Venous thromboembolism and bleeding in cancer patients: role of inflammatory and cardiac biomarkers
Bibliographic record
Abstract
BACKGROUND AND AIMS: Patients with cancer have increased risk of venous thromboembolism (VTE) and bleeding. Inflammatory and cardiac biomarkers may predict these complications, but their role remains unclear. This study examined associations between two inflammatory-related markers (C-reactive protein and growth differentiation factor-15) and two cardiac markers [N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T (hs-TnT)] with VTE and clinically relevant bleeding in cancer patients. METHODS: A post hoc analysis of the AVERT trial, which evaluated apixaban for VTE prevention in ambulatory cancer patients with a Khorana score of ≥2, was performed. Biomarkers were measured at baseline and 1 month, with C-reactive protein also at 3 months. Fine and Gray regression, accounting for competing risk of death and adjusted for age and advanced cancer, estimated subdistribution hazard ratios (SHRs) for VTE and clinically relevant bleeding. RESULTS: Of 574 patients, 514 provided baseline samples. One- and 3-month samples were available from 454 and 447, and 378 and 364, patients without prior VTE and bleeding events, respectively. Elevated baseline growth differentiation factor-15 was associated with increased VTE risk [SHR 1.36, 95% confidence interval (CI) 1.01-1.84]. N-terminal pro-B-type natriuretic peptide (SHR 1.44, 95% CI 1.08-1.92) and C-reactive protein (SHR 1.38, 95% CI 1.07-1.76) were linked to bleeding risk. Increasing high-sensitivity troponin T from baseline to 1 month was associated with higher VTE risk (SHR 1.89, 95% CI 1.14-3.16). Nomograms were developed to estimate VTE and clinically relevant bleeding risks. CONCLUSIONS: Select inflammatory-related and cardiac markers were associated with VTE and bleeding risks in cancer patients, which can be determined using developed nomograms. Prospective research is needed to confirm these findings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".