Coronary artery bypass grafting in acute coronary syndromes: modern indications and approaches
Bibliographic record
Abstract
PURPOSE OF REVIEW: Acute coronary syndromes (ACS) are a leading cause of morbidity and mortality worldwide, with approximately 1.2 million hospitalizations annually in the U.S. This review aims to explore the contemporary evidence regarding revascularization strategies, including percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG), in ACS patients. It also addresses the unresolved questions concerning the optimal procedural aspects of surgery and antithrombotic therapy for secondary prevention postsurgery. RECENT FINDINGS: Recent studies highlight that while PCI is generally preferred for its timeliness in high-risk non-ST-elevation ACS (NSTE-ACS) patients, CABG offers a benefit in terms of cardiovascular events in those with multivessel disease, particularly in the presence of diabetes and higher coronary disease complexity. For ST-elevation myocardial infarction (STEMI), CABG is less frequently utilized due to the preference for primary PCI, but it remains crucial for patients with complex anatomy or failed PCI. Furthermore, the optimal timing and type of antiplatelet therapy post-CABG remain controversial, with current evidence supporting the use of dual antiplatelet therapy (DAPT) to reduce ischemic events but necessitating careful management to balance bleeding risks. SUMMARY: In patients with ACS, the choice between PCI and CABG depends on the complexity of coronary disease and patient comorbidities. CABG is particularly beneficial for multivessel disease in NSTE-ACS and specific STEMI cases where PCI is not feasible. The management of antiplatelet therapy postsurgery requires a nuanced approach to minimize bleeding risks while preventing thrombotic complications. Further randomized clinical trials are needed to solidify these findings and guide clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".