Predictors of NOAC versus VKA use for stroke prevention in patients with newly diagnosed atrial fibrillation: Results from GARFIELD-AF
Bibliographic record
Abstract
INTRODUCTION: A principal aim of the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) was to document changes in treatment practice for patients with newly diagnosed atrial fibrillation during an era when non-vitamin K antagonist oral anticoagulants (NOACs) were becoming more widely adopted. In these analyses, the key factors which determined the choice between NOACs and vitamin K antagonists (VKAs) are explored. METHODS: Logistic least absolute shrinkage and selection operator regression determined predictors of NOAC and VKA use. Data were collected from 24,137 patients who were initiated on AC ± antiplatelet (AP) therapy (NOAC [51.4%] or VKA [48.6%]) between April 2013 and August 2016. RESULTS: The most significant predictors of AC therapy were country, enrolment year, care setting at diagnosis, AF type, concomitant AP, and kidney disease. Patients enrolled in emergency care or in the outpatient setting were more likely to receive a NOAC than those enrolled in hospital (OR 1.16 [95% CI: 1.04-1.30], OR: 1.15 [95% CI: 1.05-1.25], respectively). NOAC prescribing seemed to be favored in lower-risk groups, namely, patients with paroxysmal AF, normotensive patients, and those with moderate alcohol consumption, but also the elderly and patients with acute coronary syndrome. By contrast, VKAs were preferentially used in patients with permanent AF, moderate to severe kidney disease, heart failure, vascular disease, and diabetes and with concomitant AP. CONCLUSION: GARFIELD-AF data highlight marked heterogeneity in stroke prevention strategies globally. Physicians are adopting an individualized approach to stroke prevention where NOACs are favored in patients with a lower stroke risk but also in the elderly and patients with acute coronary syndrome.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".