Pre-existing COPD is associated with an increased risk of mortality and severity in COVID-19: a rapid systematic review and meta-analysis
Bibliographic record
Abstract
Objectives: The objective of this systematic review and meta-analysis was to investigate COVID-19 mortality and severity among patients with pre-existing COPD. Methods: We performed systematic searches in Ovid Medline, Embase via Ovid, PubMed, and Scopus from 15 December 2019 to 7 July 2020. Studies which reported the association and presented data on risk estimate (Hazard Ratio [HR]) with 95% confidence intervals (95%CIs) were extracted. A random-effects model was used to obtain the pooled estimates, and a pooled Risk Ratio (RR) was calculated. Study quality was assessed using a modified version of the Newcastle–Ottawa Scale. Results: Our meta-analysis showed an increased likelihood of mortality in COVID-19 patients with pre-existing COPD (RR 3.18, 95% CI 2.11–4.80, HR 1.90, 95%CI 1.11–3.26). Furthermore, the pooled estimate for the association between pre-existing COPD and severity due to COVID-19 was also significant (RR 3.63, 95%CI 2.48–5.31). Males had an increased risk of mortality (RR 1.20, 95%CI 1.12–1.29) compared to females. Conclusion: We found that patients with pre-existing COPD had more than 3 times higher risk of mortality and severe COVID-19. There is a need to identify patients with pre-existing COPD during the pandemic so that early interventions can be aimed at this group of patients.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.045 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".