Growth Differentiation Factor-15, High-Sensitivity Cardiac Troponin T, and N-Terminal pro-B-type Natriuretic Peptide for Predicting Risk of Venous Thromboembolism in Ambulatory Cancer Patients Receiving Chemotherapy
Bibliographic record
Abstract
Abstract Growth differentiation factor-15 (GDF-15), high-sensitivity cardiac troponin T (hs-TnT), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) are associated with increased risk of venous thromboembolism (VTE) in noncancer patients. However, the performance of these biomarkers in cancer patients is unknown. Our objective was to assess performance of these biomarkers in predicting VTE in cancer patients at intermediate to high risk for VTE (Khorana Score ≥ 2). We used 1-month plasma samples from AVERT trial patients to determine if GDF-15, NT-proBNP, and hs-TnT levels are associated with VTE incidence between 1 and 7 months from the start of chemotherapy. The minimal Euclidean distance of the receiver operating characteristic curve was used to derive optimal cut-offs for GDF-15 and NT-proBNP given there was no evidence of a commonly used cut-off. Logistic and Fine and Gray competing risk regression analyses were used to calculate odds ratios (ORs) and subdistribution hazard ratios, respectively, while adjusting for age, sex, anticoagulation, and antiplatelet therapy. We tested in two groups: all patients (n = 476, Model 1) and all patients with nonprimary brain cancers (n = 454, Model 2). In models 1 and 2, GDF-15 ≥2,290.9 pg/mL had adjusted ORs for VTE of 1.65 (95% confidence interval [CI]: 0.89–3.08), and 2.28 (95% CI: 1.28–4.09), respectively. hs-TnT ≥14.0 pg/mL was associated with higher odds of VTE in models 1 and 2 (adjusted ORs: 2.26 [95% CI: 1.40–3.65] and 2.03 [95% CI: 1.07–3.84], respectively). For NT-proBNP, levels ≥183.5 pg/mL were not associated with VTE. Similar results were observed in the Fine and Gray analysis. Our results indicate that increased GDF-15 and hs-TnT levels predicted increased VTE risk.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".