Dabigatran versus warfarin under standard or pharmacogenetic-guided management for the prevention of stroke and systemic thromboembolism in patients with atrial fibrillation: a cost/utility analysis using an analytic decision model
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is the most common form of heart arrhythmia and a leading cause of stroke and systemic embolism. Chronic anticoagulation is recommended for preventing those complications. Our study aimed to compare the cost/utility (CU) of three main anticoagulation options: 1) standard warfarin dosing (SD-W) 2) warfarin dosage under the guidance of CYP2C9 and VKORC1 genotyping (GT-W) and 3) dabigatran 150 mg twice a day. METHODS: A Markov state transition model was built to simulate the expected C/U of dabigatran, SD-W and GT-W anticoagulation therapy for the prevention of stroke and systemic thromboembolism in patients with atrial fibrillation over a period of 5 years under the perspective of the public health care system. Model inputs were derived from extensive literature search and government's data bases. Outcomes considered were the number of total major events (thromboembolic and hemorrhagic events), total costs in Canadian dollars (1CAD$ = 1$US), total quality-adjusted life years (QALYs), costs/QALYs and incremental costs/QALYs gained (ICUR). RESULTS: Raw base case results show that SD-W has the lowest C/U ratio. However, the dabigatran option might be considered as an alternative, as its cost per additional QALY gained compared to SD-W is CAD $ 4 765, i.e. less than 50 000, the ICUR threshold generally accepted to adopt an intervention. At the same threshold, GT-W doesn't appear to be an alternative to SD-W. Our results were robust to one-way and multi-way sensitivity analyses. CONCLUSION: SD-W has the lowest C/U ratio among the 3 options. However, dabigatran might be considered as an alternative. GT-W is not C/U and should not currently be recommended for the routine anticoagulotherapy management of AF patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".