目的:基于中国卫生服务体系角度评价血塞通软胶囊对比安慰剂治疗缺血性中风患者的经济性.方法:基于血塞通软胶囊的Ⅲ期对照试验和已发表的文献数据构建markov模型,模拟患者终身的质量调整生命年(quality-adjusted life year,QALY)和增量成本效果比(Incremental cost-effectiveness ratio ICER),并进行单因素敏感性分析和概率敏感性分析检验模型的不确定性.结果:基础分析结果显示,与安慰剂组相比,血塞通软胶囊在获得0.32个QALY且节约医疗成本,具有绝对优势,单因素敏感性分析和概率敏感性分析验证了模型的稳健性.单因素敏感性分析显示对模型结果影响程度最大的4个因素分别是mRS3-5首次住院成本、mRS0-2首次住院成本、mRS0-2的状态效用值及贴现率;概率敏感性分析显示在我国的意愿支付值之下血塞通软胶囊具有经济性的概率为100％.结论:与安慰剂相比,血塞通软胶囊在提升QALY的同时降低了相应的成本,对治疗我国缺血性中风患者具有经济性.
Cost-effectiveness Analysis of Ischemic Stroke Treatment with Xuesaitong Soft Capsules Based on Markov Modeling
Objective:To evaluate the cost-effectiveness of Xuesaitong soft capsules versus placebo for the treatment of ischemic stroke patients based on the perspective of the Chinese health service system.Methods:A markov model was constructed based on the phase Ⅲcontrolled trial of Xuesaitong soft capsule and published literature data to simulate the quality-adjusted life year(QALY)and the incre-mental cost-effectiveness ratio(ICER)over the lifetime of the patients,and one-way sensitivity analysis and probabilistic sensitivity analy-sis were performed to test the uncertainty of the model.Results:The results of the baseline analysis showed that Xuesaitong soft gelatin cap-sules had an absolute advantage in obtaining 0.32 QALYs and saving healthcare costs compared with the placebo group,and the one-way sensitivity analysis and probabilistic sensitivity analysis verified the robustness of the model.The one-way sensitivity analysis showed that the four factors with the greatest degree of influence on the model results were mRS3-5 first hospitalization cost,mRS0-2 first hospitalization cost,state utility value of mRS0-2,and discount rate;the probabilistic sensitivity analysis showed that there was a 100％probability that the capsules would be cost-effectiveness under China's willingness-to-pay(WTP).Conclusion:Compared with placebo,Xuesaitong soft gelatin capsule enhances QALY while reducing the corresponding cost,which is economical in treating ischemic stroke patients in China.
国家科技期刊平台
NSTL
万方数据
