Alirocumab as Add-On to Atorvastatin Versus Other Lipid Treatment Strategies: ODYSSEY OPTIONS I Randomized Trial
Bibliographic record
Abstract
CONTEXT: Despite current standard of care, many patients at high risk of cardiovascular disease (CVD) still have elevated low-density lipoprotein cholesterol (LDL-C) levels. Alirocumab is a fully human monoclonal antibody inhibitor of proprotein convertase subtilisin/kexin type 9. OBJECTIVE: The objective of the study was to compare the LDL-C-lowering efficacy of adding alirocumab vs other common lipid-lowering strategies. DESIGN, PATIENTS, AND INTERVENTIONS: Patients (n = 355) with very high CVD risk and LDL-C levels of 70 mg/dL or greater or high CVD risk and LDL-C of 100 mg/dL or greater on baseline atorvastatin 20 or 40 mg were randomized to one of the following: 1) add-on alirocumab 75 mg every 2 weeks (Q2W) sc; 2) add-on ezetimibe 10 mg/d; 3) double atorvastatin dose; or 4) for atorvastatin 40 mg regimen only, switch to rosuvastatin 40 mg. For patients not achieving protocol-defined LDL-C goals, the alirocumab dose was increased (blinded) at week 12 to 150 mg Q2W. MAIN OUTCOME MEASURE: The primary end point was percentage change in calculated LDL-C from baseline to 24 weeks (intent to treat). RESULTS: Among atorvastatin 20 and 40 mg regimens, respectively, add-on alirocumab reduced LDL-C levels by 44.1% and 54.0% (P < .001 vs all comparators); add-on ezetimibe, 20.5% and 22.6%; doubling of atorvastatin dose, 5.0% and 4.8%; and switching atorvastatin 40 mg to rosuvastatin 40 mg, 21.4%. Most alirocumab-treated patients (87.2% and 84.6%) achieved their LDL-C goals. Most alirocumab-treated patients (86%) maintained their 75-mg Q2W regimen. Treatment-emergent adverse events occurred in 65.4% of alirocumab patients vs 64.4% ezetimibe and 63.8% double atorvastatin/switch to rosuvastatin (data were pooled). CONCLUSIONS: Adding alirocumab to atorvastatin provided significantly greater LDL-C reductions vs adding ezetimibe, doubling atorvastatin dose, or switching to rosuvastatin and enabled greater LDL-C goal achievement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".