Effect of alirocumab on cardiovascular outcomes after acute coronary syndromes according to age: an ODYSSEY OUTCOMES trial analysis
Bibliographic record
Abstract
AIMS: Lowering low-density lipoprotein cholesterol (LDL-C) reduces cardiovascular risk irrespective of age, but the evidence is less strong for older patients. METHODS AND RESULTS: This prespecified analysis from ODYSSEY OUTCOMES compared the effect of alirocumab vs. placebo in 18 924 patients with recent acute coronary syndrome (ACS) according to age. We examined the effect of assigned treatment on occurrence of the primary study outcome, a composite of coronary heart disease death, myocardial infarction, ischaemic stroke, or unstable angina requiring hospitalization [major adverse cardiovascular event (MACE)] and all-cause death. Relative risk reductions were consistent for patients ≥65 vs. <65 years for MACE [hazard ratio (HR) 0.78, 95% confidence interval (CI) 0.68-0.91 vs. 0.89, 0.80-1.00; Pinteraction = 0.19] and all-cause death [HR 0.77, 0.62-0.95 vs. 0.94, 0.77-1.15; Pinteraction = 0.46], and consistent for MACE when dichotomizing at age 75 years (HR 0.85, 0.64-1.13 in ≥75 vs. 0.85, 0.78-0.93 in <75, Pinteraction = 0.19). When considering age as a continuous variable in regression models, advancing age increased risk of MACE, as well as the absolute reduction in MACE with alirocumab, with numbers-needed-to-treat for MACE at 3 years of 43 (25-186) at age 45 years, 26 (15-97) at age 75 years, and 12 (6-81) for those at age 85 years. Although adverse events were more frequent in older patients, there were no differences between alirocumab and placebo. CONCLUSION: In patients with recent ACS, alirocumab improves outcomes irrespective of age. Increasing absolute benefit but not harm with advancing age suggests that LDL-C lowering is an important preventive intervention for older patients after ACS.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".