO-SCORAD and its severity items daily to weekly. EczemaPred outperformed standard time-series forecasting models such as a mixed effect autoregressive model. The uncertainty in predicting PO-SCORAD was mainly attributed to that in predicting intensity signs (75% of the overall uncertainty). EczemaPred serves as a computational framework to make a personalised prediction of AD severity dynamics relevant to clinical practice. EczemaPred is available as an R package.
Sections du résumé
BACKGROUND
BACKGROUND
Atopic dermatitis (AD) is a chronic inflammatory skin disease leading to substantial quality of life impairment with heterogeneous treatment responses. People with AD would benefit from personalised treatment strategies, whose design requires predicting how AD severity evolves for each individual.
OBJECTIVE
OBJECTIVE
This study aims to develop a computational framework for personalised prediction of AD severity dynamics.
METHODS
METHODS
We introduced EczemaPred, a computational framework to predict patient-dependent dynamic evolution of AD severity using Bayesian state-space models that describe latent dynamics of AD severity items and how they are measured. We used EczemaPred to predict the dynamic evolution of validated patient-oriented scoring atopic dermatitis (PO-SCORAD) by combining predictions from the models for the nine severity items of PO-SCORAD (six intensity signs, extent of eczema, and two subjective symptoms). We validated this approach using longitudinal data from two independent studies: a published clinical study in which PO-SCORAD was measured twice weekly for 347 AD patients over 17 weeks, and another one in which PO-SCORAD was recorded daily by 16 AD patients for 12 weeks.
RESULTS
RESULTS
EczemaPred achieved good performance for personalised predictions of PO-SCORAD and its severity items daily to weekly. EczemaPred outperformed standard time-series forecasting models such as a mixed effect autoregressive model. The uncertainty in predicting PO-SCORAD was mainly attributed to that in predicting intensity signs (75% of the overall uncertainty).
CONCLUSIONS
CONCLUSIONS
EczemaPred serves as a computational framework to make a personalised prediction of AD severity dynamics relevant to clinical practice. EczemaPred is available as an R package.
Identifiants
pubmed: 35344305
doi: 10.1002/clt2.12140
pmc: PMC8967258
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e12140Subventions
Organisme : British Skin Foundation
ID : 005R18
Organisme : Association Centre Nantais de Recherche Appliquée aux Affections Cutanées
Organisme : Laboratoires Dermatologiques Avène
Organisme : Eczema Foundation
Informations de copyright
© 2022 The Authors. Clinical and Translational Allergy published by John Wiley & Sons Ltd on behalf of European Academy of Allergy and Clinical Immunology.
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