LAUSR

BACKGROUND AND OBJECTIVE Both Microvascular decompression (MVD) and Gamma knife radiosurgery (GKRS) are time tested treatment modalities for Trigeminal Neuralgia. There is little evidence in the literature studying these modalities head to head in a cost-effectiveness comparison. OBJECTIVE To evaluate the cost-effectiveness of MVD compared with GKRS for treating patients with Trigeminal Neuralgia. METHODS We developed a Markov cost-effectiveness model for the US health care system to account for all costs related to MVD and GKRS as treatment modalities for Trigeminal Neuralgia, from the health care system perspective, over a patient lifetime horizon. A base case was estimated using data from prior publications, from our own GKRS experience, and from a current data analysis of patients undergoing MVD. We derived model inputs, including health care costs, survival, and utility estimates, from literature. We utilized age-, sex- and race-specific mortality rates from national registries. Costs studied included those for MVD, for GKRS, for treating complications from either procedure, and for medications throughout patient lifetimes. We performed multiple one-way, 2-way and probabilistic sensitivity analyses to confirm the robustness of model assumptions and results. The incremental cost-effectiveness ratio (ICER), with a threshold of $50,000 per quality-adjusted life-year (QALY) gained, defined cost-effectiveness. RESULTS The base case had an ICER of $12,154 per quality-adjusted life-year for MVD compared to GKRS. Probabilistic sensitivity (Monte Carlo) analysis demonstrated that MVD was cost-effective in 70% of model iterations. GKRS was favored when the willingness to pay threshold was less than $12,000 per QALY gained. CONCLUSIONS In patients medically eligible for either procedure, we found MVD to be the most cost-effective modality to treat Trigeminal Neuralgia, primarily because of its reported greater durability. MVD remained the most cost-effective strategy across a broad range of model input values in sensitivity analyses.