Postoperative atrial fibrillation predicts long-term survival after aortic-valve surgery but not after mitral-valve surgery: a retrospective study
Bibliographic record
Abstract
Background Postoperative atrial fibrillation (POAF) has been reported to be associated with reduced long-term survival after isolated coronary artery bypass grafting surgery. The objective of this study was to determine the impact of POAF on long-term survival after valvular surgery. Methods The authors retrospectively analysed the preoperative and operative data of 2986 consecutive patients with no preoperative history of atrial fibrillation undergoing first valvular surgery (aortic-valve replacement (AVR), mitral valve replacement or mitral valve repair (MVR/MVRp) with or without coronary artery bypass grafting surgery) in their institution between 1995 and 2008 (median follow-up 5.31 years, range 0.1-15.0). The authors investigated the impact of POAF on survival using multivariable Cox regression. Results Patients with POAF were older, and were more likely to have hypertension or renal failure when compared with patients without POAF. The 12-year survival in patients with POAF was 45.7±2.8% versus 61.4±2.1% in patients without POAF (p<0.001). On a multivariable analysis, when adjusting for age and other potential confounding factors, POAF tended to be associated with lower long-term survival (HR for all-cause death (HR)=1.17, 95% CI 1.00 to 1.38, p=0.051). The authors also analysed this association separately in patients with AVR and those with MVR/MVRp. In the multivariable analysis, POAF was a significant predictor of higher long-term mortality in patients with AVR (HR=1.22, CI 1.02 to 1.45, p=0.03) but not in patients with MVR/MVRp (HR=0.87, CI 0.58 to 1.29, p=0.48). Conclusions POAF is significantly associated with long-term mortality following AVR but not after MVR/MVRp. The underlying factors involved in the pathogenesis of POAF after MVR/MVRp may partially account for the lack of association between POAF and survival in these 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".