Incidence, Risk Factors, and Outcomes of Postoperative Atrial Fibrillation after Cardiac Surgery
Bibliographic record
Abstract
Abstract Objective: Postoperative atrial fibrillation (POAF) is a known complication after cardiac surgery. This study investigated the incidence, perioperative outcomes, and predictors of POAF. Methods: We conducted a retrospective review of all eligible patients undergoing cardiac surgery procedures at King Khalid University Hospital, Riyadh, Saudi Arabia, between April 2015 and June 2021. Prespecified demographic, perioperative, and comorbidity data were collected, and summary statistics were done. Results: The incidence of POAF was 10.8% (114/1053 patients). Most patients had POAF detected in the first 72 h, except those who underwent septal defect repair procedures. Patients who developed POAF had significantly higher rates of complications, including major adverse cardiovascular events, pneumonia, bleeding and shock, acute kidney injury, and congestive heart failure (all had a P ≤ 0.005). Advanced age and increased body mass index were the preoperative predictors of POAF. Furthermore, undergoing coronary artery bypass grafting (CABG), valve replacement surgery, or a combined procedure were also predictors of POAF. Conclusion: POAF after cardiac surgery is a common complication with increased risks of significant complications. Efforts to prevent POAF incidence are required through prediction and preventive measures. More studies are needed to determine if early detection and prompt treatment could mitigate the clinical sequelae of POAF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".