Perioperative Intravenous Amiodarone Does Not Reduce the Burden of Atrial Fibrillation in Patients Undergoing Cardiac Valvular Surgery
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation is a common complication after cardiac surgery. Postoperative atrial fibrillation is associated with increased risks of morbidity and mortality, and, therefore, preventive strategies using oral amiodarone have been developed but are often unpractical. Intravenous amiodarone administered after the induction of anesthesia and continued postoperatively for 48 h could represent an effective strategy to prevent postoperative atrial fibrillation in patients undergoing cardiac valvular surgery. METHODS: Single-center, double-blinded, double-dummy, randomized controlled trial in patients undergoing valvular surgery. Patients received either an intravenous loading dose of 300 mg of amiodarone or placebo in the operating room, followed by a perfusion of 15 mg . kg(-1) . 24 h(-1) for 2 days. The primary endpoint was the development of atrial fibrillation occurring at any time within the postoperative period. RESULTS: One hundred twenty patients were randomly assigned (mean age was 65 +/- 11 yr). Overall atrial fibrillation occurred more frequently in the perioperative intravenous amiodarone group compared with the placebo group (59.3 vs. 40.0%; P = 0.035). Four preoperative factors were found to be independently associated with a higher risk of developing postoperative atrial fibrillation: older age (P = 0.0003), recent myocardial infarction (<6 months; P = 0.026), preoperative angina (P = 0.0326), and use of a calcium channel blocker preoperatively (P = 0.0078) when controlling for groups. CONCLUSION: In patients undergoing cardiac valvular surgery, a strategy using intravenous amiodarone for 48 h is not efficacious in reducing the risk of atrial fibrillation during cardiac valvular surgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 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".