Clinical outcomes and cost-utility analysis of GKRS plus TKIs versus TKIs in patients with EGFR-mutant lung adenocarcinoma and brain metastases: a Markov decision model.

Abstract

Objective: This study focuses on epidermal growth factor receptor-mutated lung adenocarcinoma, known for frequent brain metastasis. It aimed to compare the clinical outcomes and cost-effectiveness of combining Gamma Knife radiosurgery (GKRS) with tyrosine kinase inhibitors (TKIs) (GKRS+TKI group) versus TKIs alone (TKI group) for the treatment of patients with newly diagnosed brain metastasis in this condition.

Methods: Study characteristics of the two groups were matched using inverse probability of treatment weighting (IPTW). In the incremental cost-utility ratio (ICUR) model, a healthcare provider perspective, a 1-month cycle length, a 5-year time horizon, and a discount rate of 2% per year for both effectiveness and costs were adopted. Probabilistic and one-way sensitivity analyses were also conducted to demonstrate the robustness of the findings. Statistical analysis was performed using IBM SPSS version 23.0, and cost-effectiveness analysis was conducted using TreeAge Pro software.

Results: After applying IPTW, the GKRS+TKI group included 205 patients, and the TKI group consisted of 102 patients, with no statistically significant differences in whole confounders. The GKRS+TKI group demonstrated significantly prolonged median progression-free survival (37.5 months vs 10.6 months, p < 0.001) and median overall survival (55.1 months vs 30.8 months, p < 0.001) compared with the TKI group. The GKRS plus TKI strategy achieved an ICUR of $30,532.25 per quality-adjusted life year relative to the TKIs at the willingness-to-pay threshold of US$33,059 (Taiwan's per capita gross domestic product).

Conclusions: The use of GKRS plus TKIs not only reduces disease recurrence and improves prognosis but also demonstrates a higher level of cost-effectiveness. These findings offer valuable guidelines for clinicians and inform healthcare authorities in optimizing resource allocation for improved medical care.