Comparing Microvascular Decompression with Gamma Knife Radiosurgery for Trigeminal Neuralgia. A Cost-Effectiveness Analysis

World Neurosurg. (2019) 125:207-216

Both microvascular decompression (MVD) and Gamma Knife radiosurgery (GKRS) are time-tested treatment modalities for trigeminal neuralgia (TN). 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 TN.

METHODS: We developed a Markov cost-effectiveness model for the U.S. health care system to account for all costs related to MVD and GKRS as treatment modalities for TN, from the health care system perspective, over a patient lifetime horizon. A base case was estimated using data from previous studies, 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 the literature. We used age-specific, sex-specific, and race-specific mortality 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 1-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 lifeyear (QALY) gained, defined cost-effectiveness.

RESULTS: The base case had an ICER of $12,154 per QALY for MVD compared with GKRS. Probabilistic sensitivity (Monte Carlo) analysis showed that MVD was cost-effective in 70% of model iterations. GKRS was favored when the willingness to pay threshold was <$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 TN, 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

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