Real-life practice in the management of new-onset postoperative atrial fibrillation early after cardiac surgery
Bibliographic record
Abstract
OBJeCtiveS: To investigate the real-world pharmacological management of postoperative atrial fibrillation (POAF) in patients undergoing cardiac surgery.MethOdS: A retrospective cohort analysis consisting of adult patients who underwent coronary artery bypass grafting, valve or combined surgery from January to December 2011 was performed using a clinical registry.The peri-and postoperative pharmacological management (rate control, rhythm control, anticoagulant therapy) of POAF was evaluated.Stepwise multivariate regression analyses were used to identify determinants for medication use at discharge.ReSuLtS: The cohort consisted of 1145 patients, of whom 377 (32.9%) developed POAF and 271 (23.7%) were included.At discharge, 251 patients (92.6%) received β-blocker therapy and 122 (45.0%) received antiarrhythmic therapy.Two hundred sixty-one (96.3%) received rateand/or rhythm-control therapy.Forty-eight (17.7%) patients received warfarin on discharge, although 38 had an additional indication.Men and urgent inpatients were less likely to be discharged on warfarin.Among 145 patients discharged on antiarrhythmic and/or anticoagulant therapy, 121 (83.4%) attended follow-up.Only 28.1% (34 of 121) had an electrocardiogram or Holter monitoring performed; despite this, antiarrhythmic medications were either continued or not addressed in 47.7% (51 of 107) of patients discharged on therapy.CONCLuSiONS: Treatment of POAF with rate-and/or rhythm-control medications was consistent with current national guideline recommendations.However, anticoagulant therapy use was low and appeared to be limited to patients with another indication.Assessment of POAF medications and rhythm status at postoperative follow-up visit was inconsistent.Thus, efforts to improve the management of POAF should focus on appropriately discontinuing unnecessary medications at postoperative follow-up to minimize the risk of adverse effects.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".