Effect of Preexisting Asthma on the Risk of ICU Admission, Intubation, and Death from COVID-19: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background. The Centers for Disease Control and Prevention (CDC) identifies asthma as a comorbidity in COVID-19 that increases the risk of severity and death. However, research has shown that asthma is not associated with increased severity and death, thus making the consequences of asthma in COVID-19 unclear. Methods. We searched the electronic databases PubMed, WHO COVID-19 database, and Taylor and Francis Online for studies that compared the medical outcomes of COVID-19 between patients with and without asthma, from the emergence of SARS-CoV-2 in December 2019 to the 3rd of September 2021, excluded duplicates, reviews, editorials, and case reports, and screened the titles, abstracts, and full texts. The quality of the included studies was assessed using the Newcastle–Ottawa Scale (NOS) for nonrandomized studies. Rates of intensive care unit (ICU) admission, intubation, and death among patients with and without asthma were compiled and meta-analysis was conducted using a random-effects model. Results. Nineteen studies with a total of 289,449 participants met the inclusion criteria. COVID-19 patients with asthma had no significant association with increased risk of ICU admission, intubation, and death as compared with those without asthma ((odds ratio (OR) = 1.25, confidence interval (CI) = 0.90–1.74, I2 = 82%, X2 = 55.13, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> <mo><</mo> <mn>0.01</mn> </math> ), (OR = 0.89, CI = 0.59–1.34, I2 = 91%, X2 = 110.82, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>p</mi> <mo><</mo> <mn>0.01</mn> </math> ), and (OR = 0.90, 95% CI = 0.63–1.27, I2 = 88%, X2 = 146.96, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>p</mi> <mo><</mo> <mn>0.01</mn> </math> )), respectively. Conclusion. Preexisting asthma did not significantly increase the risk of poorer prognosis and death from COVID-19.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.048 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".