Preoperative atrial fibrillation decreases event-free survival following cardiac surgery☆
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between preoperative atrial fibrillation and early and late clinical outcomes following cardiac surgery. METHODS: A retrospective cohort including all consecutive coronary artery bypass graft and/or valve surgery patients between 1995 and 2005 was identified (n = 9796). No patient had a concomitant surgical AF ablation. The association between preoperative atrial fibrillation and in-hospital outcomes was examined. We also determined late death and cardiovascular-related re-hospitalization by linking to administrative health databases. Median follow-up was 2.9 years (maximum 11 years). RESULTS: The prevalence of preoperative atrial fibrillation was 11.3% (n = 1105), ranging from 7.2% in isolated CABG to 30% in valve surgery. In-hospital mortality, stroke, and renal failure were more common in atrial fibrillation patients (all p < 0.0001), although the association between atrial fibrillation and mortality was not statistically significant in multivariate logistic regression. Longitudinal analyses showed that preoperative atrial fibrillation was associated with decreased event-free survival (adjusted hazard ratio 1.55, 95% confidence interval 1.42-1.70, p < 0.0001). CONCLUSIONS: Preoperative atrial fibrillation is associated with increased late mortality and recurrent cardiovascular events post-cardiac surgery. Effective management strategies for atrial fibrillation need to be explored and may provide an opportunity to improve the long-term outcomes of cardiac surgical patients.
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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 | 0.000 | 0.005 |
| 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 | 0.001 |
| 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".