Sex differences in D-dimer and critical illness in patients with COVID-19: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Observed sex differences in COVID-19 outcomes suggest that men are more likely to experience critical illness and mortality. Thrombosis is common in severe COVID-19, and D-dimer is a significant marker for COVID-19 severity and mortality. It is unclear whether D-dimer levels differ between men and women, and the effect of D-dimer levels on disease outcomes remains under investigation. Objectives We aimed to evaluate the sex difference in the D-dimer level among hospitalized patients with COVID-19 and the effect of sex and D-dimer level on disease outcomes. Methods We meta-analyzed articles reporting D-dimer levels in men and women hospitalized for COVID-19, until October 2021, using random effects. Primary outcomes were mortality, critical illness, and thrombotic complications. Results In total, 11,682 patients from 10 studies were analyzed ( N = 5606 men (55.7%), N = 5176 women (44.3%)). Men had significantly higher odds of experiencing mortality (odds ratios (OR) = 1.41, 95% CI: [1.25, 1.59], P ≤ .001, I 2 = 0%) and critical illness (OR = 1.76, 95% CI: [1.43, 2.18], P ≤ .001, I 2 = 61%). The mean D-dimer level was not significantly different between men and women (MD = 0.08, 95% CI: [−0.23, 0.40], P = .61, I 2 = 52%). In the subgroup analysis, men had significantly higher odds of experiencing critical illness compared with women in both the "higher" ( P = .006) and "lower" ( P = .001) D-dimer subgroups. Conclusion Men have significantly increased odds of experiencing poor COVID-19 outcomes compared with women. No sex difference was found in the D-dimer level between men and women with COVID-19. The diversity in D-dimer reporting impacts data interpretation and requires further attention.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".