Achievement of ESC/EAS LDL-C treatment goals after an acute coronary syndrome with statin and alirocumab
Bibliographic record
Abstract
AIMS: European guidelines set low-density lipoprotein cholesterol (LDL-C) treatment goals <1.4 mmol/L after acute coronary syndrome (ACS), and <1.0 mmol/L for patients with recurrent cardiovascular events ≤2 years. Many ACS patients do not achieve these goals on statin alone. We examined actual goal achievement with alirocumab and projected achievement with ezetimibe, either added to optimized statin therapy. METHODS AND RESULTS: The ODYSSEY OUTCOMES trial (NCT01663402) compared alirocumab with placebo in 18 924 patients with recent ACS and hyperlipidaemia despite high-intensity or maximum-tolerated statin therapy. This subanalysis comprised 17 589 patients with LDL-C ≥1.4 mmol/L at baseline who did not receive ezetimibe treatment. High-intensity statin treatment was used in 88.8%. Median (interquartile range) baseline LDL-C was 2.3 (1.9-2.7) mmol/L. With alirocumab, 94.6% of patients achieved LDL-C <1.4 mmol/L at ≥1 post-baseline measurement vs. 17.3% with placebo. Among 2236 patients with a previous cardiovascular event within 2 years (before the qualifying ACS), 85.2% vs. 3.5%, respectively, achieved LDL-C <1.0 mmol/L. Among patients not treated with ezetimibe, we projected that its use would have achieved LDL-C <1.4 and <1.0 mmol/L in 10.6 and 0%, respectively, at baseline (assuming 18 ± 3% reduction of LDL-C). CONCLUSION: Among patients with recent ACS and LDL-C ≥1.4 mmol/L despite optimized statin therapy, the addition of alirocumab allowed 94.6% to achieve the 2019 European guideline LDL-C goal <1.4 mmol/L, and 85.2% of those with recurrent cardiovascular events to achieve <1.0 mmol/L. In contrast, the addition of ezetimibe to optimized statin therapy was projected to achieve LDL-C <1.4 mmol/L in only 10.6% of patients at baseline.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| 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".