Venous Thromboembolism in Hospitalized COVID-19 Patients: Systematic Review
Bibliographic record
Abstract
Background Coagulopathy associated with COVID-19 infection and venous thromboembolism (VTE) have emerged as significant contributors to morbidity among patients infected with SARS-CoV-2. Objective We performed a systematic review to estimate VTE incidence in hospitalized patients and to analyze characteristic factors in the VTE cohort. Methods We searched PubMed and Google Scholar using specified title search terms “SARS-CoV-2” or “COVID-19” and “venous thromboembolism” and “anticoagulation” among others to identify peer-reviewed journal articles published between June 22, 2019, and June 22, 2020. Data were systematically extracted and synthesized using Microsoft Excel for analysis. The main outcome was VTE incidence, and measures included patient characteristics, anticoagulation, and clinical outcomes with assessment for associations. Results In total, 14 studies were included comprising 1677 patients. Most patients (n=1306, 82.4%) received anticoagulation (either VTE prophylaxis or treatment). VTE incidence was 26.9% (SE 3.1; 95% CI 20.8-33.1) and was correlated with systematic screening (r2=0.34, P=.03) and study duration (r2=–0.33, P=.03). D-dimer was higher for the VTE cohort (5.62 [SD 0.9] vs 1.43 [SD 0.6]; P<.001). Odds of VTE were higher at the intensive care unit (odds ratio [OR] 6.38, 95% CI 3.67-11.11; P<.001) but lower with anticoagulation (OR 0.58, 95% CI 0.36-0.92; P=.02). Conclusions Despite the utilization of background anticoagulation, VTE incidence was historically high. Future studies are needed to provide additional data to guide optimal VTE prophylaxis and diagnostic strategies.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".