Abstract WP394: Insights Into The Cost-effectiveness Of Blood Pressure Management For Secondary Stroke Prevention
Bibliographic record
Abstract
Objective: To determine the relationship between the risk of dementia, risk of recurrent stroke and other factors on the cost-effectiveness of intensive blood pressure management for secondary stroke prevention. Methods: A Markov model incorporated data from the PROGRESS trial. It included cost (2011$) and utility estimates from the literature. Intensive management was a 9mmHg reduction in systolic blood pressure compared to usual management. The cost of the additional medications was assumed to be the cost of perindopril. Relative risk terms modified the probability of adverse events with intensive treatment. Outcomes were recurrent stroke, dementia, recurrent stroke with dementia and death. Quality of life with intensive management was lower than usual treatment. Assumptions were tested with one-way and multi-way sensitivity analyses. Results: For the base case analysis, the lifetime costs of usual management were $80,200 with 5.626 quality adjusted life years (QALYs). The lifetime costs of intensive management were $85,500 with 5.627 QALYs. The incremental cost-effectiveness ratio (ICER) was $5 million, thus the added costs of intensive management were not offset by increased quality of life. However, this conclusion was sensitive to several assumptions. Intensive management became cost-effective (ICER < $100,000) when (1) ages < 52 years were treated (base case: 64 years), (2) the quality of life with intensive management increased slightly, by 0.01, (3) the cost of BP medications was $270/year, (4) the probability of recurrent stroke was ≥ 5.4% (base case: 3.7%), or (5) the relative risk of recurrent stroke with intensive management was ≤ 0.69 (base case: 0.77). The probability of dementia or the relative risk of dementia did not influence the model. If the cost of medications to lower BP by 9 mmHg was ≤ $260/year, intensive management was dominant with lower lifetime costs and greater QALYs. Conclusion: With the base case assumptions, intensive BP management was not the cost-effective choice. Nevertheless cost-effective management can be achieved depending on the age of the cohort and the cost of maintenance medications. This analysis highlights important factors to be considered during the planned cost-effectiveness analysis of the SPS3 trial.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".