Efficacy and Safety of Alirocumab 150 mg Every 4 Weeks in Patients With Hypercholesterolemia Not on Statin Therapy: The ODYSSEY CHOICE II Study
Bibliographic record
Abstract
BACKGROUND: The PCSK9 antibody alirocumab (75 mg every 2 weeks; Q2W) as monotherapy reduced low-density lipoprotein-cholesterol (LDL-C) levels by 47%. Because the option of a monthly dosing regimen is convenient, ODYSSEY CHOICE II evaluated alirocumab 150 mg Q4W in patients with inadequately controlled hypercholesterolemia and not on statin (majority with statin-associated muscle symptoms), receiving treatment with fenofibrate, ezetimibe, or diet alone. METHODS AND RESULTS: Patients were randomly assigned to placebo, alirocumab 150 mg Q4W or 75 mg Q2W (calibrator arm), with dose adjustment to 150 mg Q2W at week (W) 12 if W8 predefined LDL-C target levels were not met. The primary efficacy endpoint was LDL-C percentage change from baseline to W24. Mean baseline LDL-C levels were 163.9 mg/dL (alirocumab 150 mg Q4W, n=59), 154.5 mg/dL (alirocumab 75 mg Q2W, n=116), and 158.5 mg/dL (placebo, n=58). In the alirocumab 150 mg Q4W and 75 mg Q2W groups (49.1% and 36.0% of patients received dose adjustment, respectively), least-squares mean LDL-C changes from baseline to W24 were -51.7% and -53.5%, respectively (placebo [+4.7%]; both groups P<0.0001 versus placebo). In total, 63.9% and 70.3% of alirocumab-treated patients achieved their LDL-C targets at W24. Treatment-emergent adverse events occurred in 77.6% (alirocumab 150 mg Q4W), 73.0% (alirocumab 75 mg Q2W), and 63.8% (placebo) of patients, with injection-site reactions among the most common treatment-emergent adverse events. CONCLUSIONS: Alirocumab 150 mg Q4W can be considered in patients not on statin with inadequately controlled hypercholesterolemia as a convenient option for lowering LDL-C. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT02023879.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".