Cost-effectiveness Analysis for Genotyping before Allopurinol Treatment to Prevent Severe Cutaneous Adverse Drug Reactions
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Patients with an HLA-B*58:01 allele have an increased risk of developing severe cutaneous adverse drug reactions (SCAR) when treated with allopurinol. Although one-off pharmacogenetic testing may prevent life-threatening adverse drug reactions, testing prior to allopurinol initiation incurs additional costs. The study objective was to evaluate the cost-effectiveness of HLA-B*58:01 screening compared with using other available urate-lowering agents (ULA). METHODS: A decision-analytical model was used to compare direct medical costs and effectiveness [including lifetime saved, quality-adjusted life-yrs (QALY) gained] in treating new patients with the following options: (1) genetic screening followed by allopurinol prescribing for noncarriers of HLA-B*58:01, (2) prescribing benzbromarone without screening, (3) prescribing febuxostat without screening, and (4) prescribing allopurinol without screening. A 1-year time frame and third-party payer perspective were modeled for both the entire cohort (base-case) and for the subgroup of patients with chronic kidney disease (CKD). RESULTS: The incremental cost-effectiveness ratio of genetic screening prior to ULA therapy was estimated as New Taiwan (NT) $234,610 (US$7508) per QALY gained in the base-case cohort. For patients with CKD, it was estimated as NT$230,925 (US$7390) per QALY. The study results were sensitive to the probability of benzbromarone/febuxostat-related hypersensitivity, and a negative predicted value of genotyping. CONCLUSION: HLA-B*58:01 screening gave good value for money in preventing allopurinol-induced SCAR in patients indicated for ULA therapy. In addition to the costs of genotyping, it is important to monitor ULA safety closely in adopting HLA-B*58:01 screening in practice.
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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 | 0.001 | 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 | 0.001 | 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 it