Atrial Fibrillation Following Cardiac Surgery: A Retrospective Cohort Series
Bibliographic record
Abstract
Atrial fibrillation (AF) is a common postoperative complication of cardiac surgery, yet the prevention and treatment of postoperative AF remains controversial and varies among practitioners. The purpose of this study was to document the incidence and time of onset of postoperative AF in a cardiac surgical cohort, examine risk factors implicated in the occurrence of postoperative AF, and assess effectiveness of current treatment strategies implemented for postoperative AF. A retrospective health record review was conducted on 1078 adults following cardiac surgery. Data on demographic, preoperative, perioperative, and postoperative risk factors for postoperative AF, documented episodes of AF, and clinical outcomes were recorded. Overall incidence of postoperative AF was 39.6%: 57.6% after cardiac valve surgery, 69.3% after combined coronary artery bypass graft and valve surgery, and 33% after bypass graft surgery alone. The peak onset of postoperative AF occurred on the second postoperative day. Advancing age, history of AF, combined cardiac valve and coronary artery bypass graft surgery, and high Mg+2 levels on the third postoperative day were significant predictors of postoperative AF in this cohort. Length of hospitalization increased with the presence of postoperative AF. Findings corroborate that multiple factors play a role in the development of AF following cardiac surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".