Detection and management of postoperative atrial fibrillation after coronary artery bypass grafting or non-cardiac surgery: a survey by the AF-SCREEN International Collaboration
Bibliographic record
Abstract
Abstract We developed a survey to describe current practice on the detection and management of new-onset postoperative atrial fibrillation (POAF) occurring after coronary artery bypass grafting (CABG) or non-cardiac surgery. We e-mailed an online anonymous questionnaire of 17 multiple choice or rank questions to an international network of healthcare professionals. Between June 2023 and June 2024, 158 participants from 25 countries completed the survey. For CABG patients, 62.7% of respondents reported use of telemetry to detect POAF on the ward until discharge, and 40% reported no dedicated methods for monitoring AF recurrences during follow-up. The largest number (46%) reported prescribing oral anticoagulants (OACs) at discharge if patients were at risk according to CHA2DS2-VASc/CHA2DS2-VA scores, and the most common duration of OAC therapy was 3 months to 1 year (43%). For non-cardiac surgery patients, POAF detection methods varied, with 29% using periodic 12-lead ECG and 27% using telemetry followed by periodic ECGs. For monitoring AF recurrence, 33% reported planned cardiology visits with ECG. Regarding OAC prescription during follow-up, 51% reported they prescribe OACs only for patients who are at risk of stroke, and 42% prescribe OACs for an interval of 3 months to 1 year. The most commonly reported barrier to OAC prescription was the lack of randomized controlled trial data. For both CABG and non-cardiac surgery, the reported methods for POAF detection and recurrences monitoring were heterogeneous and prescription patterns for OACs varied greatly. The most frequently reported concern about long-term anticoagulation was lack of randomized data, indicating the urgent need for sound studies that inform daily clinical practice.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".