Association of Pregnancy With Coronavirus Cytokine Storm: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SAR2-COV-2) that identified first in Wuhan, China, in December 2019, rapidly spreading to the rest of the globe, becoming a pandemic. Some studies have eluded to an association between pregnancy status and severe COVID-19 cytokine storm, some, in contrast, have demonstrated such. The aim of the current study was to find the relationship between pregnancy status and clinical COVID-19 severity characterized by cytokine storm through a systematic review and meta-analysis approach. Methods We searched Google Scholar, PubMed, Scopus, Web of Science, and Embase databases to identify clinical studies suitable for inclusion in this meta-analysis. Studies reporting pregnancy status and comparing the COVID-19 severity cytokine storm outcome were included. The COVID-19 severity characterized by cytokine storm was described using parameters such as; Intensive Care Unit Admission, Invasive Mechanical Ventilation, Mechanical Ventilation, Hospital Admission, Pro and Inflammatory cytokine levels, consolidation on chest CT scan, pulmonary infiltration, extreme fevers as characteristic of cytokine storm, syndromic severity, higher neutrophil count indicative of cytokine storm and severe COVI-19 presentation. Results A total of 17 articles detailing 840332 COVID-19 women were included. Our meta-analysis revealed a relationship between positive pregnancy status and severe COVID-19 cytokine storm case (random effect model, OR=2.47; 95% CI: 1.63-3.73; P < 0.0001), with a cumulative incidence of 6432 (14.1%) among the pregnant women with COVID-19 and 24352 (3.1%) among the non-pregnant women with COVI-19. Further to this, we found that the sub-analysis between Single Centre and Multiple Centre studies demonstrated seemingly the same as heterogeneity (I 2 = 72 and (I 2 = 98), respectively. Sensitivity analysis on each sub-group revealed that pregnancy was significantly related to severe COVID-19 with cytokine storm from single Centre studies, (fixed effect model, OR= 3.97; 95% CI: 2.26-6.95; P< 0.00001) with very low heterogeneity (I 2 = 2 %; P = 0.42). Conclusion Being pregnant is clearly associated with experiencing a severe COVID-19 characterized by a cytokine storm. The SARS-COV-2 epidemic should serve as an impetus for pregnant women diagnosed with COVID-19, and map out salient risk factors associated with its severity. The trial is registered with the International Prospective Register of Systematic Reviews (PROSPERO) CRD42021242011.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".