Cost Implications of the Use of Ramipril in High-Risk Patients Based on the Heart Outcomes Prevention Evaluation (HOPE) Study
Bibliographic record
Abstract
BACKGROUND: The HOPE study has demonstrated that ramipril is beneficial (ie, prevents cardiovascular death, myocardial infarction, and stroke) for a broad range of patients without evidence of left ventricular dysfunction or heart failure who are at high risk for cardiovascular event. In this study, we report the cost implications, in both the United States and Canada, of the use of ramipril after the HOPE study. METHODS AND RESULTS: A third-party perspective was chosen (Medicare for the United States and Ministry of Health for Canada). We calculated the costs of the management strategies of ramipril and placebo. An annual discount rate of 3% was used over the 4.5 years of follow-up. Sensitivity analyses were performed. Costs are reported in United States dollars and in Canadian dollars, respectively. The total costs per patient (including acquisition costs of ramipril) were not different between the groups in both countries (United States, $13 520 versus $13 631; Canada, $8702 versus $8588). From the distribution of cases in the bootstrap analysis, we found that 90% of cases fall either into a cost-neutral or cost-saving situation (64% in United States and 27% in Canada) or into a cost-effectiveness situation with an incremental cost-effectiveness ratio <$10 000 (in respective currency) per primary event saved. CONCLUSIONS: On the basis of these results, we suggest that the use of ramipril is likely to represent an efficient use of resources in both countries. These findings support the use of ramipril in populations included in the HOPE study.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".