Inclisiran and cardiovascular events: a patient-level analysis of phase III trials
Bibliographic record
Abstract
BACKGROUND: Inclisiran, an siRNA administered twice-yearly, significantly reduced LDL cholesterol (LDL-C) in Phase III trials. Whether lowering LDL-C with inclisiran translates into a lower risk of cardiovascular (CV) events is not yet established. METHODS AND RESULTS: Patient-level, pooled analysis of ORION-9, -10 and -11, included patients with heterozygous familial hypercholesterolaemia, atherosclerotic CV disease (ASCVD), or ASCVD risk equivalent on maximally tolerated statin-therapy, randomized 1:1 to receive 284 mg inclisiran or placebo on Days 1, 90, and 6-monthly thereafter for 18 months. Prespecified exploratory endpoint of major cardiovascular events (MACEs) included non-adjudicated CV death, cardiac arrest, non-fatal myocardial infarction (MI), and fatal and non-fatal stroke, evaluated as part of safety assessments using a standard Medical Dictionary for Regulatory Activities basket. Although not prespecified, total fatal and non-fatal MI, and stroke were also evaluated. Mean LDL-C at baseline was 2.88 mmol/L. At Day 90, the placebo-corrected percentage reduction in LDL-C with inclisiran was 50.6%, corresponding to an absolute reduction of 1.37 mmol/L (both P < 0.0001). Among 3655 patients over 18 months, 303 (8.3%) experienced MACE, including 74 (2.0%) fatal and non-fatal MIs, and 28 (0.8%) fatal and non-fatal strokes. Inclisiran significantly reduced composite MACE [OR (95% CI): 0.74 (0.58-0.94)], but not fatal and non-fatal MIs [OR (95% CI): 0.80 (0.50-1.27)] or fatal and non-fatal stroke [OR (95% CI): 0.86 (0.41-1.81)]. CONCLUSION: This analysis offers early insights into the potential CV benefits of lowering LDL-C with inclisiran and suggests potential benefits for MACE reduction. These findings await confirmation in the larger CV outcomes trials of longer duration.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".