Cost-effectiveness Analysis for Genotyping before Allopurinol Treatment to Prevent Severe Cutaneous Adverse Drug Reactions
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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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".