Venous and Arterial Thrombosis in Ambulatory and Discharged COVID-19 Patients: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Introduction Venous and arterial thromboses are frequently observed complications in patients with severe novel coronavirus disease 2019 (COVID-19) infection who require hospital admission. In this study, we evaluate the epidemiology of venous and arterial thrombosis events in ambulatory and postdischarge patients with COVID-19 infection. Materials and Method EMBASE and MEDLINE were searched up to July 21, 2021, in addition to other sources. We included studies that assessed the epidemiology of venous and arterial thrombosis events in ambulatory and postdischarge COVID-19 patients. Results A total of 16 studies (102,779 patients) were identified. The overall proportion of venous thromboembolic events in all patients, that is, ambulatory and postdischarge, was 0.80% (95% confidence interval [CI]: 0.44–1.28), 0.28% (95% CI: 0.07–0.64), and 1.16% (95% CI: 0.69–1.74), respectively. Arterial events occurred in 0.75% (95% CI: 0.27–1.47) of all patients, 1.45% (95% CI: 1.10–1.86) of postdischarge patients, and 0.23% (95% CI: 0.019–0.66) of ambulatory patients. The pooled incidence rate estimates per 1,000 patient-days for VTE events were 0.06 (95% CI: 0.03–0.08) and 0.12 (95% CI: 0.07–0.19) for outpatients and postdischarge, respectively, whereas for arterial events were 0.10 (95% CI: 0–0.30) and 0.26 (95% CI: 0.16–0.37). Conclusion This study found a low risk of venous and arterial thrombi in ambulatory and postdischarge COVID-19 patients, with a higher risk in postdischarge patients compared with ambulatory patients. This suggests that regular universal thromboprophylaxis in these patient populations is probably not necessary.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".