Emerging Implications of Elevated Lipoprotein(a) Levels in Coronary Artery Bypass Graft Surgery
Bibliographic record
Abstract
BACKGROUND: Coronary artery bypass grafting (CABG) remains a cornerstone in the management of coronary artery disease (CAD). In nonurgent surgical revascularization cases, preoperative optimization of modifiable risk factors can improve outcomes. There is increasing interest in the relationship between lipoprotein(a) levels and the risk for ischemic cardiovascular disease, particularly how CABG outcomes are in turn affected. This review highlights the role of lipoprotein(a) in the pathogenesis of CAD and CABG outcomes and discusses future directions for its optimal management in the perioperative period. METHODS: The PubMed/MEDLINE database was reviewed until March 2024 to capture publications that evaluated and/or described the relationship between lipoprotein(a) and CABG surgery or CAD outcomes. RESULTS: The available literature supports lipoprotein(a) as a causal and independent risk factor for the pathogenesis of CAD. Elevated lipoprotein(a) levels are associated with an increased risk of adverse post-CABG outcomes, including graft occlusion incidence and major adverse cardiovascular events. Genetic variations influencing lipoprotein(a) levels play a role in disease progression and surgical outcomes. Several therapies aimed at reducing lipoprotein(a) levels, currently in phase III clinical trials, show promise for improving the prognosis after CABG. CONCLUSIONS: Among individuals undergoing surgical revascularization for CAD, lipoprotein(a) levels may help define risk and inform best practices for perioperative management. We advocate for the routine measurement of lipoprotein(a) in all patients undergoing CABG. Emerging lipoprotein(a)-lowering agents show promise for secondary prevention of cardiac events, although dedicated analyses in cardiac surgical subcohorts will be important to evaluate their role in improving CABG outcomes.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".