Outcomes of Concomitant Coronary Artery Bypass Grafting in Patients With Infective Endocarditis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
It is current practice to perform concomitant coronary artery bypass grafting (CABG) in patients with infective endocarditis who have relevant coronary artery disease. However, CABG may add complexity to the operation. We performed a systematic review and a meta-analysis of studies that presented outcomes from patients who underwent valve surgery because of infective endocarditis with or without concomitant CABG. Three databases were assessed. Perioperative mortality was the primary outcome. Long-term mortality and postoperative stroke were the secondary outcomes. Inverse variance method and random model were performed. Five studies with a total of 5,408 patients were included. Mean follow-up was 8.2 years. Just 1 study addressed exclusively patients with documented coronary artery disease. Perioperative mortality did not differ between patients with or without concomitant CABG (odds ratio 1.53, 95% confidence interval 0.52 to 4.48, p = 0.44). Long-term mortality did not differ between patients who received and those who did not receive concomitant CABG (odds ratio 1.79, confidence interval 0.88 to 3.65, p = 0.11). Only 1 study from a multicenter registry reported data on the occurrence of postoperative stroke, which demonstrated that its incidence after adjustment was 26% in patients with concomitant CABG versus 21% in patients without concomitant CABG (p = 0.003). The results suggest that in endocarditis patients, adding CABG to valve surgery does not affect perioperative or long-term mortality. Data available on the impact of concomitant CABG on neurologic outcomes are limited to a retrospective multicenter registry and suggest that concomitant CABG may be associated with higher postoperative stroke.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".