A cost–utility analysis of thrombopoietin receptor agonists for treating pediatric immune thrombocytopenia purpura after failure of first‐line therapies
Bibliographic record
Abstract
BACKGROUND: Thrombopoietin receptor agonists (TPO-RAs) have emerged as a recommended treatment for children with persistent and/or chronic immune thrombocytopenic purpura (ITP). The purpose of this study was to evaluate the cost-effectiveness of TPO-RAs relative to treatment without TPO-RAs (non-TPO-RAs/usual care) for ITP in children who do not respond to first-line therapy and in whom splenectomy is not recommended in Ontario, Canada, from a hospital payer perspective. PROCEDURE: A 2-year Markov model with an embedded decision tree was used. Data on medications used, dose, response rate, bleeding, and emergency treatment events were collected from the Hospital for Sick Children in Toronto. The health outcomes were described in quality-adjusted life-years (QALYs). Health-state utilities were derived from the peer-reviewed literature. Scenario analyses, deterministic, and probabilistic sensitivity analyses were conducted. Economic costs were measured in 2021 Canadian dollars ($1.00 = US$0.80) RESULTS: TPO-RAs are estimated to result in an increased cost of $27,118 and a QALY gain of 0.21 compared to non-TPO-RAs over a 2-year horizon, resulting in an incremental cost-effectiveness ratio (ICER) of $129,133. In a 5-year scenario analysis, the ICER fell to $76,403. In the probabilistic sensitivity analysis, TPO-RAs exhibit a 40.0% probability of being cost-effective at a conventional ($100,000) willingness-to-pay threshold per QALY gained. CONCLUSIONS: Further assessment of the long-term efficacy of TPO-RAs is warranted to obtain more precise long-term estimates. As the costs of TPO-RAs decline with the introduction of generic formulations, TPO-RAs may be increasingly cost-effective.
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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 | 0.014 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".