Anticoagulation practice patterns in COVID‐19: A global survey
Bibliographic record
Abstract
Background: Best practice for prevention, diagnosis, and management of venous thromboembolism (VTE) in patients with coronavirus disease 2019 (COVID-19) is unknown due to limited published data in this population. Objectives: We aimed to assess current global practice and experience in management of COVID-19-associated coagulopathy to identify information to guide prospective and randomized studies. Methods: Physicians were queried about their current approach to prophylaxis, diagnosis, and treatment of VTE in patients with COVID-19 using an online survey tool distributed through multiple international organizations between April 10 and 14, 2020. Results: Five hundred fifteen physicians from 41 countries responded. The majority of respondents (78%) recommended prophylactic anticoagulation for all hospitalized patients with COVID-19, with most recommending use of low-molecular-weight heparin or unfractionated heparin. Significant practice variation was found regarding the need for dose escalation of anticoagulation outside the setting of confirmed or suspected VTE. Respondents reported the use of bedside testing when unable to perform standard diagnostic imaging for diagnosis of VTE. Two hundred ninety-one respondents reported observing thrombotic complications in their patients, with 64% noting that the complication was pulmonary embolism. Of the 44% of respondents who estimated incidence of thrombosis in patients with COVID-19 in their hospital, estimates ranged widely from 1% to 50%. One hundred seventy-four respondents noted bleeding complications (34% minor bleeding, 14% clinically relevant nonmajor bleeding, and 12% major bleeding). Conclusion: Well-designed epidemiologic studies are urgently needed to understand the incidence and risk factors of VTE and bleeding complications in patients with COVID-19. Randomized clinical trials addressing use of anticoagulation are also needed.
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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 | 0.011 | 0.466 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".