992-P: Coronary Artery Calcium Scoring to Improve Statin Use in Adults with Type 2 Diabetes Not on Statin Therapy—Anticipated Cost Utility
Bibliographic record
Abstract
Background: Statin use in adults with diabetes is sub-optimal. Coronary artery calcium (CAC) scoring can provide a visual quantification of CV risk and may improve statin initiation and long-term use. Objective: Estimate the cost-utility of CAC scoring compared to traditional CV risk counselling only, for improving statin use in adults with diabetes who have not previously had a CV event, and are not on statin therapy. Methods: Probabilistic Markov model from a health system perspective, over a 10-year time horizon. The effectiveness of CAC scoring was obtained from the EISNER trial. Outcomes included acute coronary syndrome (ACS), stroke, heart failure, mortality, and radiation-induced cancer. Outcome rates and costs were obtained from an analysis of Alberta administrative data, and from publicly available sources. The base population modeled was men age 60, with additional analyses for other age, sex-groups and scenarios. Results: CAC scoring increased statin use to 44% vs 25% with traditional CV risk counselling only. There were 8.4 fewer ACS, 5.5 fewer strokes, and 1.6 fewer deaths / 1000 individuals. Costs were slightly lower (-$14, 95% CI [-$402-(+$208)]) (CAD), for a small benefit in quality-adjusted life years (+0.01, [0.00-0.02]), with mean ICER of $7,132 (-$27,828-(+$65,513)) / QALY. At a willingness-to-pay of $50,000 / QALY, CAC was cost-effective in 95% of trials. CAC scoring was not cost-effective in younger men or in women, but could be cost-effective or cost savings in men (age ≥ 40) and older women (age ≥ 60) who refuse statin therapy, but would be persuaded by personalized evidence of coronary artery disease (CAC score > 10). Conclusion: CAC scoring may be cost-effective in older men not receiving statins, and in men or older women who agree to use statins if shown personalized evidence of coronary artery disease. Disclosure D.Lau: None. G.J.Pearson: Advisory Panel; HLS Therapeutics Inc., Novartis Pharmaceuticals Corporation, Pharmacience, Consultant; Trimedic Therapeutics. P.Raggi: None. S.Klarenbach: Research Support; Bayer Inc., Allergan, GlaxoSmithKline plc., CSL Behring, Lundbeck, University Hospital Foundation (University of Alberta), Purdue 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".