4115Effect of alirocumab on recurrent cardiovascular events after acute coronary syndrome, according to the intensity of background statin treatment
Bibliographic record
Abstract
Abstract Background Statins are a cornerstone of therapy for coronary heart disease. We describe the effects of alirocumab (ALI) in patients (pts) with recent acute coronary syndrome (ACS) and dyslipidaemia per category of statin use. Methods ODYSSEY OUTCOMES compared ALI with placebo (PBO) in 18,924 pts with recent ACS and dyslipidaemia despite high-intensity/maximum tolerated statin (atorvastatin 40–80 mg/d or rosuvastatin 20–40 mg/d). Lower doses could be used if there were symptoms, laboratory abnormalities, or contraindications with higher doses. In cases of documented intolerance to ≥2 statins, pts could qualify on no statin treatment. Pts were randomized to ALI (75 mg SC Q2W, with possible uptitration to 150 mg Q2W) or PBO. Median follow-up was 2.8 years. Primary endpoint was major adverse cardiovascular events (MACE: CHD death, non-fatal MI, ischaemic stroke, or unstable angina requiring hospitalization). Pts were categorized by statin therapy at baseline: high intensity (88.8%), low or moderate intensity (8.7%), or no statin use (2.4%). In each category we determined the relative (hazard ratio [HR]) and absolute risk reductions (ARR) for MACE with ALI. Results Overall, ALI reduced MACE (HR 0.85, 95% CI 0.78–0.93; P<0.001). HRs were consistent across statin categories (Table). Baseline LDL-C increased across high-intensity, low/moderate-intensity, and no statin categories. Correspondingly, there was a gradient of the risk of MACE in the PBO group across these categories (10.8%, 10.7%, and 26%). With ALI treatment, the mean reduction in LDL-C from baseline to Month 4 increased across the 3 statin categories and correspondingly the ARRs for MACE were 1.3%, 3.2%, and 8.0% (P interaction <.001). LDL-C values and MACE events All patients High-intensity statin Low-/moderate-intensity statin No statin Interaction P-value N=18,924 (100%) N=16,811 (88.8%) N=1653 (8.7%) N=460 (2.4%) (treatment x statin category) PBO (N=9462) ALI (N=9462) PBO (N=8431) ALI (N=8380) PBO (N=804) ALI (N=849) PBO (N=227) ALI (N=233) LDL-C at baseline, mmol/L, mean (SE)* 2.39 (0.01) 2.39 (0.01) 2.35 (0.01) 2.35 (0.01) 2.41 (0.03) 2.43 (0.03) 3.76 (0.08) 3.82 (0.08) Change in LDL-C from baseline to Month 4, mmol/L, mean (SE) 0.03 (0.01) −1.4 (0.01) 0.03 (0.01) −1.37 (0.01) 0.01 (0.02) −1.47 (0.02) −0.004 (0.06) −2.27 (0.06) <0.001 MACE, n (%)* 1052 (11.1) 903 (9.5) 907 (10.8) 797 (9.5) 86 (10.7) 64 (7.5) 59 (26.0) 42 (18.0) HR (95% CI) 0.85 (0.78−0.93) 0.88 (0.80−0.96) 0.69 (0.50−0.95) 0.65 (0.43−0.96) 0.14 ARR (%) (95% CI) 1.6 (0.7−2.4) 1.3 (0.3−2.2) 3.2 (0.4−5.9) 8.0 (0.4−15.5) <0.001 *P<0.001 for difference among statin categories. Conclusions In ODYSSEY OUTCOMES, patients unable to receive high-intensity statin treatment showed greater ARRs with ALI, consistent with higher baseline LDL-C concentration and greater absolute LDL-C reduction. Patients unable to receive high-intensity statin treatment are an important group to consider for treatment with ALI after ACS. Acknowledgement/Funding Funded by Sanofi and Regeneron 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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".