Pharmacological Acromegaly Treatment: Cost-Utility and Value of Information Analysis

Objectives: To conduct a cost-utility analysis comparing drug strategies involving octreotide, lanreotide, pasireotide, and pegvisomant for the treatment of patients with acromegaly who have failed surgery, from a Brazilian public payer perspective. Methods: A probabilistic cohort Markov model was developed. One-year cycles were employed. The patients started at 45 years of age and were followed lifelong. Costs, efficacy, and quality of life parameters were retrieved from the literature. A discount rate (5%) was applied to both costs and efficacy. The results were reported as costs per quality-adjusted life year (QALY), and incremental cost-effectiveness ratios (ICERs) were calculated when applicable. Scenario analyses considered alternative dosages, discount rate, tax exemption, and continued use of treatment despite lack of response. Value of information (VOI) analysis was conducted to explore uncertainty and to estimate the costs to be spent in future research. Results: Only lanreotide showed an ICER reasonable for having its use considered in clinical practice (R$ 112,138/US$ 28,389 per QALY compared to no treatment). Scenario analyses corroborated the base-case result. VOI analysis showed that much uncertainty surrounds the parameters, and future clinical research should cost less than R$ 43,230,000/US$ 10,944,304 per year. VOI also showed that almost all uncertainty that precludes an optimal strategy choice involves quality of life. Conclusions: With current information, the only strategy that can be considered cost-effective in Brazil is lanreotide treatment. No second-line treatment is recommended. Significant uncertainty of parameters impairs optimal decision-making, and this conclusion can be generalized to other countries. Future research should focus on acquiring utility data.

Automated summary

The study compared cost-utility analysis of drug strategies for acromegaly in Brazil, with lanreotide showing the most cost-effective results. Future research should focus on acquiring utility data to reduce uncertainty in decision-making.