Device-detected atrial fibrillation before and after acute cardiac events: insights from ASSERT
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Background Atrial fibrillation (AF) that is first detected concurrently with or shortly after another cardiac event is often thought to be caused by acute cardiac injury, and therefore reversible. Methods ASSERT enrolled patients >65 years old with hypertension and a pacemaker, but without known AF. We evaluated participants who had a cardiac event [angina/myocardial infarction (MI), cardiac catheterization/percutaneous coronary intervention (PCI), cardiac surgery or other (e.g. pericarditis, hypertensive crisis)] and compared the prevalence of device-detected AF before and after these events. Results Among 2580 participants, 178 (6.9%) had at least one cardiac event over a mean 2.5 years of follow-up. In the 30 days following a first cardiac event, the prevalence of device-detected AF >6 min was 12.4% (95% confidence interval [CI] 7.9%-18.1%), which was higher than in the 30 days before the event (12.4% versus 4.5%, P = 0.004) (Figure 1). The prevalence of device-detected AF following the event was comparable across event subtypes (MI: 13.8%, 95%CI 7.9-18.1%; PCI: 6.9%, 95%CI 1.9-16.7%; Surgery: 20.0%, 95%CI 5.7-43.7%; Other: 18.5%, 95%CI 6.3-38.1%). There was a significant association between device-detected AF in the 6 months before a cardiac event and device-detected AF in the 30 days after a cardiac event: odds ratio (OR, adjusted for CHA2DS2-VASc score) for episodes >6 min 7.07 (95%CI 2.07-24.19; P = 0.002); adjusted OR for episodes >24 hours: 11.41 (95%CI 1.47-88.43; p = 0.020). Conclusions Acute cardiac events are associated with an increase in the prevalence of device-detected AF. These episodes are associated with a prior history of device-detected AF. Abstract Figure 1
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".