Does high-density lipoprotein influence the development of saphenous vein graft disease after coronary bypass surgery?: exploratory analysis from the CASCADE trial
Bibliographic record
Abstract
BACKGROUND: Low levels of high-density lipoprotein (HDL) purportedly increase the risk after coronary bypass surgery. This may relate to the development of saphenous vein graft (SVG) disease early postoperatively, but this premise has never been evaluated in the context of a prospective trial. METHODS: The CASCADE Trial was a multi-center study of 113 patients evaluating the use of postoperative clopidogrel. Patients received standard lipid management after surgery (96% statins). At 12 months, angiography and intravascular ultrasound was performed to assess SVG occlusion and intimal hyperplasia, respectively. In this exploratory analysis, we evaluated the influence of HDL levels on the development of SVG disease at 12 months, using the established cut-off of <40 mg/dL suggesting increased risk. RESULTS: While HDL levels increased over the time-period of the trial (P < 0.0001), 51.1% of patients had HDL levels <40 mg/dL 12 months after surgery. Slightly more SVG occlusions occurred amongst patients with HDL levels <40 mg/dL (6.8%), compared to patients with HDL levels >40 mg/dL (4.0%, P = 0.5). With multivariate adjustment, HDL level <40 mg/dL was associated with a trend towards more SVG occlusions (odds ratio: 3.2; P = 0.12). Lower HDL level was also associated with more intimal hyperplasia on ultrasound at 12 months (P = 0.10). Patients who had HDL levels >60 mg/dL had the least amount of intimal hyperplasia, significantly less than the remainder of the cohort (P = 0.01). CONCLUSIONS: Within this population, lower HDL levels were associated with trends towards more graft occlusions and more vein intimal hyperplasia. Modulation of postoperative HDL levels may represent a valuable future strategy for the reduction of SVG disease.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".