Cost-effectiveness of clopidogrel, prasugrel and ticagrelor for dual antiplatelet therapy after acute coronary syndrome: a decision-analytic model
Bibliographic record
Abstract
BACKGROUND: The use of prasugrel or ticagrelor as part of dual antiplatelet therapy with acetylsalicylic acid after acute coronary syndrome (ACS) improves clinical outcomes relative to clopidogrel. The relative cost-effectiveness of these agents are unknown. We conducted an economic analysis evaluating 12 months of treatment with clopidogrel, prasugrel or ticagrelor after ACS. METHODS: We developed a fully probabilistic Markov cohort decision-analytic model using a lifetime horizon, from the perspective of the Ontario Ministry of Health and Long-Term Care. The model incorporated risks of death, recurrent ACS, heart failure, major bleeding and other adverse effects of treatment. Data on probabilities and utilities were obtained from the published literature where available. The primary outcome was quality-adjusted life-years (QALYs). RESULTS: Treatment with clopidogrel was associated with the lowest effectiveness (7.41 QALYs, 95% confidence interval [CI] 1.05-14.79) and the lowest cost ($39 601, 95% CI $8434-$111 186). Ticagrelor treatment had an effectiveness of 7.50 QALYs (95% CI 1.13-14.84) at a cost of $40 649 (95% CI $9327-$111 881). The incremental cost-effectiveness ratio (ICER) for ticagrelor relative to clopidogrel was $12 205 per QALY gained. Prasugrel had an ICER of $57 630 per QALY gained relative to clopidogrel. Ticagrelor was the preferred option in 90% of simulations at a willingness-to-pay threshold of $50 000 per QALY gained. INTERPRETATION: Ticagrelor was the most cost-effective agent when used as part of dual antiplatelet therapy after ACS. This conclusion was robust to wide variations in model parameters.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".