Differences in Postoperative Atrial Fibrillation Incidence and Outcomes After Cardiac Surgery According to Assessment Method and Definition: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background Postoperative atrial fibrillation (POAF) is the most frequent complication of cardiac surgery. Despite clinical and economic implications, ample variability in POAF assessment method and definition exist across studies. We performed a study‐level meta‐analysis to evaluate the influence of POAF assessment method and definition on its incidence and association with clinical outcomes. Methods and Results A systematic literature search was conducted to identify studies comparing the outcomes of patients with and without POAF after cardiac surgery that also reported POAF assessment method. The primary outcome was POAF incidence. The secondary outcomes were in‐hospital mortality, stroke, intensive care unit length of stay, and postoperative length of stay. Fifty‐nine studies totaling 197 774 patients were included. POAF cumulative incidence was 26% (range: 7.3%–53.1%). There were no differences in POAF incidence among assessment methods (27%, [range: 7.3%–53.1%] for continuous telemetry, 27% [range: 7.9%–50%] for telemetry plus daily ECG, and 19% [range: 7.8%–42.4%] for daily ECG only; P >0.05 for all comparisons). No differences in in‐hospital mortality, stroke, intensive care unit length of stay, and postoperative length of stay were found between assessment methods. No differences in POAF incidence or any other outcomes were found between POAF definitions. Continuous telemetry and telemetry plus daily ECG were associated with higher POAF incidence compared with daily ECG in studies including only patients undergoing isolated coronary artery bypass grafting. Conclusions POAF incidence after cardiac surgery remains high, and detection rates are variable among studies. POAF incidence and its association with adverse outcomes are not influenced by the assessment method and definition used, except in patients undergoing isolated coronary artery bypass grafting.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.036 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".