Dyslipidemia: Reduction in Estimated Risk for Coronary Artery Disease After Use of Ezetimibe with a Statin
Bibliographic record
Abstract
BACKGROUND: The aim of lipid-lowering treatment is to reduce the risk for cardiovascular events. Patients not at target lipid levels while on hydroxymethylglutaryl coenzyme A reductase inhibitors (statin) monotherapy are at increased cardiovascular risk. OBJECTIVE: To describe the impact of coadministration of ezetimibe with a statin on the estimated 10 year risk for coronary artery disease (E-R(CAD)) in patients with hypercholesterolemia and above-target low-density lipoprotein cholesterol (LDL-C) levels after statin monotherapy. METHODS: Post hoc analysis was conducted of a prospective, open-label, single-cohort, multicenter Canadian study of 953 patients who were treated for 6 weeks with ezetimibe 10 mg/day coadministered with their current statin at an unaltered dose. For each patient, E-R(CAD) at baseline and at 6 weeks was calculated using the Framingham model. The primary outcome measure of the analysis was the change in E-R(CAD). RESULTS: A total of 825 patients with data at baseline and 6 weeks were included in the analysis. There were 423 (51.3%) patients with hypertension, 107 (13.0%) with diabetes mellitus but not metabolic syndrome, 160 (19.4%) with metabolic syndrome but not diabetes mellitus, and 235 (28.5%) with both diabetes mellitus and metabolic syndrome. After 6 weeks of ezetimibe coadministration with statin therapy, mean E-R(CAD) was reduced by 4.1% from 15.6% to 11.5%, which is equivalent to a 25.3% risk reduction (p < 0.001). Of the 225 (27.3%) patients with high E-R(CAD) (> or = 20.1%) at baseline, 144 (64.0%) converted to a lower E-R(CAD) category (p < 0.001). Patients with both diabetes mellitus and metabolic syndrome experienced the highest mean percent reduction in E-R(CAD) of -29.4% (p < 0.001). CONCLUSIONS: For patients with above-target LDL-C levels while on statin monotherapy, coadministration of ezetimibe with the statin is effective in significantly reducing the E-R(CAD).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".