Cost-effectiveness analysis of dabigatran and anticoagulation monitoring strategies of vitamin K antagonist
Bibliographic record
Abstract
BACKGROUND: Vitamin K antagonists are commonly used for the prevention of thromboembolic events. Patient self-monitoring of vitamin K antagonists has proved superior to usual care. Dabigatran has been shown, relative to warfarin, to reduce thromboembolic events without increasing bleeding. METHODS: We constructed a Markov model to compare vitamin K self-monitoring strategies to dabigatran including effectiveness and costs of monitoring and complications (thromboembolism and major bleeding). The model was used to project the incidence of these complications, life years, quality-adjusted life years, and health system costs with anticoagulant treatment throughout life. The analysis was conducted from the health system perspective and from the societal perspective. RESULTS: Low quality evidence suggests that self-monitoring is at least as effective as dabigatran for the outcomes of thrombosis, bleeding and death. Moderate quality evidence that patient self-monitoring is more effective than other forms of monitoring degree of anticoagulation with vitamin K antagonists, reducing the relative risk of thromboembolism by 41% and death by 34%. The cost per quality adjusted year gained relative to other warfarin monitoring strategies is well below 30,000 € in the short term, and is a dominant alternative from the fourth year. In comparison with dabigatran, the lower annual cost and its equivalence in terms of effectiveness made self-monitoring the dominant option. These results were confirmed in the probabilistic sensitivity analysis. CONCLUSIONS: We have moderate quality evidence that self-monitoring of vitamin K antagonists is a cost-effective alternative compared with hospital and primary care monitoring, and low quality evidence, compared with dabigatran. Our analyses contrast with the available cost analysis of dabigatran and usual care of anticoagulated patients.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".