Baseline triglycerides and non-HDL-C and apoB goal attainment in the ORION-10 and ORION-11 trials
Bibliographic record
Abstract
Abstract Introduction Elevated triglyceride (TG) levels contribute to the total burden of circulating atherogenic lipoprotein levels and are associated with increased cardiovascular (CV) risk. LDL-C underestimates risk in patients with elevated TG. Therefore, 2019 ESC/EAS guidelines recommend apoB or non-HDL-C as secondary lipid goals for patients with TG >150mg/dL. Purpose To assess the impact of inclisiran on apoB and non-HDL-C goal attainment across a range of TG levels among patients with atherosclerotic CV disease (ASCVD). Methods The ORION-10 and ORION-11 trials included 3178 patients with ASCVD and LDL-C >70mg/dl despite maximally tolerated statins randomized to inclisiran or placebo (1:1). Pre-specified secondary endpoints were placebo-corrected changes in lipids at Day 510. For this analysis patients were stratified by TG quartiles at baseline within each trial. The proportion of individuals attaining apoB <55 mg/dL or non-HDL-C <70mg/dl within each trial were assessed across TG strata and the likelihood of goal attainment within TG strata assessed using logistic regression. Results In ORION-10, TG quartiles were ≤94, 94 to ≤128, 128 to ≤181 and >181mg/dl respectively and in ORION-11 corresponding values were ≤101, 101 to ≤135, 135 to ≤183 and >183mg/dl. As compared to placebo a significantly greater proportion of patients randomised to inclisiran attained apoB goals within each TG strata (Table). Similar results were observed for non-HDL-C. Conclusion Among patients with ASCVD on maximally tolerated statin and high TG levels, attainment of apoB and non-HDL-C secondary lipid targets was more likely with inclisiran than placebo. Inclisiran may be a useful therapeutic option for patients with atherogenic dyslipidaemia. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): The Medicines Company
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| 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 | 0.002 |
| 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".