Characteristics of patients with atrial fibrillation prescribed antiplatelet monotherapy compared with those on anticoagulants: insights from the GARFIELD-AF registry
Bibliographic record
Abstract
Aims: Current atrial fibrillation (AF) guidelines discourage antiplatelet (AP) monotherapy as alternative to anticoagulants (ACs). Why AP only is still used is largely unknown. Methods and results: Factors associated with AP monotherapy prescription were analysed in GARFIELD-AF, a registry of patients with newly diagnosed (≤6 weeks) AF and ≥1 investigator-determined stroke risk factor. We analysed 51 270 patients from 35 countries enrolled into five sequential cohorts between 2010 and 2016. Overall, 20.7% of patients received AP monotherapy, 52.1% AC monotherapy, and 14.1% AP + AC. Most AP monotherapy (82.5%) and AC monotherapy (86.8%) patients were CHA2DS2-VASc ≥2. Compared with patients on AC monotherapy, AP monotherapy patients were frequently Chinese (vs. Caucasian, odds ratio 2.73) and more likely to have persistent AF (1.32), history of coronary artery disease (2.41) or other vascular disease (1.67), bleeding (2.11), or dementia (1.81). The odds for AP monotherapy increased with 5 years of age increments for patients ≥75 years (1.24) but decreased with age increments for patients 55-75 years (0.86). Antiplatelet monotherapy patients were less likely to have paroxysmal (0.67) or permanent AF (0.57), history of embolism (0.56), or alcohol use (0.90). With each cohort, AP monotherapy declined (P<0.0001), especially non-indicated use. AP + AC and no antithrombotic therapy were unchanged. However, even in 2015 and 2016, about 50% of AP-treated patients had no indication except AF (71% were CHA2DS2-VASc ≥2). Conclusion: Prescribing AP monotherapy in newly diagnosed AF has declined, but even nowadays a substantial proportion of AP-treated patients with AF have no indication for AP. Clinical Trial Registration: URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".