Cost-Effectiveness of Testing for NS5A Resistance to Optimize Treatment of Elbasvir/Grazoprevir for Chronic Hepatitis C in China

FRONTIERS IN PHARMACOLOGY(2021)

引用 0|浏览13
暂无评分
摘要
Objectives: Baseline presence of nonstructural protein 5A (NS5A) resistance-associated variants can attenuate the efficacy of new direct-acting antivirals. A potential method to attain the higher efficacy would be to screen for NS5A polymorphisms prior to the initiation of therapy and to adjust the treatment length based on the test results. However, baseline testing adds additional costs and it is unclear whether this would represent a high value strategy for chronic hepatitis C in China. Methods: A hybrid model compared 1) standard 12-weeks treatment (no testing), 2) shortened 8-weeks treatment (no testing), and 3) baseline testing with 12-/8-weeks treatment for those with/without NS5A polymorphisms from a lifetime Chinese health care payer perspective. All model inputs were retrieved from clinical trials and publically available literature. And sensitivity analyses were also conducted to assess the impact of uncertainty. Results: Baseline testing was associated with overall increase in total health care cost of USD 13.50 and in QALYs of 0.002 compared with standard 12-weeks treatment (no testing), yielded in an ICER of USD 6750/QALY gained. Scenario analyses suggested that shortened 8-weeks treatment (no testing) was found to be lower costs and great QALYs compared with other two strategies when the sustained virologic response (SVR) rate increased to 95%. Sensitivity analyses indicated that the results were robust. Conclusions: Our results suggest prior assessment of NS5A sensitivity followed by optimizing treatment duration was an economic strategy. In addition, shortened 8-weeks treatment (no testing) was shown to be dominant with the SVR rate increased to 95%.
更多
查看译文
关键词
cost-effectiveness, baseline testing, resistance-associated polymorphisms, elbasvir, grazoprevir, hepatitis C virus
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要