Genetics of coronary artery disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients with multivessel coronary artery disease (CAD) may undergo revascularization by either percutaneous coronary intervention (PCI) or coronary artery bypass graft surgery (CABG). This review will discuss the use of polygenic risk scores for risk-stratification of patients with multivessel CAD in order to guide the choice of revascularization. RECENT FINDINGS: A 57-single nucleotide polymorphism (SNP)-polygenic risk score can accurately risk-stratify patients with CAD and identify those who will receive greater benefit from statin therapy. The most recent genomic studies reveal 243 different SNPs are now significantly associated with CAD. Randomized clinical trials comparing PCI vs. CABG (FREEDOM, SYNTAX, NOBLE, EXCEL) have uncovered factors related to CAD severity (diabetes, SYNTAX score) are critical determinants of outcomes after revascularization. SUMMARY: There is a need to discover predictors of outcomes after PCI vs. CABG to improve clinical decision-making in multivessel CAD. High polygenic risk score is associated with increased CAD severity and better outcomes with statin therapy. Randomized clinical trials indicate CAD severity is associated with better outcomes after CABG compared with PCI. Accordingly, polygenic risk score could also be associated with better outcomes after CABG vs. PCI and used to optimize revascularization for patients with multivessel CAD.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".