Lipoprotein(a) and the effect of alirocumab on coronary and non-coronary revascularization following acute coronary syndrome
Bibliographic record
Abstract
Abstract Background Many patients require arterial revascularization after an index ACS. Lipoprotein(a) is thought to play a pathogenic role in atherothrombosis. In the ODYSSEY OUTCOMES trial, the PCSK9 inhibitor alirocumab reduced major adverse cardiovascular events after ACS, with greater reduction among those with higher lipoprotein(a). Objectives We determined whether the risk of first coronary or any (coronary, peripheral artery or carotid) revascularization after ACS was modified by the level of lipoprotein(a) and treatment with alirocumab or placebo. Methods The ODYSSEY OUTCOMES trial (NCT01663402) compared alirocumab with placebo in 18,924 patients with ACS and elevated atherogenic lipoproteins despite optimized statin treatment. Treatment effects were summarized by competing-risks proportional hazard models. Results A total of 1559 (8.2%) patients had coronary, 204 (1.1%) peripheral artery, and 40 (0.2%) carotid revascularization after randomization. Alirocumab reduced first coronary revascularization (9.6% vs. 11.3% at 4 years; hazard ratio [HR] 0.88, 95% confidence interval [CI] 0.80–0.97; p=0.01) and any first revascularization (10.8% vs. 13.0%; 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 (ptrend <0.0001) and inversely related to treatment HRs (ptrend <0.001). The greatest benefits of alirocumab on coronary or any revascularization were observed in patients with baseline lipoprotein(a) in the top quartile (≥59.6 mg/dL) (figures). Conclusions Alirocumab reduced revascularization after ACS. The risk of revascularization and reduction in that risk with alirocumab were greatest in patients with elevated lipoprotein(a) at baseline. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): SanofiRegeneron Pharmaceuticals
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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) | 0.001 | 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 | 0.001 | 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".