Predictors of nonresponse to dupilumab in patients with atopic dermatitis: A machine learning analysis.


Journal

Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology
ISSN: 1534-4436
Titre abrégé: Ann Allergy Asthma Immunol
Pays: United States
ID NLM: 9503580

Informations de publication

Date de publication:
09 2022
Historique:
received: 14 03 2022
revised: 11 05 2022
accepted: 23 05 2022
pubmed: 1 6 2022
medline: 24 8 2022
entrez: 31 5 2022
Statut: ppublish

Résumé

Many patients with atopic dermatitis (AD) have a suboptimal response to systemic therapy. This study assessed predictors of nonresponse to dupilumab in patients with AD. Data (April 2017 through June 2019) for patients aged 12 years and above with AD (International Classification of Diseases-9/10-Clinical Modification: 691.8/L20.x) who initiated dupilumab on or after April 1, 2017 (index date) were collected from an electronic health record and insurance claims database. Nonresponse indicators (dupilumab discontinuation, addition of another systemic therapy or phototherapy, addition of a high-potency topical corticosteroid, AD-related hospital visit, AD-related emergency department visit, incident skin infection) were predicted from available demographic and clinical variables using machine learning. Among 419 patients (mean age: 45 years), 145 (35%) experienced at least 1 indicator of nonresponse in the 6-month postindex period. In patients with at least 1 indicator, the most common was dupilumab discontinuation (47% [68/145]). Of note, this analysis could not capture nonmedical reasons for dupilumab discontinuation (eg, cost, access). The most common predictors of nonresponse were a claim for ibuprofen (in 69% of patients with a nonresponse indicator) and a Quan-Charlson Comorbidity Index value of 3 to 4 (59%). Systemic dupilumab therapy for AD can be associated with a relatively high prevalence of nonresponse indicators. Factors associated with these indicators-that is, predictors of nonresponse-may be used to optimize disease management.

Sections du résumé

BACKGROUND
Many patients with atopic dermatitis (AD) have a suboptimal response to systemic therapy.
OBJECTIVE
This study assessed predictors of nonresponse to dupilumab in patients with AD.
METHODS
Data (April 2017 through June 2019) for patients aged 12 years and above with AD (International Classification of Diseases-9/10-Clinical Modification: 691.8/L20.x) who initiated dupilumab on or after April 1, 2017 (index date) were collected from an electronic health record and insurance claims database. Nonresponse indicators (dupilumab discontinuation, addition of another systemic therapy or phototherapy, addition of a high-potency topical corticosteroid, AD-related hospital visit, AD-related emergency department visit, incident skin infection) were predicted from available demographic and clinical variables using machine learning.
RESULTS
Among 419 patients (mean age: 45 years), 145 (35%) experienced at least 1 indicator of nonresponse in the 6-month postindex period. In patients with at least 1 indicator, the most common was dupilumab discontinuation (47% [68/145]). Of note, this analysis could not capture nonmedical reasons for dupilumab discontinuation (eg, cost, access). The most common predictors of nonresponse were a claim for ibuprofen (in 69% of patients with a nonresponse indicator) and a Quan-Charlson Comorbidity Index value of 3 to 4 (59%).
CONCLUSION
Systemic dupilumab therapy for AD can be associated with a relatively high prevalence of nonresponse indicators. Factors associated with these indicators-that is, predictors of nonresponse-may be used to optimize disease management.

Identifiants

pubmed: 35640774
pii: S1081-1206(22)00493-8
doi: 10.1016/j.anai.2022.05.025
pii:
doi:

Substances chimiques

Antibodies, Monoclonal, Humanized 0
dupilumab 420K487FSG

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

354-359.e5

Commentaires et corrections

Type : CommentIn

Informations de copyright

Copyright © 2022 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Auteurs

Jashin J Wu (JJ)

Dermatology Research and Education Foundation, Irvine, California.

Chih-Ho Hong (CH)

Department of Dermatology and Skin Science and Probity Medical Research, University of British Columbia, Surrey, British Columbia, Canada.

Joseph F Merola (JF)

Division of Rheumatology and Immunology, Department of Dermatology and Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.

David Gruben (D)

Global Biostatistics and Data Management, Pfizer Inc, Groton.

Erman Güler (E)

Inflammation & Immunology, Pfizer Ltd, Istanbul, Turkey.

Claire Feeney (C)

Inflammation & Immunology, Pfizer Ltd, Surrey, United Kingdom.

Ankur Bhambri (A)

Inflammation & Immunology, Pfizer Inc, Collegeville, Pennsylvania.

Daniela E Myers (DE)

Patient and Health IMpact, Pfizer Inc, Collegeville, Pennsylvania.

Marco DiBonaventura (M)

Patient and Health Impact, Pfizer Inc, New York, New York. Electronic address: marco.dibonaventura@pfizer.com.

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Classifications MeSH