Comparing rivaroxaban and apixaban in GARFIELD-AF according to ROCKET AF and ARISTOTLE trial selection criteria
Bibliographic record
Abstract
Abstract Introduction There is debate on the extent to which differences in selection criteria and outcome definitions used for ARISTOTLE and ROCKET AF – the trials for the approval of apixaban and rivaroxaban, respectively, for non-valvular atrial fibrillation – influenced their differences in outcomes relative to vitamin K antagonists (VKAs). In absence of randomized trials comparing the two non-vitamin K oral antagonists (NOACs) directly, this question can be addressed using data from the Global Anticoagulant Registry in the FIELD–Atrial Fibrillation (GARFIELD-AF) registry, a large, high-quality prospective observational study of newly diagnosed AF patients. Purpose To assess the influence of the ARISTOTLE and ROCKET AF inclusion and exclusion criteria on results for safety and efficacy of apixaban and rivaroxaban versus VKA using uniform endpoints in GARFIELD-AF. Methods We selected patients treated with apixaban, rivaroxaban or VKA from GARFIELD-AF who were eligible for ARISTOTLE or ROCKET AF as per the original trial criteria. We replicated the inclusion criteria in the GARFIELD-AF population and derived those eligible for each trial. We calculated the adjusted hazard ratios (HRs) for stroke or systemic embolism, major bleeding and all-cause mortality within 2 years of enrolment for apixaban as well as rivaroxaban versus VKA (reference) in those eligible for each trial. We used a propensity score overlap weighted Cox model to emulate trial randomization between NOAC and VKA. Results Among patients on apixaban, rivaroxaban and VKA, 2570/3615 (71%), 3560/4914 (72%) and 8005/11734 (71%) were eligible for ARISTOTLE, respectively, and 1612/3615 (45%), 2005/4914 (41%) and 4368/11734 (37%), respectively, were eligible for ROCKET AF. Cardiovascular co-morbidity was greater in those eligible for ROCKET AF than in those eligible for ARISTOTLE. In patients selected using the more restrictive ROCKET AF criteria, apixaban and rivaroxaban users showed similar results when compared with VKA (see Figure). The two sets of comparisons remained non-significant in difference when applying the less restrictive ARISTOTLE criteria, but there were trends for less similarity. Conclusion Apixaban showed similar results to rivaroxaban when selecting for higher-risk patients using the ROCKET AF criteria. In patients selected using ARISTOTLE criteria the similarity was less pronounced. Our results underline the problems faced in comparing treatments across rather than within clinical trials. For instance, co-morbidities were substantially different for patients recruited into the original ARISTOTLE and ROCKET AF trials. The current work points to the need for high-quality observational data for assessment of relative drug performance in absence of direct drug comparisons through randomized trials. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): This work was supported by an unrestricted research grant from Bayer AG, Berlin, Germany, to TRI, London, UK, which sponsors the GARFIELD-AF registry. This work is supported by KANTOR CHARITABLE FOUNDATION for the Kantor-Kakkar Global Centre for Thrombosis Science.
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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 | 0.039 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".