Association between D-Dimer levels and mortality in patients with coronavirus disease 2019 (COVID-19): a systematic review and pooled analysis
Bibliographic record
Abstract
Background Several observational studies have reported elevated baseline D-dimer levels in patients hospitalized for moderate to severe coronavirus disease 2019 (COVID-19). These elevated baseline D-dimer levels have been associated with disease severity and mortality in retrospective cohorts. Objectives To review current available data on the association between D-Dimer levels and mortality in patients admitted to hospital for COVID-19. Methods We performed a systematic review of published studies using MEDLINE and EMBASE through 13 April 2020. Two authors independently screened all records and extracted the outcomes. A random effects model was used to estimate the standardized mean difference (SMD) with 95% confidence intervals (CI). Results Six original studies enrolling 1355 hospitalized patients with moderate to critical COVID-19 (391 in the non-survivor group and 964 in the survivor group) were considered for the final pooled analysis. When pooling together the results of these studies, D-Dimer levels were found to be higher in non-survivors than in-survivors. The SMD in D-Dimer levels between non-survivors and survivors was 3.59 μg/L (95% CI 2.79–4.40 μg/L), and the Z-score for overall effect was 8.74 ( P < 0.00001), with a high heterogeneity across studies (I 2 = 95%). Conclusions Despite high heterogeneity across included studies, the present pooled analysis indicates that D-Dimer levels are significantly associated with the risk of mortality in COVID-19 patients. Early integration of D-Dimer testing, which is a rapid, inexpensive, and easily accessible biological test, can be useful to better risk stratification and management of COVID-19 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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".