Serplulimab plus chemotherapy as first-line treatment for extensive-stage small-cell lung cancer: A cost-effectiveness analysis

Serplulimab联合化疗作为广泛期小细胞肺癌一线治疗方案:成本效益分析

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Abstract

INTRODUCTION: The ASTRUM-005 trial (NCT04063163) revealed that combination serplulimab plus chemotherapy (etoposide and carboplatin [EC]) treatment was associated with survival advantages relative to chemotherapy alone in patients diagnosed with extensive-stage small-cell lung cancer (ES-SCLC). As these immuno-chemotherapeutic regimens are extremely expensive, however, it is critical that the relative cost-effectiveness of combination serplulimab and chemotherapy treatment as a first-line treatment for ES-SCLC patients be examined in detail. METHODS: The cost-effectiveness of combined serplulimab plus chemotherapeutic treatment was examined using a comprehensive Markov model with a 10-year boundary, enabling the calculation of overall cost, life years (LYs), quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICER). Model instability was interrogated through one-way and probabilistic sensitivity analyses. RESULTS: Serplulimab plus chemotherapy or chemotherapy alone respectively yielded 1.217 QALYs (2.243 LYs) and 0.885 QALYs (1.661 LYs) with corresponding total costs of $11,202 and $7,194, with an ICER of $12,077 per QALY ($6,883 per LY). This model was most strongly influenced by the utility of progression-free survival. Probabilistic sensitivity analysis showed that serplulimab plus chemotherapy had a 91.6% probability of being cost-effective at a willingness-to-pay (WTP) of $37,653 per QALY (3 × capita gross domestic product of China in 2021). In subgroup analyses, this combination treatment regimen was found to be most cost-effective in patients who were former smokers, had an ECOG performance status of 0, and were diagnosed with brain metastases. CONCLUSION: From a payer perspective in China, combination serplulimab plus chemotherapy treatment represents a cost-effective first-line intervention for ES-SCLC patients.

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