A systematic review of biomarkers among hospitalized patients with COVID‐19 predictive of venous thromboembolism: A communication from the Predictive and Diagnostic Variables Scientific and Standardization Committee of the ISTH
Bibliographic record
Abstract
Background: Thrombosis is reported to occur more often among patients with COVID-19 than otherwise expected in the setting of viral pneumonia and sepsis. Systemic inflammatory biomarkers may be associated with venous thromboembolism (VTE) risk. The ISTH subcommittee on Predictive and Diagnostic Variables in Thrombotic Disease aimed to report the evidence on prognostic biomarkers for VTE in hospitalized patients with COVID-19. Methods: Using a standardized Preferred Reporting Items for Systematic Reviews and Meta-analysis methodology, we conducted a systematic literature review to identify studies reporting prognostic biomarkers for VTE among hospitalized patients with COVID-19. Eligible studies included adults hospitalized with COVID-19 and reported the prognostic associations between any biomarker measured on admission, and the subsequent diagnosis of deep vein thrombosis or pulmonary embolism. Two authors reviewed titles and abstracts, and three authors extracted study data and performed review of bias. Results were displayed descriptively. Meta-analysis was not possible. Results: From the initial 196 identified studies, full-text review was performed for 72 studies. Admission D-dimer levels were associated with VTE during hospitalization in five studies, and elevated platelet count was associated with VTE during hospitalization in one study. The risk of bias ranged from low to high for included studies. Overall, there was a paucity of high-quality prognostic studies. Studies on other biomarkers did not meet the systematic review inclusion criteria. Conclusions: Admission D-dimer was associated with VTE diagnosis during hospitalization for COVID-19; however, prospective validation of this finding is needed to identify optimal D-dimer thresholds to guide VTE prophylaxis measures.
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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 | 0.024 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".