Inclisiran in patients with prior myocardial infarction: A post hoc pooled analysis of the ORION-10 and ORION-11 Phase 3 randomised trials
Bibliographic record
Abstract
BACKGROUND AND AIMS: Among patients with prior myocardial infarction (MI), the risk of future ischaemic cardiovascular events is increased, and intensive lipid-lowering therapy (LLT) is indicated to achieve guideline-recommended low-density lipoprotein cholesterol (LDL-C) goals. Here, the efficacy and safety of inclisiran, a small interfering ribonucleic acid, were evaluated in patients with or without prior MI from the pooled ORION-10 and ORION-11 Phase 3 trials. METHODS: Patients (n = 2636) were randomised 1:1 to receive 284 mg inclisiran (300 mg inclisiran sodium) or placebo on Day 1, Day 90, and 6-monthly thereafter over 18 months, along with background oral LLT, including statins. Of these, 1643 (62.3%) patients had an MI prior to randomisation, stratified as recent (>3 months to <1 year) or remote (≥1 year), and 993 (37.7%) patients were without a prior MI. The percentage change in LDL-C from baseline and safety were assessed. RESULTS: Baseline characteristics were well balanced across the treatment arms and MI strata. The mean (95% confidence interval) placebo-corrected LDL-C reductions from baseline to Day 510 with inclisiran were 52.6% (40.1, 65.1), 50.4% (47.0, 53.8), and 51.6% (47.4, 55.9) for recent, remote, and no prior MI, respectively. Corresponding time-adjusted LDL-C reductions were 50.0% (41.4, 58.7), 52.2% (49.8, 54.7), and 51.2% (48.1, 54.2). In each MI stratum, treatment-emergent adverse events (TEAEs) at the injection site (all mild to moderate) were observed more in inclisiran-treated patients than placebo, without an excess of other TEAEs. CONCLUSIONS: Inclisiran provided effective and consistent LDL-C lowering, irrespective of MI status.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".