Optimal medical therapy after coronary artery bypass grafting: a primer for surgeons
Bibliographic record
Abstract
PURPOSE OF REVIEW: After coronary artery bypass grafting (CABG), patients remain at increased risk of cardiovascular events and death. Cardiac surgeons have the opportunity to reduce this risk by optimizing post-CABG patients' medical therapy. RECENT FINDINGS: Recent developments in lipid-lowering, diabetes management, antithrombotic therapy, and anti-inflammatory therapy can significantly improve prognosis in patients with chronic coronary artery disease. PCSK-9 inhibitors should be used in patients with elevated LDL cholesterol despite maximally tolerated statin therapy. Icosapent ethyl should be considered in patients with elevated triglycerides despite maximally tolerated statin therapy. Long-acting GLP-1 receptor agonists or SLGT-2 inhibitors should be used in all post-CABG patients with type 2 diabetes. Intensified antithrombotic therapy with DAPT or DPI reduces MACE (and DPI reduces mortality) in patients with high atherosclerotic burden. Colchicine has not yet been incorporated into guidelines on OMT for stable CAD but it is reasonable to consider using it in high-risk patients. SUMMARY: We review the foundations of optimal medical therapy after CABG, and summarize recent advances with a focus on practical application for the busy cardiac surgeon.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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; a candidate call from one teacher head, 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".