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Record W4297460252 · doi:10.1111/jdv.18613

The effects of systemic immunomodulatory treatments on <scp>COVID</scp>‐19 outcomes in patients with atopic dermatitis: Results from the global <scp>SECURE‐AD</scp> registry

2022· article· en· W4297460252 on OpenAlexaff
Annelie H. Musters, Conor Broderick, David Prieto‐Merino, Andrea Chiricozzi, Giovanni Damiani, Ketty Peris, Sandipan Dhar, Abhishek De, Esther E. Freeman, B.W.M. Arents, Tim Burton, Angela L. Bosma, Ching‐Chi Chi, G. Fletcher, Aaron M. Drucker, Kenji Kabashima, Emilie F. de Monchy, Maitreyee Panda, Dmitri Wall, Christian Vestergaard, E. Mahé, Laura Bonzano, Leila Kattach, Maddalena Napolitano, María Fernanda Ordóñez, Eva Haufe, Cataldo Patruno, Alan D. Irvine, Phyllis I. Spuls, Carsten Flohr

Bibliographic record

VenueJournal of the European Academy of Dermatology and Venereology · 2022
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsWomen's College HospitalUniversity of Toronto
FundersUniversiteit van AmsterdamNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research
KeywordsMedicineAtopic dermatitisCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakDermatologyVirologyInternal medicineDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Limited data are available on the effects of systemic immunomodulatory treatments on COVID-19 outcomes in patients with atopic dermatitis (AD). OBJECTIVE: To investigate COVID-19 outcomes in patients with AD treated with or without systemic immunomodulatory treatments, using a global registry platform. METHODS: Clinicians were encouraged to report cases of COVID-19 in their patients with AD in the Surveillance Epidemiology of Coronavirus Under Research Exclusion for Atopic Dermatitis (SECURE-AD) registry. Data entered from 1 April 2020 to 31 October 2021 were analysed using multivariable logistic regression. The primary outcome was hospitalization from COVID-19, according to AD treatment groups. RESULTS: 442 AD patients (mean age 35.9 years, 51.8% male) from 27 countries with strongly suspected or confirmed COVID-19 were included in analyses. 428 (96.8%) patients were treated with a single systemic therapy (n = 297 [67.2%]) or topical therapy only (n = 131 [29.6%]). Most patients treated with systemic therapies received dupilumab (n = 216). Fourteen patients (3.2%) received a combination of systemic therapies. Twenty-six patients (5.9%) were hospitalized. No deaths were reported. Patients treated with topical treatments had significantly higher odds of hospitalization, compared with those treated with dupilumab monotherapy (odds ratio (OR) 4.65 [95%CI 1.71-14.78]), including after adjustment for confounding variables (adjusted OR (aOR) 4.99 [95%CI 1.4-20.84]). Combination systemic therapy which did not include systemic corticosteroids was associated with increased odds of hospitalization, compared with single agent non-steroidal immunosuppressive systemic treatment (OR 8.09 [95%CI 0.4-59.96], aOR 37.57 [95%CI 1.05-871.11]). Hospitalization was most likely in patients treated with combination systemic therapy which included systemic corticosteroids (OR 40.43 [95%CI 8.16-207.49], aOR 45.75 [95%CI 4.54-616.22]). CONCLUSIONS: Overall, the risk of COVID-19 complications appears low in patients with AD, even when treated with systemic immunomodulatory agents. Dupilumab monotherapy was associated with lower hospitalization than other therapies. Combination systemic treatment, particularly combinations including systemic corticosteroids, was associated with the highest risk of severe COVID-19.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.236
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2022
Admission routes1
Has abstractyes

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