The cost-effectiveness of UGT1A1 genotyping before colorectal cancer treatment with irinotecan from the perspective of the German statutory health insurance.

BACKGROUND: The evidence concerning the cost-effectiveness of UGT1A1*28 genotyping is ambiguous and does not allow drawing valid conclusions for Germany. This study evaluates the cost-effectiveness of UGT1A1 genotyping in patients with metastatic colorectal cancer undergoing irinotecan-based chemotherapy compared to no testing from the perspective of the German statutory health insurance. MATERIAL AND METHODS: A decision-analytic Markov model with a life time horizon was developed. No testing was compared to two genotype-dependent therapy strategies: 1) dose reduction by 25%; and 2) administration of a prophylactic G-CSF growth factor analog for homozygous and heterozygous patients. Probability, quality of life and cost parameters used in this study were based on published literature. Deterministic and probabilistic sensitivity analyses were performed to account for parameter uncertainties. RESULTS: Strategy 1 dominated all remaining strategies. Compared to no testing, it resulted in only marginal QALY increases (0.0002) but a cost reduction of €580 per patient. Strategy 2 resulted in the same health gains but increased costs by €10 773. In the probabilistic analysis, genotyping and dose reduction was the optimal strategy in approximately 100% of simulations at a threshold of €50 000 per QALY. Deterministic sensitivity analysis shows that uncertainty for this strategy originated primarily from costs for irinotecan-based chemotherapy, from the prevalence of neutropenia among heterozygous patients, and from whether dose reduction is applied to both homozygotes and heterozygotes or only to the former. CONCLUSION: This model-based synthesis of the most recent evidence suggests that pharmacogenetic UGT1A1 testing prior to irinotecan-based chemotherapy dominates non-personalized colon cancer care in Germany. However, as structural uncertainty remains high, these results require validation in clinical practice, e.g. based on a managed-entry agreement.