How Cost-Effective Is the Treatment of Dyslipidemia in Patients With Diabetes but Without Cardiovascular Disease?
Bibliographic record
Abstract
OBJECTIVE: Epidemiological studies have shown that the risk of myocardial infarction (MI) in diabetic patients without cardiovascular disease (CVD) is comparable to the risk of MI in patients with CVD. We used a validated Markov model to compare the long-term costs and benefits of treating dyslipidemia in diabetic patients without CVD versus treating CVD patients without diabetes in the U.S. The generalizability and robustness of these results were also compared across six other countries (Canada, France, Germany, Italy, Spain, and the U.K.). RESEARCH DESIGN AND METHODS: With use of the Cardiovascular Disease Life Expectancy Model, cost effectiveness simulations of simvastatin treatment were performed for men and women who were 40-70 years of age and had dyslipidemia. We forecast the long-term risk reduction in CVD events after treatment. On the basis of the Scandinavian Simvastatin Survival Study results, we assumed a 35% reduction in LDL cholesterol and an 8% rise in HDL cholesterol. RESULTS: In the U.S., treatment with simvastatin for CVD patients without diabetes was cost-effective, with estimates ranging from $8,799 to $21,628 per year of life saved (YOLS). Among diabetic individuals without CVD, lipid therapy also appeared to be cost-effective, with estimates ranging from $5,063 to $23,792 per YOLS. In the other countries studied, the cost effectiveness of treating diabetes in the absence of CVD was comparable to the cost effectiveness of treating CVD in the absence of diabetes. CONCLUSIONS: Among diabetic men and women who do not have CVD, lipid therapy is likely to be as effective and cost-effective as treating nondiabetic individuals with CVD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".