Characterizing the Association Between Asthma, and COVID-19 severity
Bibliographic record
Abstract
BACKGROUND: Despite heterogeneity in outcomes in patients infected with SARS-CoV-2, few studies have investigated the risks of developing respiratory failure and death in COVID-19 patients with pre-existing asthma. This study evaluated the impact of asthma on patients with COVID-19. METHODS: This was a retrospective cohort study using data from the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) registry. CCEDRRN collected data from consecutive patients presenting to one of 52 participating emergency departments being tested for severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). We included patients from March 1st, 2020, to December 31st, 2021. Those who were ≥18 years and testing positive for SARS CoV-2 were considered for inclusion. We excluded those who were below the age of 18 and asymptomatic patients. The primary outcome of interest was a composite of intubation or death in the hospital and the secondary outcome is severe COVID-19 as defined by the WHO. Multivariable modified Poisson regression was used to assess the association between asthma and the composite outcome while adjusting for possible confounding variables, including inhaled corticosteroid (ICS) use. RESULTS: There were 38,139 patients who met the study inclusion criteria. Among these 2,826 patients had asthma (7.41%). We found no evidence to suggest an association between asthma and intubation or death in the hospital (RR: 0.97; 95% CI: 0.86, 1.1). Patients who were 80+ years were the highest risk age group developing the primary outcome (RR: 10.54; 95% CI: 7.01, 15.85) compared with the reference group 18-29 years. ICS had a higher risk of developing the primary outcome compared to non-ICS users (RR: 1.12; 95% CI: 1.01, 1.25). CONCLUSION: No evidence was found to suggest that asthma is associated with death or intubation in the hospital or the development of severe COVID-19 among emergency department patients infected with SARS-CoV-2.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".