Causes of death in patients with atrial fibrillation and a recent acute coronary syndrome or percutaneous coronary intervention: insights from the AUGUSTUS trial
Bibliographic record
Abstract
Abstract Background Patients with atrial fibrillation (AF) and concomitant coronary artery disease are at increased risk for poor outcomes. Less is known about specific causes of death in this population. Methods We describe specific causes of death among patients with AF and acute coronary syndrome and/or percutaneous coronary intervention included in the AUGUSTUS trial and followed for 6 months. An independent clinical events committee, blinded to treatment assignment, adjudicated cause of death according to pre-defined criteria. The association between baseline factors and all-cause death was evaluated using Cox proportional hazards modeling. Results A total of 151 deaths occurred in 4614 patients and were adjudicated as follows: 111 (73.5%) deaths due to cardiovascular (CV) causes and 40 (26.5%) due to non-CV causes. The most common cause of CV death was sudden death (n=39 [35.1%]), followed by myocardial infarction (n=29 [26.1%]) and heart failure (n=24 [21.6%]). The most common causes of non-CV death were infection (n=11 [27.5%]), bleeding (n=8 [20.0%]), and malignancy (n=5 [12.5%]). Increasing age, African American race, history of heart failure, treatment with diuretics, and lower body weight were associated with an increased risk of all-cause death (Table). Conclusions Among patients with AF and coronary artery disease, cardiovascular causes were responsible for the majority of deaths within 6 months. Our findings provide relevant information to inform the design of future studies in this population. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): AUGUTUS was funded by Bristol Myers Squibb and Pfizer.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".