Abstract 9131: Clinical, Biochemical and Genetic Predictors of Late Coronary Artery Bypass Graft Failure
Bibliographic record
Abstract
BACKGROUND: We tested the hypothesis that known clinical and biochemical risk factors for atherosclerosis have prognostic promise as novel biochemical markers to predict coronary artery bypass graft (CABG) failure . We also utilized a high-throughput microarray analysis to identify novel single nucleotide polymorphisms (SNPs) genetic predictors of graft failure and to better understand the pathogenesis of graft occlusion. METHODS AND RESULTS: This is a nested case-control sub-study of the Radial Artery Patency Study (RAPS) 5 year follow-up ( NCT00187356 ). Between June 1996 and January 2001, 87 patients underwent CABG. Of these, 26 patients (29.9%) had an occluded study graft (saphenous vein graft or radial artery) at angiographic follow-up (8.0±1.1 years). Clinical parameters as well as late angiography and concurrent blood biomarker analysis and surgical outcomes data were included in a multivariable analysis to determine independent predictors of graft failure. Risk factors of long term graft failure were fibrinogen (OR 3.94; 95%CI [1.33-11.63], p=0.01), creatinine (OR 1.06; 95%CI [1.02-1.10], p=0.006) and diabetes mellitus (OR 5.15; 95%CI [1.08-24.59], p=0.04). Interestingly, HDL (OR 0.74, 95%CI [0.53-1.02], p=0.06) was weakly protective against long term graft failure but other lipid markers, LDL and total cholesterol were not predictors. We identified the association of several human single nucleotide polymorphisms with graft failure including a novel link with mutations in glutathione-s-tranferase α3 . Human coronary arteries and bypass grafts demonstrated increased GSTα3 expression in atherosclerotic plaques and in tissues surrounding occluded saphenous vein grafts. CONCLUSION: We identify diabetes to be a clinical predictor and plasma fibrinogen, creatinine and HDL as potential novel biomarkers which, together may help to risk stratify patients for development of graft failure. We further demonstrate a novel association between GSTα3 and graft failure, a potential pathogenetic mechanism of saphenous vein graft occlusion.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".