Low-molecular-weight heparins for the prevention of recurrent venous thromboembolism in patients with cancer: A systematic literature review of efficacy and cost-effectiveness
Bibliographic record
Abstract
BACKGROUND: Patients with cancer have an elevated risk of venous thromboembolism. Importantly, patients with cancer, who have metastatic disease, renal insufficiency, or are receiving anticancer therapy, have an even higher risk of a recurrent event. Similarly, the risk of recurrent venous thromboembolism is higher than the risk of an initial event. To reduce the risk, extended duration of prophylaxis for up to six months with low-molecular-weight heparins such as dalteparin, enoxaparin, nadroparin, and tinzaparin is recommended by international guidelines. In this paper, the clinical and economic literature is reviewed to provide evidenced based recommendations based on clinical benefit and economic value. METHODS: A systematic review of major databases was conducted from January 1996 to October 2016 for randomized controlled trials evaluating the four distinct low-molecular-weight heparins against a vitamin K antagonists control group for the prevention of recurrent venous thromboembolism in patients with active cancer. This was then followed by the application of the National Institute of Health and Clinical Excellence guidance to assess the quality of all trials that met the inclusion criteria. Finally, the cost-effectiveness literature supporting the value proposition of each product was reviewed. RESULTS: Six randomized trials met the inclusion criteria. There were one, two, and three trials that compared dalteparin, tinzaparin, and enoxaparin to a vitamin K antagonists control group. However, there were no trials for nadroparin in the setting of secondary venous thromboembolism prevention. In addition, only the dalteparin and one of the tinzaparin trials were of high quality and adequately powered. Of the two studies, only the dalteparin trial reported a statistically significant benefit in terms of venous thromboembolism absolute risk reduction when compared to a vitamin K antagonists control group (HR = 0.48; p = 0.002). In addition, there was robust pharmacoeconomic data from Canada, the Netherlands, France, and Austria supporting the cost-effectiveness of dalteparin for this indication. There were no such studies for any of the other agents. CONCLUSIONS: The totality of high-quality clinical and cost-effectiveness data supports the use of dalteparin over other low-molecular-weight heparins for preventing recurrent venous thromboembolism in patients with cancer.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".