Predictors of recurrent thrombosis and anticoagulant-related bleeding in patients with cancer
Bibliographic record
Abstract
9565 Background: Anticoagulant therapy is treatment of choice for cancer-associated thrombosis. However, the risk of symptomatic recurrent thrombosis (RT) is approximately 9% on low molecular weight heparin (LMWH) and 17% on vitamin K antagonist (VKA) therapy, while the risk of anticoagulant-related major bleeding is about 5%. Given such complications are associated with morbidity and increased resource utilization, prognostic factors that identify patients at high risk for RT or bleeding would be useful for individualizing therapy. We performed a post-hoc analysis of the CLOT study (N Eng J Med 2003;349,146–53) for predictors of RT and bleeding in patients with proximal deep vein thrombosis (DVT) or pulmonary embolism (PE) who were randomized to receive either dalteparin LMWH or VKA for 6 months. Methods: Cox proportional hazards modeling analyses were performed using prospectively collected data from the CLOT study database. Potential baseline factors associated with RT and bleeding examined in the models were identified a priori based on published literature. Factors for RT were: dalteparin, age, gender, ECOG status, smoking status, presence of metastases, tumor site, history of DVT or PE, recent surgery, cancer treatment and body mass index. Factors for bleeding were: dalteparin, age, gender, ECOG, tumor type, major surgery, cancer treatment, platelet count, body mass index, creatinine, and concurrent use of antiplatelet agents. Results: Data from 673 cancer patients were available for the analysis. There were 80 patients with RT, 31 with major bleeds and 77 with minor bleeds. Three statistically significant predictors for RT were identified (with their hazard ratio and corresponding 95% CI): dalteparin (0.52; 0.32–0.82), every 10 year increase in age (0.77; 0.66–0.90) and presence of metastases (2.59; 1.20–6.60). Of the tumour sites investigated, lung (3.51; 1.62–7.62) and unknown primaries (3.63; 1.36–1.90) were predictive of RT. None of the factors examined in the models were found to be predictive of bleeding. Conclusions: Baseline factors may identify cancer patients with a higher risk of RT despite anticoagulant therapy. The risk of anticoagulant-related bleeding is not predictable at treatment onset. [Table: see text]
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".