Circulating Blood Biomarkers and Risk of Venous Thromboembolism in Cancer Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Cancer patients have an increased risk of venous thromboembolism (VTE). Currently, the availability of highly discriminatory prediction models for VTE in cancer patients is limited. The implementation of biomarkers in prediction models might lead to refined VTE risk prediction. In this systematic review and meta-analysis, we aimed to evaluate candidate biomarkers and their association with cancer-associated VTE. Methods We searched Medline, EMBASE, and Cochrane Central for studies that evaluated biomarkers in adult cancer patients from inception to September 2022. We included studies reporting on VTE after a cancer diagnosis with biomarker measurements performed at a defined time point. Median/mean differences (for continuous measures) and odds ratios (for dichotomous measures) with 95% confidence intervals were estimated and pooled using random-effects models. Results We included 113 studies in the systematic review. Of these, 50 studies were included in the meta-analysis. We identified two biomarkers at cancer diagnosis (factor VIII and time to peak thrombin), three biomarkers pre-chemotherapy (D-dimer, fibrinogen, and mean platelet volume), and one biomarker preoperatively (platelet count) that had significant median or mean differences. Additionally, we found that hemoglobin <100 g/L and white blood count >11 × 109/L were significantly associated with future VTE risk only when measured at cancer diagnosis. Pre-chemotherapy neutrophil-to-lymphocyte ratio ≥3 and preoperative platelet count ≥400 × 109/L were also found to be associated with future VTE risk. Conclusion In conclusion, this study identified nine candidate blood biomarkers that may help in optimizing VTE prediction in cancer patients that should be further explored in future studies.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.007 | 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".