Short-term and Long-term Risk of Stroke in Patients With Perioperative Atrial Fibrillation After Cardiac Surgery: Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Perioperative atrial fibrillation (POAF) after cardiac surgery has been associated with an increased risk of stroke in some studies. However, the exact magnitude of this association during short-term and long-term follow-up remains unclear. METHODS: We searched PubMed, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) for the time period from database inception to October 2020. We included observational studies with ≥ 100 patients that reported data on short-term or long-term stroke risk in patients with and without POAF after cardiac surgery. Data were pooled using random-effects models. We reported summary risk ratios (RRs) for studies reporting multivariable adjusted results and calculated absolute risk differences (ARDs) with 95% confidence intervals (CIs). RESULTS: = 16%; 10 studies) and absolute risk of long-term stroke (1.06 vs 0.88 per 100 patient-years; ARD 0.18 per 100 patient-years; 95% CI, 0.07-0.26). Sensitivity analyses of high-quality studies and studies reporting either ischemic or embolic strokes yielded similar findings. CONCLUSIONS: POAF after cardiac surgery was associated with an increased risk of both short-term and long-term stroke. However, the long-term stroke ARD was small, and whether these patients will benefit from long-term oral anticoagulation therapy is unclear.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".