Venous and arterial thrombosis in COVID-19: An updated narrative review
Bibliographic record
Abstract
Hospitalized patients with coronavirus disease 2019 (COVID-19), particularly those admitted to the intensive care unit (ICU) are at high risk of morbidity and mortality. Several observational studies have described hemostatic derangements and thrombotic complications in patients with COVID-19. The aim of this review article is to summarize the current evidence on pathologic findings, pathophysiology, coagulation and hemostatic abnormalities, D-dimer’s role in prognostication epidemiology and risk factors of thrombotic complications, and the role of prophylactic and therapeutic anticoagulation in patients with COVID-19. While existing evidence is limited in quality, COVID-19 appears to increase micro-and macro-vascular thrombosis rates in hospitalized and critically ill patients, which may contribute to the burden of disease. D-dimer can be used for risk stratification of hospitalized patients, but its role to guide anticoagulation therapy remains unclear. Evidence of higher quality is needed to address the role of therapeutic anticoagulation or high-intensity venous thromboembolism prophylaxis in COVID-19 patients. The prevalence of venous thromboembolism (VTE) in hospitalized COVID-19 patients is high, therefore, clinicians should have a high index of suspicion. The pathophysiology of thrombosis is likely related to a combination of SARS-CoV-2 direct endothelial injury and dysregulated inflammation causing coagulation activation. The current evidence on the value of D-dimer guided therapy is limited. The rate of VTE post-hospital discharge is very low, supporting the safety of current discharge practice without VTE prophylaxis in most patients. The role of higher-intensity VTE prophylaxis or therapeutic anticoagulation in critically ill COVID-19 patients without documented or suspected VTE remains uncertain. Therapeutic anticoagulation in hospitalized non-critically ill patients with COVID-19 may improve outcomes but more research is warranted.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".