The Burden of Coronavirus Disease 2019 and Its Complications in Patients With Atopic Dermatitis—A Nested Case-Control Study
Bibliographic record
Abstract
BACKGROUND: The burden of coronavirus disease 2019 (COVID-19) among patients with atopic dermatitis (AD) is poorly understood. OBJECTIVES: The aims of the study were to characterize a large cohort of COVID-19-positive adult patients with AD and to identify predictors of COVID-19-associated hospitalization and mortality. METHODS: A population-based nested case-control study was performed. Multivariable logistic regression was used to evaluate odds ratios and 95% confidence intervals of predictors for COVID-19-associated hospitalization and mortality. RESULTS: Of 78,073 adult patients with AD, 3618 (4.6%) tested positive for COVID-19. Subclinical COVID-19 infection occurred in 3368 (93.1%) of COVID-19-positive patients, whereas 123 (3.4%), 46 (1.3%), 55 (1.5%), and 26 (0.7%) patients developed a mild, moderate, severe, and critical disease, respectively. Altogether, 250 patients (6.0%) were hospitalized, and 40 patients (1.1%) died because of COVID-19 complications. Coronavirus disease 2019-associated hospitalization was independently associated with the intake of extended courses of systemic corticosteroids (adjusted odds ratio, 1.96; 95% confidence interval, 1.23-3.14; P = 0.005). None of AD-related variables independently predicted COVID-19-associated mortality. The presence of comorbid metabolic syndrome, chronic obstructive pulmonary disease, chronic renal failure, and depression projected both COVID-19-associated hospitalization and mortality. CONCLUSIONS: Prolonged systemic corticosteroids during the pandemic are associated with increased odds of COVID-19-associated hospitalization and should be avoided in patients with AD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".