Cost-effectiveness of apixaban vs. aspirin for the reduction of thrombo-embolism in high-risk patients with device-detected atrial fibrillation: insights from the ARTESiA trial
Bibliographic record
Abstract
AIMS: Apixaban was superior to aspirin for the prevention of stroke or systemic embolism in participants with subclinical atrial fibrillation (SCAF) in the Apixaban for the Reduction of Thromboembolism in Patients With Device-Detected Subclinical Atrial Fibrillation trial. This was especially true for those with CHA2DS2-VASc score > 4. Understanding the cost-effectiveness of treating SCAF is important for decision-makers. METHODS AND RESULTS: Canadian, UK, German, and US direct healthcare costs [in 2023 US dollars (USD)] were applied to hospitalized events (including strokes and bleeds) and study drugs for all participants with a CHA2DS2-VASc score > 4 to determine the mean cost per participant during the trial (mean follow-up 3.5 years). A daily cost of $0.63, $0.11, $2.26, and $6.06 for apixaban in Canada, the UK, Germany, and the USA was used. If in-trial results were not cost-saving (below $0), the prospective plan was to perform a lifetime cost-effectiveness analysis using a Markov model and a willingness-to-pay of 50 000 USD per quality-adjusted life year (QALY). After considering the cost of study medication and clinical events over 3.5 years, apixaban was dominant (cost-saving and more effective) in Canada (-$2301) and the UK (-$902) but cost more in Germany and the USA ($600 and $1990, respectively). Over a lifetime, treatment with apixaban produced a net gain of 0.107 QALYs, but with costs in both Germany ($2623 more) and the USA ($9110 more), yielding an incremental cost-effectiveness ratio of $24 514 per QALY for Germany and $85 140 for the USA. CONCLUSION: In patients with SCAF and a CHA2DS2-VASc score > 4, apixaban is cost saving in Canada and the UK and cost-effective in Germany. Apixaban was not cost-effective in the USA under the base cost assumption but would be cost-effective at a daily cost of $4.35 and cost saving at $3.59.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".