MétaCan
Menu
← Back to cohort

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

2025· article· en· W7127608316 on OpenAlexaffabout
C Sticherling, Wesley Tong, R D Lopes, C B Granger, Roopinder K. Sandhu, C M Linde, J W Erath, M R Gold, M Alings, W F Mcintyre, J C Nielsen, D J Wright, A P Benz, J S Healey, A Lamy

Bibliographic record

VenueEuropean Heart Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsCanadian VIGOUR CentrePopulation Health Research Institute
Fundersnot available
KeywordsApixabanAtrial fibrillationStroke (engine)Randomized controlled trialAspirinSubclinical infectionIschemic strokeHeart failure

Abstract

fetched live from OpenAlex

Abstract Background The Apixaban for the Reduction of Thrombo-Embolism in Patients with Subclinical Atrial Fibrillation (ARTESiA) trial demonstrated that apixaban, compared to aspirin, prevented stroke or systemic embolism, with the largest effect among patients with a CHA₂DS₂-VASc score >4. We hypothesize that apixaban in patients with a CHA₂DS₂-VASc score >4 will be cost-effective due to cost savings from stroke prevention. Methods Using healthcare perspectives specific to Canada, the UK, Germany and the US, direct healthcare costs (in 2023 USD) were applied to hospitalized events and study drugs used by all CHA₂DS₂-VASc score >4 participants. The daily cost of apixaban was $0.63, $0.11, $2.26, and $6.06 for Canada, the UK, Germany and the US. We determined the mean cost per participant for the full duration of the trial (mean follow-up of 3.5 years). If in-trial results were not cost-saving (below $0), we performed a lifetime cost-effectiveness analysis using a Markov model and assumed a willingness to pay of 50,000 USD per Quality Adjust Life-Year (QALY). The Markov model consisted of five states (Alive, Dead, Discontinued, Rankin 0-2 Post-Stroke and Rankin 3-5 Post-Stroke) with a 3-month cycle length. While alive, participants experienced one of eight events (no event, non-fatal myocardial infarction, non-fatal ischemic stroke, fatal ischemic stroke, non-fatal hemorrhagic stroke, fatal hemorrhagic stroke, major gastrointestinal/ genitourinary bleed, or death) based on data from the ARTESiA trial. Costs and utilities were obtained from national tariffs and published literature. Results During the trial, the cost of events was lower in the apixaban arm in all countries. After taking into consideration the cost of study medication, apixaban was cost saving (dominant strategy) in Canada (-$2301) and the UK (-$902) but cost more in Germany and the US ($600 and $1990 respectively) and thus modelling was required for these two countries. Over a lifetime, apixaban results in 4.995 QALYs compared to 4.888 QALYs for patients taking aspirin; a difference of 0.107 QALYs. During this period participants taking apixaban accrued higher costs than those taking aspirin in both Germany ($2623 more) and the US ($9110 more). This resulted in an incremental cost effectiveness ratio of $24,514 for Germany and $85,140 per QALY for the US. One-way sensitivity analyses of drug and event costs and of event probabilities used in our model identified the cost of apixaban as the only factor impacting cost-effectiveness. In the US, this sensitivity analysis indicates a daily cost of $4.35 is required to achieve a willingness-to-pay of $50,000/QALY. Conclusion In patients with device detected atrial fibrillation with CHA₂DS₂-VASc score >4, apixaban is cost-saving in Canada and the UK and cost-effective in Germany. Apixaban is not cost-effective in the US at current prices.Table 1

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.064
GPT teacher head0.333
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueEuropean Heart Journal→Same topicAtrial Fibrillation Management and Outcomes→French-language works237,207→