Abstract 1519: Atrial Fibrillation Is Independently Associated With A High Risk of Death in Patients With Acute Coronary Syndromes
Bibliographic record
Abstract
Purpose: To assess whether atrial fibrillation (AF) is associated with 1-year mortality among patients with acute coronary syndromes (ACS). Methods: We analyzed the 1-year mortality of 110,588 ACS patients using a pooled database of clinical trials. Cox proportional hazards modeling was used to adjust for baseline characteristics including AF that may be associated with the risk of death within 7 days, and death from day 7 to 1 year. We examined the effect of AF in patients with ST-segment elevation versus those with non ST-segment elevation. Results: The prevalence of in-hospital AF was 8.0%. Baseline characteristics according to AF or not were: median age (70 vs 62 years), systolic blood pressure (130 vs 130mmHg), Killip class III and IV (4.2% vs 1.6%), history of hypertension (41% vs 48%), diabetes mellitus (19% vs 16%), prior congestive heart failure (7% vs 3.5%), and prior myocardial infarction (23% vs 20%). The figure shows the significant relationship of AF with mortality in the overall population. AF was significantly associated with an increased risk of in-hospital mortality in the non ST elevation ACS patients (adjusted odds ratio (OR) of 1.9 95% CI 1.5 to 2.5), but not in the ST elevation ACS patients (adjusted OR 1.0 95% CI 0.9 to 1.1). However, AF was significantly associated with an increased risk of death from day 7 to 1 year for both ST and non ST elevation ACS patients (adjusted OR 3.8 95% CI 3.5 to 4.1, and 2.2 95% CI 1.7 to 2.7, respectively). Conclusion: Among patients with ACS, AF is independently associated with a higher risk of 1-year mortality for both ST and non ST elevation ACS patients. Understanding the reasons for this increased risk may provide opportunity for improving care.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".