Cost-utility analysis of low-dose pioglitazone in a population with prediabetes and a history of stroke and TIA
Bibliographic record
Abstract
Abstract BACKGROUND Among patients with type 2 diabetes and a history of strokes or transient ischemic attacks, pioglitazone significantly reduces the risk of recurrent stroke. The Insulin Resistance Intervention in Stroke (IRIS) trial found that pioglitazone also reduced the risks of stroke or transient ischemic attacks and new-onset diabetes among patients with insulin resistance. As reported by our previous work, the low-dose pioglitazone was found to provide most of the clinical benefit of high-dose pioglitazone, with fewer adverse effects. We report a model-based economic evaluation to determine the cost-effectiveness of the low-dose pioglitazone versus placebo. METHODS A lifetime Markov model, with an annual cycle length and five health states (event-free, myocardial infarction, stroke, new-onset diabetes, death), was developed. Transition probabilities were extracted from a subgroup of IRIS patients with insulin resistance, defined by a glycosylated hemoglobin level of 5.7% to 6.4% (mean follow-up of 5 years). Health state costs and utilities were based on public sources and literature data, respectively. Utilities were weighted by time spent in health states to calculate quality-adjusted life years (QALYs). The incremental cost per QALY gained was estimated for the population. Annual discount rates of 0%, 1.5%, and 3% were applied. In addition to deterministic analyses, probabilistic sensitivity analyses were conducted to deal with parameter uncertainty. The analyses were conducted from a Canadian public payer perspective in 2023 Canadian dollars. RESULTS The base case results indicated that over a lifetime, the expected costs were CAN$31,534 for low-dose pioglitazone and CAN$55,076 for placebo, resulting in a cost saving of CAN$23,542 in favor of the low-dose pioglitazone. Expected QALYs were 25.10 for the low-dose pioglitazone daily and 19.32 for placebo, resulting in a difference of 5.78 QALYs in favor of low-dose pioglitazone. Probability sensitivity analyses with varying discount rates confirmed these results. CONCLUSIONS Compared with placebo, low-dose pioglitazone is the dominant strategy. Key Points Question Compared with a placebo, is low-dose pioglitazone cost-effective in treating stroke/ transient ischemic attack (TIA) and new-onset diabetes in a simulated population with prediabetics? Findings Over a lifetime, the expected costs were CAN$31,534 for low-dose pioglitazone and CAN$55,076 for placebo, resulting in a cost saving of CAN$23,542 in favor of low-dose pioglitazone. Expected QALYs were 25.10 for low-dose pioglitazone daily and 19.32 for placebo, resulting in a difference of 5.78 QALYs in favor of low-dose pioglitazone. Sensitivity and scenario analyses confirmed the results. Meaning The model-based economic evaluation indicates that low-dose pioglitazone, compared with placebo, is the dominant strategy.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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) | 0.003 | 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".