Early Versus Delayed Stroke After Cardiac Surgery: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background Although it is traditionally regarded as a single entity, perioperative stroke comprises 2 separate phenomena (early/intraoperative and delayed/postoperative stroke). We aimed to systematically evaluate incidence, risk factors, and clinical outcome of early and delayed stroke after cardiac surgery. Methods and Results A systematic review ( MEDLINE , EMBASE , Cochrane Library) was performed to identify all articles reporting early (on awakening from anesthesia) and delayed (after normal awakening from anesthesia) stroke after cardiac surgery. End points were pooled event rates of stroke and operative mortality and incident rate of late mortality. Thirty-six articles were included (174 969 patients). The pooled event rate for early stroke was 0.98% (95% CI 0.79% to 1.23%) and was 0.93% for delayed stoke (95% CI 0.77% to 1.11%; P=0.68). The pooled event rate of operative mortality was 28.8% (95% CI 17.6% to 43.4%) for early and 17.9% (95% CI 14.0% to 22.7%) for delayed stroke, compared with 2.4% (95% CI 1.9% to 3.1%) for patients without stroke ( P<0.001 for early versus delayed, and for perioperative stroke, early stroke, and delayed stroke versus no stroke). At a mean follow-up of 8.25 years, the incident rate of late mortality was 11.7% (95% CI 7.5% to 18.3%) for early and 9.4% (95% CI 5.9% to 14.9%) for delayed stroke, compared with 3.4% (95% CI 2.4% to 4.8%) in patients with no stroke. Meta-regression demonstrated that off-pump was inversely associated with early stroke (β=-0.009, P=0.01), whereas previous stroke (β=0.02, P<0.001) was associated with delayed stroke. Conclusions Early and delayed stroke after cardiac surgery have different risk factors and impacts on operative mortality as well as on long-term survival.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.024 | 0.022 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".