Efficacy of alirocumab according to background statin type and dose: pooled analysis of 8 ODYSSEY Phase 3 clinical trials
Bibliographic record
Abstract
Low-density lipoprotein cholesterol (LDL-C) reductions with the PCSK9 monoclonal antibody alirocumab may be affected by background statin dose due to increased PCSK9 levels with higher statin doses. Data from 8 Phase 3 trials conducted with background statin (n = 4629) were pooled by alirocumab dose (75 or 150 mg every 2 weeks) and control (placebo/ezetimibe), and analyzed by background statin type/dose. Overall, 58.4% received high-dose statins (atorvastatin 40-80 mg, rosuvastatin 20-40 mg, simvastatin 80 mg), 28.6% moderate-dose statins (atorvastatin 20-<40 mg, rosuvastatin 10-<20 mg, simvastatin 40-<80 mg), and 12.9% low-dose statins (atorvastatin <20 mg, rosuvastatin <10 mg, simvastatin <40 mg). Mean baseline PCSK9 levels were higher with high versus moderate and low statin doses (318.5 vs 280.6 ng/mL). Baseline LDL-C levels were similar across pools, regardless of statin intensity. No associations were observed between statin type/dose and LDL-C % change from baseline or % of patients achieving LDL-C goals at Week 24 for alirocumab versus control (interaction P-values non-significant). Incidence of adverse events was similar for alirocumab versus control, except for a higher rate of injection-site reactions with alirocumab. In summary, alirocumab provided consistent LDL-C reductions and was generally well tolerated independent of background statin type/dose.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".