Practical pharmacogenetics: the cost effectiveness of screening for thiopurine s-methyltransferase polymorphisms in patients with rheumatological conditions treated with azathioprine.
Bibliographic record
Abstract
OBJECTIVE: Thiopurine S-methyltransferase (TPMT), which catalyzes the inactivation of azathioprine (AZA), exhibits genetic polymorphism that results in dose related, serious toxicities (mainly hematological cytopenias) in 10-15% of individuals treated with AZA. Polymerase chain reaction (PCR) tests provide a sensitive, specific means of prospectively identifying these patients before AZA therapy and minimizing toxicity through dosage reduction. Our objective was to model the cost effectiveness of the 2 alternative AZA treatment strategies in rheumatologic conditions: (1) utilizing PCR to determine polymorphisms leading to TPMT deficiencies prior to AZA therapy with a reduction in dose; and (2) no testing. The analysis was conducted from a third party payer perspective over one year. METHODS: A decision analytic model was applied to map the costs and outcomes of patients under both strategies. Data applied to the model included the positive and negative predictive values of the PCR, the probabilities of adverse events due to AZA, and the costs associated with their management. Sources of data included published clinical trials, diagnostic test evaluations, surveillance trials, and economic evaluations. RESULTS: Dose related toxicities resulted in AZA discontinuation rates of 10-20%. The usual dosing strategy cost $677 Cdn per patient, whereas the genotype directed dosing strategy cost $663 Cdn per patient. In the genotype dosing strategy, the number needed to treat to avoid one adverse event over 6 months was 20. Thus, the genotype based dosing strategy dominated the usual dosing strategy. One-way sensitivity analyses revealed that the estimates were robust to ranges of +/- 30% for the costs, the properties of the PCR test, and the probability of adverse events. CONCLUSION: The introduction of PCR testing to identify TPMT polymorphisms prior to AZA treatment may represent good value in certain health care settings.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".