Venous Thromboembolism, Corticosteroids and COVID-19: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The novel coronavirus disease 2019 (COVID-19) predisposes patients to venous thromboembolism (VTE) due to risk factors, severe infection, and severe inflammatory responses. The objective is to determine the risk of developing VTE after corticosteroid administration during COVID-19 treatment. Using PRISMA reporting guidelines, a review was conducted from inception until 20 September 2020 with MESH terms including “venous thromboembolism” and “covid-19,” using MEDLINE, Scopus, CINAHL Plus, and WHO Global Database. The inclusion criteria included studies with COVID-19 patients aged 18 years and older with VTE diagnosed by duplex ultrasonography or computed tomography pulmonary angiography (CTPA). Exclusion criteria were studies with non COVID-19 patients and non-VTE patients aged less than 18 years. Quality appraisal was conducted of included studies using the Newcastle-Ottawa Scale (NOS). A random-effect model using 95% confidence intervals, and significance of findings was assessed using Review Manager V5.4.We included 12 observational studies with 2801 patients (VTE n = 434; non-VTE; n = 2367). Patients had a higher risk of presenting with VTE when being administered corticosteroids during treatment of COVID-19 (RR = 1.39, 95% CI = 1.10 to 1.77, I2 = 0%). A positive effect size was found (SMD = 1.00, 95% CI = 0.67 to 1.32, I2 = 85%) for D-dimer laboratory values (µg/mL) in the VTE group. While critically ill COVID-19 patients are more likely to require corticosteroid treatment, it may be associated with increased risk of VTE, and poor clinical prognosis. Risk assessment is warranted to further evaluate patients as case-by-case in reducing VTE and worsening clinical outcomes.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".