Effectiveness and Safety of Apixaban in over 3.9 Million People with Atrial Fibrillation: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: There is a plethora of real-world data on the safety and effectiveness of direct-acting oral anticoagulants (DOACs); however, study heterogeneity has contributed to inconsistent findings. We compared the effectiveness and safety of apixaban with those of other direct-acting oral anticoagulants (DOACs) and vitamin K antagonists (VKA e.g., warfarin). Methods: A systematic review and meta-analysis was conducted retrieving data from PubMed, SCOPUS and Web of Science from January 2009 to December 2021. Studies that evaluated apixaban (intervention) prescribed for adults (aged 18 years or older) with AF for stroke prevention compared to other DOACs or VKAs were identified. Primary outcomes included stroke/systemic embolism (SE), all-cause mortality, and major bleeding. Secondary outcomes were intracranial haemorrhage (ICH) and ischaemic stroke. Randomised controlled trials and non-randomised trials were considered for inclusion. Results: In total, 67 studies were included, and 38 studies were meta-analysed. Participants taking apixaban had significantly lower stroke/SE compared to patients taking VKAs (relative risk (RR) 0.77, 95% confidence interval (CI) 0.64–0.93, I2 = 94%) and dabigatran (RR 0.84, 95% CI 0.74–0.95, I2 = 66%), but not to patients administered rivaroxaban. There was no statistical difference in mortality between apixaban and VKAs or apixaban and dabigatran. Compared to patients administered rivaroxaban, participants taking apixaban had lower mortality rates (RR 0.83, 95% CI 0.71–0.96, I2 = 96%). Apixaban was associated with a significantly lower risk of major bleeding compared to VKAs (RR 0.58, 95% CI 0.52–0.65, I2 = 90%), dabigatran (RR 0.79, 95% CI 0.70–0.88, I2 = 78%) and rivaroxaban (RR 0.61, 95% CI 0.53–0.70, I2 = 87%). Conclusions: Apixaban was associated with a better overall safety and effectiveness profile compared to VKAs and other DOACs.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.051 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".