Abstract 9146: Antithrombotic Use and Outcomes in Patients with Atrial Fibrillation Complicating Acute Coronary Syndromes
Bibliographic record
Abstract
Background: Little is known about the use of oral anticoagulant and antiplatelet agents in patients with atrial fibrillation (AF) complicating non-ST-segment elevation (NSTE) ACS. Methods: We examined the occurrence and timing of AF and use of antithrombotic therapy in 9242 high-risk NSTE ACS patients randomized in EARLY ACS to early eptifibatide vs. delayed provisional use at PCI for whom AF information was available. Logistic regression with a landmark approach examined the association of AF within 7 days after ACS with death at 30 days. Adjusted models considered important clinical variables available prior to the 7-day landmark period. Results: Overall, 550 patients (6.0%) had AF as an in-hospital complication. AF events occurred at a median of 4 (2, 8) days after ACS, and 395 (72%) AF events occurred within 7 days. AF patients were older (median age 72 vs. 67 years), had worse renal function (median eCrCl 66 vs. 74 ml/min), more frequently had elevated troponin at baseline (90 vs. 83%), and more often had diabetes (34 vs. 30%), HTN (74 vs. 71%), or history of heart failure (15 vs. 12%) compared with patients without AF. The table shows rates of discharge antiplatelet and antithrombotic therapy. Among AF patients, 87% received aspirin at discharge, 48% received clopidogrel, and 19% received warfarin. Triple therapy (combination of aspirin, clopidogrel, and warfarin) was used in only 5.7%. After adjusting for clinical variables, in-hospital AF within 7 days post ACS was associated with nearly 5-fold higher risk for death between 7 and 30 days (HR 4.83, 95% CI 3.06-7.62). Conclusions: AF complicated 6% of post-ACS patients and was associated with substantially greater risk for death at 30 days. The majority of AF patients did not receive oral anticoagulation with antiplatelet agents at discharge, highlighting unmet needs to better optimize antithrombotic therapy at hospital discharge in these patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".