Cost‐effectiveness analysis of low‐dose direct oral anticoagulant (DOAC) for the prevention of cancer‐associated thrombosis in the United States
Bibliographic record
Abstract
BACKGROUND: Randomized controlled trials (RCTs) have demonstrated that low-dose direct oral anticoagulants (DOACs), including rivaroxaban and apixaban, may help reduce the incidence of cancer-associated venous thromboembolism (VTE). METHODS: A cost-utility analysis was performed from the health sector perspective using a Markov state-transition model in patients with cancer who are at intermediate-to-high risk for VTE. Transition probability, relative risk, cost, and utility inputs were obtained from a meta-analysis of the RCTs and relevant epidemiology studies. Differences in cost, quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio (ICER) per patient were calculated over a lifetime horizon. One-way, probabilistic, and scenario sensitivity analyses were conducted. RESULTS: In patients with cancer at intermediate-to-high risk for VTE, treatment with low-dose DOAC thromboprophylaxis for 6 months, compared with placebo, was associated with 32 per 1000 fewer VTE and 11 per 1000 more major bleeding episodes over a lifetime. The incremental cost and QALY increases were $1445 and 0.12, respectively, with an ICER of $11,947 per QALY gained. Key drivers of ICER variations included the relative risks of VTE and bleeding as well as drug cost. This strategy was 94% cost effective at the threshold of $50,000 per QALY. The selection of patients with Khorana scores ≥3 yielded the greatest value, with an ICER of $5794 per QALY gained. CONCLUSIONS: Low-dose DOAC thromboprophylaxis for 6 months appears to be cost-effective in patients with cancer who are at intermediate-to-high risk for VTE. The implementation of this strategy in patients with Khorana scores ≥3 may lead to the highest cost-benefit ratio.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".