Efficacy and safety of alirocumab vs ezetimibe in statin-intolerant patients, with a statin rechallenge arm: The ODYSSEY ALTERNATIVE randomized trial
Bibliographic record
Abstract
BACKGROUND: Statin intolerance limits many patients from achieving optimal low-density lipoprotein cholesterol (LDL-C) concentrations. Current options for such patients include using a lower but tolerated dose of a statin and adding or switching to ezetimibe or other non-statin therapies. METHODS: ODYSSEY ALTERNATIVE (NCT01709513) compared alirocumab with ezetimibe in patients at moderate to high cardiovascular risk with statin intolerance (unable to tolerate ≥2 statins, including one at the lowest approved starting dose) due to muscle symptoms. A placebo run-in and statin rechallenge arm were included in an attempt to confirm intolerance. Patients (n = 361) received single-blind subcutaneous (SC) and oral placebo for 4 weeks during placebo run-in. Patients reporting muscle-related symptoms during the run-in were to be withdrawn. Continuing patients were randomized (2:2:1) to double-blind alirocumab 75 mg SC every 2 weeks (Q2W; plus oral placebo), ezetimibe 10 mg/d (plus SC placebo Q2W), or atorvastatin 20 mg/d (rechallenge; plus SC placebo Q2W) for 24 weeks. Alirocumab dose was increased to 150 mg Q2W at week 12 depending on week 8 LDL-C values. Primary end point was percent change in LDL-C from baseline to week 24 (intent-to-treat) for alirocumab vs ezetimibe. RESULTS: Baseline mean (standard deviation) LDL-C was 191.3 (69.3) mg/dL (5.0 [1.8] mmol/L). Alirocumab reduced mean (standard error) LDL-C by 45.0% (2.2%) vs 14.6% (2.2%) with ezetimibe (mean difference 30.4% [3.1%], P < .0001). Skeletal muscle-related events were less frequent with alirocumab vs atorvastatin (hazard ratio 0.61, 95% confidence interval 0.38-0.99, P = .042). CONCLUSIONS: Alirocumab produced greater LDL-C reductions than ezetimibe in statin-intolerant patients, with fewer skeletal-muscle adverse events vs atorvastatin.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".