Cost-effectiveness of ticagrelor versus clopidogrel in patients with acute coronary syndromes in Canada
Bibliographic record
Abstract
BACKGROUND: Ticagrelor demonstrated a significant reduction in major cardiac events in patients with acute coronary syndrome (ACS) compared with clopidogrel in the Platelet Inhibition and Patient Outcomes (PLATO) trial. The objective of this study was to assess the cost-effectiveness of ticagrelor compared with clopidogrel in ACS patients from the perspective of the Canadian publicly funded health care system. METHODS: A two-part model was developed consisting of a 1-year decision tree and a lifetime Markov model. Within the decision tree, patients remained event-free, experienced a nonfatal myocardial infarction, a nonfatal stroke, or death due to vascular or nonvascular related causes based on data from the PLATO trial. The lifetime Markov model followed these patients and allowed for subsequent myocardial infarction, stroke, and death. Patient utility and resource use were derived from the PLATO trial. Transition probabilities and specific Canadian unit costs were derived from published sources. Univariate and probabilistic sensitivity analyses were conducted. RESULTS: In the base case lifetime analysis, treatment with ticagrelor resulted in more years of life per person (0.097), more quality-adjusted life years per person (QALYs, 0.084), and an incremental cost per QALY gained of $9,745 (Canadian$), assuming a generic cost for clopidogrel. A probabilistic sensitivity analysis demonstrated the robustness of the base case analysis, with a 93% probability of being below $20,000 per QALY gained and a 99% probability of being below $30,000 per QALY gained. CONCLUSION: Ticagrelor is a clinically superior and cost-effective option for the prevention of thrombotic events among ACS patients in Canada.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".