Evaluating Value Beyond Efficacy: A Meta-Analytic Assessment of Inclisiran’s Cost-Effectiveness in Cardiovascular Prevention
Bibliographic record
Abstract
Background/Objectives: Cardiovascular diseases continue to be the foremost global cause of morbidity and mortality, representing about 40% of all causes of death. Atherosclerotic cardiovascular disease is the most common and clinically important type of these, occurring when cholesterol accumulates over time in the artery intima, which induces an inflammatory process that leads to the production of atherosclerotic plaques. Nowadays, lipid profile alterations and high/very high cardiovascular risk can be observed in more and more patients. Combination therapy, which includes high-intensity statins, ezetimibe, bempedoic acid, and PCSK9-targeted medicines, can lower LDL-C by more than 80%, which is far more than the 50% that statin monotherapy usually achieves. Thus, novel lipid-lowering therapies are needed, as current agents—though effective in reducing cardiovascular events—leave considerable residual risk in many patients. Methods: The aim of our study was to evaluate the cost-effectiveness of Inclisiran and its association with standard of care for the prevention of cardiovascular events across multiple international settings, in articles that reported quality-adjusted life years gained and cost-effectiveness metrics. Results: Our findings suggest that the cost-effectiveness of Inclisiran is highly context-dependent, shaped by local pricing, population risk, and system-level capacity. While Inclisiran demonstrates potential economic value in high-income settings or among high-risk patients, its widespread adoption for primary prevention appears unjustified under current conditions. Conclusions: Policymakers should consider risk-based targeting, price renegotiation, and performance-based reimbursement models to improve the value proposition of such interventions.
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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 | 0.061 | 0.108 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.056 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".