Lipoprotein(a) and the Effect of Alirocumab on Revascularization After Acute Coronary Syndrome
Bibliographic record
Abstract
BACKGROUND: Many patients require revascularization after index acute coronary syndrome (ACS). Lipoprotein(a) is thought to play a pathogenic role in atherothrombosis. In ODYSSEY OUTCOMES, alirocumab reduced major adverse cardiovascular events after ACS, with greater reduction among those with higher lipoprotein(a) levels. We explored whether risk of revascularization after ACS was modified by the level of lipoprotein(a) and treatment with alirocumab or placebo. METHODS: In ODYSSEY OUTCOMES alirocumab was compared with placebo in 18,924 patients with ACS and elevated atherogenic lipoprotein levels despite optimized statin treatment. In this post hoc analysis, treatment effects are summarized using competing risks proportional hazard models. RESULTS: A total of 1559 (8.2%) patients had coronary, 204 (1.1%) had limb, and 40 (0.2%) had carotid revascularization. Alirocumab reduced coronary revascularization (2.8 vs 3.2 events per 100 patient-years; hazard ratio [HR], 0.88 [95% confidence interval (CI), 0.80-0.97]; P = 0.01) and any revascularization (3.2 vs 3.7 events per 100 patient-years; HR, 0.85 [95% CI, 0.78-0.94]; P = 0.001). Baseline lipoprotein(a) quartile was directly associated with risk of coronary or any revascularization in the placebo arm and inversely related to treatment HRs (all P for trend < 0.01). Alirocumab produced the greatest reduction of coronary revascularization in patients with baseline lipoprotein(a) in the top quartile (≥ 59.6 mg/dL; HR, 0.69 [95% CI, 0.57-0.84]), but no apparent reduction in the bottom quartile (HR, 1.00 [95% CI, 0.82-1.22]). Findings were similar for the effect of alirocumab on any revascularization. CONCLUSIONS: Alirocumab reduced revascularization rates after ACS. The risk of revascularization and reduction in that risk with alirocumab were greatest in patients with elevated lipoprotein(a) 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".