Canadian Cost-Effectiveness of Coronary Artery Calcium Screening Based on the Multi-Ethnic Study of Atherosclerosis
Bibliographic record
Abstract
Background: Cost-effectiveness of testing for coronary artery calcium (CAC) relative to other treatment strategies is not established in Canada. Objectives: The purpose of this study was to evaluate the cost-effectiveness of using CAC score-guided statin treatment compared with universal statin therapy among intermediate-risk, primary prevention patients eligible for statins. Methods: A state transition, microsimulation model used data from Canadian sources and the Multi-Ethnic Study of Atherosclerosis to simulate clinical and economic consequences of cardiovascular disease from a Canadian publicly funded health care system perspective. In the CAC score-guided treatment arm, statins were started when CAC ≥1. Outcome of interest was the incremental cost-effectiveness ratio at 5 and 10 years; an incremental cost-effectiveness ratio <$50,000 per quality-adjusted life year (QALY) gained was considered cost-effective. Sensitivity analyses examined uncertainty in model parameters. Results: Compared with universal statin treatment at 5 and 10 years, CAC score-guided statin treatment was projected to increase mean costs by $326 (95% CI: $325-$326) and $172 (95% CI: $169-$175), increase mean QALYs by 0.01 (95% CI: 0.01-0.01) and 0.02 (95% CI: 0.02-0.02), and cost $54,492 (95% CI: $52,342-$56,816) and $8,118 (95% CI: $7,968-$8,279) per QALY gained, respectively. The model was most sensitive to statin cost, CAC testing cost, adherence to statin monitoring, and disutility associated with daily statin use. At 5 years, CAC score-guided statin treatment was cost-effective when CAC test costs ranged from $80 to $160 in different scenarios. Conclusions: CAC score-guided statin initiation in comparison to universal statin treatment was borderline cost-neutral at 5 years and cost-effective at 10 years in statin-eligible Canadian patients at intermediate cardiovascular disease risk.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".