Perioperative risk factors for new-onset postoperative atrial fibrillation after coronary artery bypass grafting: a systematic review
Bibliographic record
Abstract
BACKGROUND: Postoperative atrial fibrillation (POAF) is the most common cardiac dysrhythmia to occur after coronary artery bypass grafting (CABG). However, the risk factors for new-onset POAF after CABG during the perioperative period have yet to be clearly defined. Accordingly, the aim of our systematic review was to evaluate the perioperative predictors of new-onset POAF after isolated CABG. METHOD: Our review methods adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. We searched seven electronic databases (PubMed, Embase, CINAHL, PsycArticles, Cochrane, Web of Science, and SCOPUS) to identify all relevant English articles published up to January 2020. Identified studies were screened independently by two researchers for selection, according to predefined criteria. The Newcastle-Ottawa Scale was used to evaluate the quality of studies retained. RESULTS: After screening, nine studies were retained for analysis, including 4798 patients, of whom 1555 (32.4%) experienced new-onset POAF after CABG. The incidence rate of new-onset POAF ranged between 17.3% and 47.4%. The following risk factors were identified: old age (p < 0.001), a high preoperative serum creatinine level (p = 0.001), a low preoperative hemoglobin level (p = 0.007), a low left ventricle ejection fraction in Asian patients (p = 0.001), essential hypertension (p < 0.001), chronic obstructive pulmonary disease (p = 0.010), renal failure (p = 0.009), cardiopulmonary bypass use (p = 0.002), perfusion time (p = 0.017), postoperative use of inotropes (p < 0.001), postoperative renal failure (p = 0.001), and re-operation (p = 0.005). All studies included in the analysis were of good quality. CONCLUSIONS: The risk factors identified in our review could be used to improve monitoring of at-risk patients for early detection and treatment of new-onset POAF after CABG, reducing the risk of other complications and negative clinical outcomes.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".