Cost-effectiveness of intensive lipid lowering therapy with 80 mg of atorvastatin, versus 10 mg of atorvastatin, for secondary prevention of cardiovascular disease in Canada.
Bibliographic record
Abstract
BACKGROUND: The TNT study compared high dose atorvastatin (80 mg) versus moderate atorvastatin (10 mg) treatment in 10,001 patients with stable coronary heart disease (CHD), over 4.9 years. Intensive lipid-lowering with atorvastatin (80 mg) reduced major cardiovascular events by 22%. OBJECTIVES: To assess the cost-effectiveness of intensive lipid-lowering versus moderate lipid lowering treatment from the perspective of the Canadian Ministries of Health. METHODS: A lifetime Markov model was developed to predict cardiovascular (CV) events, costs, survival, and quality-adjusted life years (QALYs) for CHD patients receiving 80 mg versus 10 mg of atorvastatin. Predictions were also made for 10- and 5-year horizons. Treatment-specific event risks were used until five years. Beyond year five, equivalent CV risks were assumed for all patients. Medical-care costs and post-event survival were estimated using Canadian data. Health utility scores were obtained from published studies. Benefits and costs were discounted 5% annually. Probabilistic and deterministic sensitivity analyses were performed. RESULTS: Treatment with atorvastatin (80 mg) over a lifetime horizon resulted in increased costs (Can$16,542 vs. Can$15,365), survival (10.12 vs. 10.03 life years), and QALYs (7.71 vs. 7.61) per patient compared with atorvastatin (10 mg), yielding an incremental cost-effectiveness of Can$12,946 per life year gained and Can$11,969 per QALY. The incremental cost per QALY remained below Can$50,000 in 98.1% of 1000 simulations. Results were robust to variations in event hazard ratios, costs, health utility values, and discount rate. CONCLUSION: Intensive atorvastatin (80 mg) treatment is predicted to be cost-effective versus atorvastatin (10 mg) for CHD 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".