Adverse outcomes among pregnant women with <scp>COVID</scp> ‐19 according to hospitalization status: A prospective individual participant data meta‐analysis in Europe and North America
Bibliographic record
Abstract
Abstract Background Understanding the varied impact of COVID‐19 severity on pregnancy outcomes is crucial for informed clinical management and targeted interventions. Objective To evaluate the impact of COVID‐19 on pregnancy outcomes, distinguishing between pregnant women managed in primary care and those requiring hospitalization. Search Strategy Regulatory authorities actively promoted global cooperation on COVID‐19's impact during pregnancy. Data were obtained through these regulatory bodies and direct researcher communication rather than through systematic searches. Selection Criteria Data sources required secondary population‐based data to identify pregnancies with COVID‐19, along with hospitalization, diagnostic and medication codes. Eligibility for the meta‐analysis was determined through protocol evaluation and researcher consultations. Data Collection and Analysis PRISMA‐IPD and Cochrane guidelines for prospective meta‐analysis were followed. Protocols and definitions were standardized across sources, and a common R script was developed. Initially, crude and adjusted relative risks (aRR) with 95% confidence intervals (CI) were calculated to assess adverse outcomes in pregnant women with and without COVID‐19 in each data source. Estimates were stratified by trimester at infection and hospitalization status. Subsequently, data were pooled using a random‐effects meta‐analysis. Main Results Data from 10 sources across seven countries contributed to the meta‐analysis, including 86 210 pregnant women diagnosed with COVID‐19, of whom 4.4% were hospitalized. Non‐hospitalized pregnant women with COVID‐19 had no increased risks of adverse outcomes compared to pregnant women without COVID‐19. However, hospitalized women with COVID‐19 in each trimester had higher risks of cesarean section, preterm birth, and LBW compared to pregnant women without COVID‐19. Hospitalization due to COVID‐19 in the third trimester was associated with increased risk of stillbirth (aRR 5.90, 95% CI: 2.22–15.71, I 2 = 0%). First‐trimester hospitalizations due to COVID‐19 did not show heightened risks of GDM (aRR 2.08, 95% CI: 0.93–4.64, I 2 = 65%), pre‐eclampsia (aRR 1.79, 95% CI: 0.48–6.66, I 2 = 71%), or major congenital anomalies (aRR 1.30, 95% CI: 0.55–3.06, I 2 = 0%). Conclusions and Relevance COVID‐19 requiring hospitalization is associated with adverse pregnancy outcomes, emphasizing the need to prevent severe illness during pregnancy. This study also highlights the importance of international collaboration for gathering pregnancy data and shows that building global research networks is essential for responding to future health crises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".