Cost-Effectiveness of Pre-emptive DPYD Genotyping Compared to Standard of Care Among Patients Receiving Fluoropyrimidine-Based Anti-cancer Treatment in Australia
Bibliographic record
Abstract
BACKGROUND: Despite international evidence demonstrating pre-emptive pharmacogenetics (PGx) screening is cost effective or cost saving in preventing serious or fatal toxicities, it is not routinely adopted in Australia. This study evaluated the cost effectiveness of PGx screening versus standard of care (SOC) among patients with cancer undergoing fluoropyrimidine-based treatment (FP) in Australia. METHODS: From the Australian healthcare perspective, we developed a cohort-based state transition model in TreeAge Pro 2024. The model used PACIFIC-PGx trial data for the PGx arm and literature-based inputs for the SOC arm. Patients transitioned between four health states (full-dose, reduced-dose, treatment termination or death) over two treatment cycles each corresponding to a standard 3-week period. Outcomes included the incremental cost-effectiveness ratio (ICER) per quality-adjusted life years (QALYs) gained, per adverse event averted, and per hospitalisation avoided. Deterministic and probabilistic sensitivity analyses (DSA, PSA) evaluated the effects of varying assumptions and the uncertainty associated with input parameters. RESULTS: PGx screening yielded incremental QALYs of 0.05 at an additional cost of $274.25 AUD (Australian dollars), resulting in an ICER of $6014.5 AUD per QALY gained compared to SOC. DSA showed the model's outcomes remained robust, with the ICER staying below the specified threshold of $50,000 AUD under a ± 20% variation in input parameters. PSA suggested PGx screening was favourable in 97.6% of iterations. CONCLUSION: This Australian single-arm study demonstrated that pre-emptive PGx screening prevents severe, fatal FP-related toxicities and hospitalisations and is likely to be cost effective. Our findings suggest the value of PGx screening and warrant implementation and reimbursement within Australian healthcare settings.
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How this classification was reachedexpand
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".