Thromboembolic Disease in Patients With Cancer and COVID-19: Risk Factors, Prevention and Practical Thromboprophylaxis Recommendations–State-of-the-Art
Bibliographic record
Abstract
Cancer and COVID-19 are both well-established risk factors predisposing to thrombosis. Both disease entities are correlated with increased incidence of venous thrombotic events through multifaceted pathogenic mechanisms involving the interaction of cancer cells or SARS-CoV2 on the one hand and the coagulation system and endothelial cells on the other hand. Thromboprophylaxis is recommended for hospitalized patients with active cancer and high-risk outpatients with cancer receiving anticancer treatment. Universal thromboprophylaxis with a high prophylactic dose of low molecular weight heparins (LMWH) or therapeutic dose in select patients, is currentlyindicated for hospitalized patients with COVID-19. Also, prophylactic anticoagulation is recommended for outpatients with COVID-19 at high risk for thrombosis or disease worsening. However, whether there is an additive risk of thrombosis when a patient with cancer is infected with SARS-CoV2 remains unclear In the current review, we summarize and critically discuss the literature regarding the epidemiology of thrombotic events in patients with cancer and concomitant COVID-19, the thrombotic risk assessment, and the recommendations on thromboprophylaxis for this subgroup of patients. Current data do not support an additive thrombotic risk for patients with cancer and COVID-19. Of note, patients with cancer have less access to intensive care unit care, a setting associated with high thrombotic risk. Based on current evidence, patients with cancer and COVID-19 should be assessed with well-established risk assessment models for medically ill patients and receive thromboprophylaxis, preferentially with LMWH, according to existing recommendations. Prospective trials on well-characterized populations do not exist.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".