New‐Onset Perioperative Atrial Fibrillation After Coronary Artery Bypass Grafting and Long‐Term Risk of Adverse Events: An Analysis From the CORONARY Trial
Bibliographic record
Abstract
Background Perioperative atrial fibrillation (POAF) is common in patients undergoing cardiac surgery. Conflicting evidence exists whether patients with POAF after cardiac surgery have an increased long-term risk of stroke and other adverse events. Methods and Results We prospectively followed for up to 5 years 4624 patients without prior atrial fibrillation who underwent coronary artery bypass grafting in an international study. POAF was defined as atrial fibrillation that occurred during the initial hospitalization for surgery, lasted for ≥5 minutes, and required treatment. Outcomes assessed were a composite of death, nonfatal myocardial infarction or nonfatal stroke, and its individual components. Median age was 67 years, and 778 (16.8%) had an episode of POAF. The incidence of the composite outcome was 6.84 and 4.10 per 100 patient-years in patients with and without POAF, and the incidence of stroke was 0.75 versus 0.45, respectively. The adjusted hazard ratios (aHRs) were 1.36 (95% CI, 1.16-1.59) for the composite outcome; 1.33 (95% CI, 1.10-1.61) for death; 1.58 (95% CI, 1.23-2.02) for myocardial infarction, and 1.27 (95% CI, 0.81-2.00) for stroke. In a landmark analysis excluding events of the initial hospital admission, the aHRs were 1.26 (95% CI, 1.03-1.54) for the composite outcome, 1.28 (95% CI, 1.03-1.59) for death, 1.70 (95% CI, 0.86-3.36) for myocardial infarction, and 1.07 (95% CI, 0.59-1.93) for stroke. At hospital discharge, 10.7% and 1.4% of patients with and without POAF received oral anticoagulation, respectively. Conclusions Patients with POAF after cardiac surgery had an increased long-term risk of adverse outcomes, mainly death and myocardial infarction. The risk of stroke was low and not increased in patients with POAF. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT00463294.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".