Abstract P564: Hypertriglyceridemia as a Treatment Target in Asymptomatic Carotid Stenosis
Bibliographic record
Abstract
Background: Data from the CDC show that approximately one-quarter of adults have elevated triglyceride (TG) levels. Recent trials have demonstrated that pharmacologic treatment of high TG levels, in patients already on statin therapy, reduces the rate of major vascular events such as myocardial infarction and stroke (REDUCE-IT trial). We sought to assess how often patients with asymptomatic carotid stenosis (CS) have elevated TG levels and factors associated with high TG values. Methods: Patients enrolled in the Carotid Revascularization and Medical Management for Asymptomatic Carotid Stenosis Trial (CREST 2) were analyzed. Baseline lipid profiles were evaluated to determine high TG treatment eligibility as per the REDUCE-IT trial. We also evaluated baseline use of pharmacologic treatment for high TG levels. Demographic factors and baseline medical conditions were studied in relation to high (>150 mg/dl) TG values. Chi square and t tests were used to assess baseline factors and abnormal TG values. Results: As of August 10, 2020, 1655 of 1689 randomized patients (mean age 69.7 years, 61% men) had baseline lipid profiles suitable for analysis. Treatment eligibility according to REDUCE-IT (LDL 41-100 mg/dl, TG>150 mg/dl) was present in 21% (345) of subjects. In these patients, the median TG value was 205 (IQR 93) mg/dl. Fibrate medications were used at baseline in 4.5% of patients. Analysis of demographic and medical history factors and TG values greater than 150 mg/dl is found in the Table. There was significant positive correlation between baseline hemoglobin A1C and triglyceride values (p<0.0001) Conclusions: One in five patients in CREST 2 has TG values that potentially justify pharmacologic treatment. Elevated TG levels are most correlated with diabetes, hypertension, obesity, decreased physical activity, and heart disease. Clinicians should investigate treatment of elevated TG levels as a component of intensive medical therapy for stroke prevention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".