Abstract 15439: High Rates of antiplatelet Use in Atrial Fibrillation Patients Treated With Oral Anticoagulation: Insights from the Stroke Prevention and Rhythm Interventions in Atrial Fibrillation (SPRINT-AF) Registry
Bibliographic record
Abstract
Introduction: Among patients with atrial fibrillation (AF) treated with oral anticoagulation (OAC) for stroke prevention, concomitant use of antiplatelet (AP) agents increases bleeding risk and may not be associated with a significant reduction in the rates of vascular events. We sought to identify factors associated with OAC+AP vs. OAC use in a contemporary AF registry. Methods: From December 2012 to July 2013, a cross-sectional analysis of 936 consecutive AF patients was performed. They were enrolled from 109 community practices (84 [77%] Primary Care practices) in 10 Canadian provinces. Demographics of patients treated with OAC+AP (primarily aspirin) vs. OAC alone were identified. Multivariable logistic regression was performed to identify factors associated with OAC+AP vs. OAC use. Results: Seven hundred and eighty-two (83.1%) patients were treated with OAC, amongst whom 143 (18.3%) were treated with OAC+AP and 639 (81.7%) were treated with OAC alone. Amongst patients treated with OAC+AP, 59 (41.3%) did not have a history of coronary artery disease (CAD) (defined as history of stable CAD, acute coronary syndrome, percutaneous coronary intervention (PCI), or coronary artery bypass surgery) or peripheral arterial disease (PAD). In the OAC+AP group, 41 (28.7%) patients had PCI, 55 (38.5%) patients had diabetes, and 24 (16.8%) patients had a previous stroke or transient ischemic attack. Patient treated with OAC+AP vs. OAC alone did not significantly differ in age: 76.7 (71.2, 82.9) vs. 76.8 (69.5, 83.1) years (median, IQR). On multivariable analysis, CAD (OR 3.60, 95% CI 2.24 to 5.55, p Conclusions: In this contemporary AF registry, about 1 in 5 OAC-treated patients was also treated with AP. Although a history of CAD was associated with OAC+AP use, about 40% of patients did not have compelling indications for being treated with AP agents. The relatively high rate of concomitant AP use in OAC-treated AF patients presents a potential opportunity to reduce major bleeding. Efforts are needed to address this practice pattern to minimize over-prescription of AP in this population.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".