COVID-19-Associated Pulmonary Embolism: Review of the Pathophysiology, Epidemiology, Prevention, Diagnosis, and Treatment
Bibliographic record
Abstract
COVID-19 is associated with endothelial activation in the setting of a potent inflammatory reaction and a hypercoagulable state. The end result of this thromboinflammatory state is an excess in thrombotic events, in particular venous thromboembolism. Pulmonary embolism (PE) has been of special interest in patients with COVID-19 given its association with respiratory deterioration, increased risk of intensive care unit admission, and prolonged hospital stay. The pathophysiology and clinical characteristics of COVID-19-associated PE may differ from the conventional non-COVID-19-associated PE. In addition to embolic events from deep vein thrombi, in situ pulmonary thrombosis, particularly in smaller vascular beds, may be relevant in patients with COVID-19. Appropriate prevention of thrombotic events in COVID-19 has therefore become of critical interest. Several changes in viral biology, vaccination, and treatment management during the pandemic may have resulted in changes in incidence trends. This review provides an overview of the pathophysiology, epidemiology, clinical characteristics, and risk factors of COVID-19-associated PE. Furthermore, we briefly summarize the results from randomized controlled trials of preventive antithrombotic therapies in COVID-19, focusing on their findings related to PE. We discuss the acute treatment of COVID-19-associated PE, which is substantially similar to the management of conventional non-COVID-19 PE. Ultimately, we comment on the current knowledge gaps in the evidence and the future directions in the treatment and follow-up of COVID-19-associated PE, including long-term management, and its possible association with long-COVID.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".