Estimating potential cardiovascular health benefits of improved population level control of LDL cholesterol through a twice-yearly siRNA-based approach: A simulation study of a health-system level intervention
Bibliographic record
Abstract
BACKGROUND AND AIMS: Inclisiran, an siRNA therapy, consistently reduces low-density lipoprotein cholesterol (LDL-C) with twice-yearly dosing. Potential cardiovascular benefits of implementing inclisiran at a population level, added to statins, were evaluated through simulation. METHODS: For each participant in the ORION-10 and ORION-11 trials comparing inclisiran with placebo, baseline 10-year cardiovascular risk was estimated using the SMART equation. The time-adjusted LDL-C difference from baseline observed 90-540 days after baseline was assumed to persist and used to estimate potential reduction in 10-year cardiovascular risk. Impact on 500,000 ORION-like individuals was simulated with Monte-Carlo. RESULTS: Mean baseline LDL-C and predicted 10-year major vascular risk among patients randomized to inclisiran (n = 1288) versus placebo (n = 1264) were 2.66 mmol/L versus 2.60 mmol/L and 24.9% versus 24.6%, respectively. Placebo-corrected time-adjusted absolute reduction in LDL-C with inclisiran was -1.32 mmol/L (95% CI -1.37 to -1.26; p < 0.001), which predicted a 10-year cardiovascular risk of 18.1% with inclisiran versus 24.7% with placebo (absolute difference [95% CI], -6.99% [-7.33 to -6.66]; p < 0.001) NNT 15. Extrapolating to 500,000 inclisiran-treated individuals, the model predicted large population shifts towards lower quintiles of risk with fewer remaining in high-risk categories; 3350 to 471 (≥80% risk), 11,793 to 3332 (60-<80% risk), 52,142 to 22,665 (40-<60% risk), 197,752 to 141,014 (20-<40% risk), and more moving into the lowest risk category (<20%) from 234,963 to 332,518. CONCLUSIONS: Meaningful gains in population health might be achieved over 10 years by implementing at-scale approaches capable of providing substantial and sustained reductions in LDL-C beyond those achievable with statins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".