Cost-effectiveness of apixaban vs. current standard of care for stroke prevention in patients with atrial fibrillation
Bibliographic record
Abstract
AIMS: Warfarin, a vitamin K antagonist (VKA), has been the standard of care for stroke prevention in patients with atrial fibrillation (AF). Aspirin is recommended for low-risk patients and those unsuitable for warfarin. Apixaban is an oral anticoagulant that has demonstrated better efficacy than warfarin and aspirin in the ARISTOTLE and AVERROES studies, respectively, and causes less bleeding than warfarin. We evaluated the potential cost-effectiveness of apixaban against warfarin and aspirin from the perspective of the UK payer perspective. RESULTS AND METHODS: A lifetime Markov model was developed to evaluate the pharmacoeconomic impact of apixaban compared with warfarin and aspirin in VKA suitable and VKA unsuitable patients, respectively. Clinical events considered in the model include ischaemic stroke, haemorrhagic stroke, intracranial haemorrhage, other major bleed, clinically relevant non-major bleed, myocardial infarction, cardiovascular hospitalization and treatment discontinuations; data from the ARISTOTLE and AVERROES trials and published mortality rates and event-related utility rates were used in the model. Apixaban was projected to increase life expectancy and quality-adjusted life years (QALYs) compared with warfarin and aspirin. These gains were expected to be achieved at a drug acquisition-related cost increase over lifetime. The estimated incremental cost-effectiveness ratio was £11 909 and £7196 per QALY gained with apixaban compared with warfarin and aspirin, respectively. Sensitivity analyses indicated that results were robust to a wide range of inputs. CONCLUSIONS: Based on randomized trial data, apixaban is a cost-effective alternative to warfarin and aspirin, in VKA suitable and VKA unsuitable patients with AF, respectively.
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 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".