Personalized prediction of daily eczema severity scores using a mechanistic machine learning model.
Journal
Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology
ISSN: 1365-2222
Titre abrégé: Clin Exp Allergy
Pays: England
ID NLM: 8906443
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
17
01
2020
revised:
10
06
2020
accepted:
06
07
2020
pubmed:
5
8
2020
medline:
3
11
2021
entrez:
5
8
2020
Statut:
ppublish
Résumé
Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalized treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms and treatment responses within and across individuals. Better prediction of AD severity over time for individual patients could help to select optimum timing and type of treatment for improving disease control. We aimed to develop a proof of principle mechanistic machine learning model that predicts the patient-specific evolution of AD severity scores on a daily basis. We designed a probabilistic predictive model and trained it using Bayesian inference with the longitudinal data from two published clinical studies. The data consisted of daily recordings of AD severity scores and treatments used by 59 and 334 AD children over 6 months and 16 weeks, respectively. Validation of the predictive model was conducted in a forward-chaining setting. Our model was able to predict future severity scores at the individual level and improved chance-level forecast by 60%. Heterogeneous patterns in severity trajectories were captured with patient-specific parameters such as the short-term persistence of AD severity and responsiveness to topical steroids, calcineurin inhibitors and step-up treatment. Our proof of principle model successfully predicted the daily evolution of AD severity scores at an individual level and could inform the design of personalized treatment strategies that can be tested in future studies. Our model-based approach can be applied to other diseases with apparent unpredictability and large variation in symptoms and treatment responses such as asthma.
Sections du résumé
BACKGROUND
Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalized treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms and treatment responses within and across individuals. Better prediction of AD severity over time for individual patients could help to select optimum timing and type of treatment for improving disease control.
OBJECTIVE
We aimed to develop a proof of principle mechanistic machine learning model that predicts the patient-specific evolution of AD severity scores on a daily basis.
METHODS
We designed a probabilistic predictive model and trained it using Bayesian inference with the longitudinal data from two published clinical studies. The data consisted of daily recordings of AD severity scores and treatments used by 59 and 334 AD children over 6 months and 16 weeks, respectively. Validation of the predictive model was conducted in a forward-chaining setting.
RESULTS
Our model was able to predict future severity scores at the individual level and improved chance-level forecast by 60%. Heterogeneous patterns in severity trajectories were captured with patient-specific parameters such as the short-term persistence of AD severity and responsiveness to topical steroids, calcineurin inhibitors and step-up treatment.
CONCLUSIONS
Our proof of principle model successfully predicted the daily evolution of AD severity scores at an individual level and could inform the design of personalized treatment strategies that can be tested in future studies. Our model-based approach can be applied to other diseases with apparent unpredictability and large variation in symptoms and treatment responses such as asthma.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
1258-1266Subventions
Organisme : Wellcome Trust
ID : 205039/Z/16/Z
Pays : United Kingdom
Organisme : National Centre for the Replacement, Refinement and Reduction of Animals in Research
ID : NC/P00217X/1
Pays : United Kingdom
Informations de copyright
© 2020 The Authors. Clinical & Experimental Allergy published by John Wiley & Sons Ltd.
Références
Johansson SGO, Bieber T, Dahl R, et al. Revised nomenclature for allergy for global use: Report of the Nomenclature Review Committee of the World Allergy Organization, October 2003. J Allergy Clin Immunol. 2004;113(5):832-836.
Weidinger S, Novak N. Atopic dermatitis. Lancet. 2016;387(10023):1109-1122.
Drucker AM, Wang AR, Li WQ, Sevetson E, Block JK, Qureshi AA. The burden of atopic dermatitis: summary of a report for the national eczema association. J Invest Dermatol. 2017;137(1):26-30.
Bieber T, D’Erme AM, Akdis CA, et al. Clinical phenotypes and endophenotypes of atopic dermatitis: Where are we, and where should we go? J Allergy Clin Immunol. 2017;139(4):S58-S64.
Galli SJ. Toward precision medicine and health: Opportunities and challenges in allergic diseases. J Allergy Clin Immunol. 2016;137(5):1289-1300.
Senn S. Statistical pitfalls of personalized medicine. Nature. 2018;563(7733):619-621.
Lipton ZC. The Mythos of Model Interpretability. ACM Queue. 2018;16(3):30-57.
Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206-215.
Goodman B, Flaxman S. European Union regulations on algorithmic decision-making and a “right to explanation”. AI Mag. 2017;38(3):50.
Bishop CM. Model-based machine learning. Philos Trans R Soc A Math Phys Eng Sci. 2013;371(1984):20120222.
Simpson A, Tan VYF, Winn J, et al. Beyond atopy. Am J Respir Crit Care Med. 2010;181(11):1200-1206.
Zhang Y, Berhane K. Bayesian mixed hidden markov models: a multi-level approach to modeling categorical outcomes with differential misclassification. Stat Med. 2014;33(8):1395-1408.
Domínguez-Hüttinger E, Christodoulides P, Miyauchi K, et al. Mathematical modeling of atopic dermatitis reveals “double-switch” mechanisms underlying 4 common disease phenotypes. J Allergy Clin Immunol. 2017;139(6):1861-1872.e7.
Christodoulides P, Hirata Y, Dominguez-Huttinger E, et al. Computational design of treatment strategies for proactive therapy on atopic dermatitis using optimal control theory. Philos Trans A Math Phys Eng Sci. 2017;375(2096):20160285.
Langan SM, Silcocks P, Williams HC. What causes flares of eczema in children? Br J Dermatol. 2009;161(3):640-646.
Thomas KS, Dean T, O’Leary C, et al. A randomised controlled trial of ion-exchange water softeners for the treatment of eczema in children. PLoS Med. 2011;8(2):e1000395.
Carpenter B, Gelman A, Hoffman MD, et al. Stan : a probabilistic programming language. J Stat Softw. 2017;76(1):1-32.
Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Stat Sci. 1992;7(4):457-472.
Steen N, Hutchinson A, Mccoll E, et al. Development of a symptom based outcome measure for asthma. BMJ. 1994;309(6961):1065.
Charman CR, Venn AJ, Williams HC. The patient-oriented eczema measure development and initial validation of a new tool for measuring atopic eczema severity from the patients’ perspective. Arch Dermatol. 2014;140:1513-1519.
Tofte S, Graeber M, Cherill R, Omoto M, Thurston M, Hanifin JM. Eczema area and severity index (EASI): A new tool to evaluate atopic dermatitis. J Eur Acad Dermatology Venereol. 1998;11:S197.
Stalder JF, Taïeb A, Atherton DJ, et al. Severity scoring of atopic dermatitis: The SCORAD index: Consensus report of the european task force on atopic dermatitis. Dermatology. 1993;186(1):23-31.
Eyerich K, Brown SJ, Perez White BE, et al. Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council. J Allergy Clin Immunol. 2019;143(1):36-45.
Tanaka G, Domínguez-Hüttinger E, Christodoulides P, Aihara K, Tanaka RJ. Bifurcation analysis of a mathematical model of atopic dermatitis to determine patient-specific effects of treatments on dynamic phenotypes. J Theor Biol. 2018;448:66-79.