PCSK9 inhibition in patients with and without prior myocardial infarction or ischemic stroke: A pooled analysis of nine randomized-controlled studies of alirocumab
Bibliographic record
Abstract
BACKGROUND: Patients with prior cardiovascular events are at very high risk of recurrent events and may benefit from low-density lipoprotein cholesterol (LDL-C) lowering beyond that achieved with maximally tolerated statins. OBJECTIVE: To assess potential differences between the efficacy and safety of the proprotein convertase subtilisin/kexin type 9 inhibitor, alirocumab, in patients with vs without prior myocardial infarction (MI)/ischemic stroke. METHODS: Data (n = 4880) were pooled from nine ODYSSEY phase 3 trials of alirocumab 75/150 mg or 150 mg every 2 weeks, mostly on background statins ± other lipid-lowering therapies. Analyses were performed according to statin status, alirocumab dose, and control (placebo or ezetimibe). RESULTS: Baseline LDL-C, non-high-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and apolipoprotein B levels were lower and lipoprotein(a) higher in patients with than without prior MI/ischemic stroke. LDL-C levels were reduced from baseline to week 24 in patients with (51.1%-62.9%) and without (43.6%-58.3%) prior MI/ischemic stroke, with no significant interaction between prior MI/ischemic stroke status and LDL-C-lowering efficacy of alirocumab vs controls. Alirocumab significantly reduced other lipid/lipoproteins (including lipoprotein[a]) similarly in patients with/without MI/ischemic stroke. Week 24 LDL-C goal attainment rates for subgroups with/without prior MI/ischemic stroke on background statins were 74.1%-84.8% and 63.7%-74.7%, respectively. The safety profile of alirocumab was generally similar regardless of prior MI/ischemic stroke status. CONCLUSIONS: Alirocumab significantly reduced LDL-C and other atherogenic lipids/lipoproteins in patients with prior MI/ischemic stroke, and the majority of this very high cardiovascular risk population achieved LDL-C goals; efficacy and safety results were similar in patients without prior MI/ischemic stroke.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.022 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".