医学
瞬态弹性成像
人口
质量调整寿命年
成本效益
增量成本效益比
内科学
疾病
纤维化
成本效益分析
肝病
重症监护医学
肝纤维化
环境卫生
风险分析(工程)
作者
Huiyul Park,Eileen L. Yoon,Mimi Kim,Sun‐Hong Kwon,Donghee Kim,Ramsey Cheung,Hye‐Lin Kim,Dae Won Jun
摘要
Abstract Background & Aims The cost‐effectiveness to screen hepatic fibrosis in at‐risk population as recommended by several professional societies has been limited. This study aimed to investigate the cost‐effectiveness of this screening strategy in the expanded at‐risk population recently proposed by several societies. Methods A combined model of the decision tree and Markov models was developed to compare expected costs, quality‐adjusted life‐years (QALYs) and incremental cost‐effectiveness ratio (ICER) between screening and no screening groups. The model included liver disease‐related health states and cardiovascular disease (CVD) states as a base‐case analysis. Screening strategy consisted of fibrosis‐4 index (FIB‐4) followed by vibration‐controlled transient elastography (VCTE) and intensive lifestyle intervention (ILI) as a treatment for diagnosed patients. Results Cost‐effectiveness analysis showed that screening the at‐risk population entailed $298 incremental costs and an additional 0.0199 QALY per patient compared to no screening (ICER $14 949/QALY). Screening was cost‐effective based on the implicit ICER threshold of $25 000/QALY in Korea. When the effects of ILI on CVD and extrahepatic malignancy were incorporated into the cost‐effectiveness model, the ICER decreased by 0.85 times from the base‐case analysis (ICER $12 749/QALY). In contrast, when only the effects of liver disease were considered in the model, excluding cardiovascular disease effects, ICER increased from the baseline case analysis to $16 305. Even when replacing with medical costs in Japan and U.S., it remained cost‐effective with the estimate below the countries' ICER threshold. Conclusions Our study provides compelling evidence supporting the cost‐effectiveness of FIB‐4‐based screening the at‐risk population for advanced hepatic fibrosis.
科研通智能强力驱动
Strongly Powered by AbleSci AI