Antithrombotic therapy and outcomes of patients with atrial fibrillation following primary percutaneous coronary intervention: results from the APEX-AMI trial
Bibliographic record
Abstract
AIMS: To assess the incidence and timing of atrial fibrillation (AF), describe antithrombotic therapy use, and evaluate the association of AF with 90 day mortality and other secondary clinical outcomes. METHODS AND RESULTS: We studied 5745 ST-segment elevation myocardial infarction patients treated with primary percutaneous coronary intervention (PCI) in APEX-AMI. Approximately 11% had AF during hospitalization. Atrial fibrillation prevalence at baseline and at discharge was 4.8% [confidence interval (CI) 4.3-5.4%] and 2.5% (CI 2.1-2.9%), respectively. The proportion of 5466 patients without AF at baseline who developed new onset AF was 6.3% (CI 5.6-6.9%). This corresponded to 9.3 cases of new onset AF/1000 patient days at risk. New onset AF was independently associated with 90 day mortality [adjusted hazard ratio (HR) 1.81; 95% CI 1.06-3.09; P = 0.029] after accounting for baseline covariates and in-hospital procedures and complications. New onset AF was associated with shock (adjusted HR 3.81; 95% CI 1.88-7.70; P = 0.0002), congestive heart failure (adjusted HR 2.66; 95% CI 1.74-4.06; P < 0.0001), and stroke (adjusted HR 2.98; 95% CI 1.47-6.04; P = 0.0024) in models accounting for baseline covariates. Of AF patients, 55% did not receive oral anticoagulation therapy at discharge. Among patients with coronary stents, 5.1% were discharged on triple therapy. Patients at highest risk of stroke (CHADS(2) score > or =2) were least likely to receive oral anticoagulation at discharge (39%). Warfarin use in patients with AF at discharge (43.4%) was associated with lower rates of 90 day mortality and stroke. CONCLUSION: Atrial fibrillation prevalence at baseline and at discharge was 4.8 and 2.5%, respectively. The proportion of patients who developed new onset AF was 6.3%. New onset AF was independently associated with 90 day mortality and was a marker of adverse outcomes in patients undergoing primary PCI.
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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 | 0.001 | 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".