医学
伊瓦卡夫托
囊性纤维化
观察研究
恶化
人口
随机对照试验
病人登记处
儿科
重症监护医学
内科学
环境卫生
囊性纤维化跨膜传导调节器
作者
Ruth H. Keogh,Rebecca Cosgriff,Eleni‐Rosalina Andrinopoulou,K. Brownlee,S.B. Carr,Karla Díaz-Ordaz,Emily Granger,Nicholas P. Jewell,Alex Lewin,Clémence Leyrat,Daniela K Schlüter,Maarten van Smeden,Rhonda D. Szczesniak,Gary Connett
出处
期刊:Thorax
[BMJ]
日期:2021-09-23
卷期号:77 (9): 873-881
被引量:25
标识
DOI:10.1136/thoraxjnl-2020-216265
摘要
Background Cystic fibrosis (CF) is a life-threatening genetic disease, affecting around 10 500 people in the UK. Precision medicines have been developed to treat specific CF-gene mutations. The newest, elexacaftor/tezacaftor/ivacaftor (ELEX/TEZ/IVA), has been found to be highly effective in randomised controlled trials (RCTs) and became available to a large proportion of UK CF patients in 2020. Understanding the potential health economic impacts of ELEX/TEZ/IVA is vital to planning service provision. Methods We combined observational UK CF Registry data with RCT results to project the impact of ELEX/TEZ/IVA on total days of intravenous (IV) antibiotic treatment at a population level. Registry data from 2015 to 2017 were used to develop prediction models for IV days over a 1-year period using several predictors, and to estimate 1-year population total IV days based on standards of care pre-ELEX/TEZ/IVA. We considered two approaches to imposing the impact of ELEX/TEZ/IVA on projected outcomes using effect estimates from RCTs: approach 1 based on effect estimates on FEV 1 % and approach 2 based on effect estimates on exacerbation rate. Results ELEX/TEZ/IVA is expected to result in significant reductions in population-level requirements for IV antibiotics of 16.1% (~17 800 days) using approach 1 and 43.6% (~39 500 days) using approach 2. The two approaches require different assumptions. Increased understanding of the mechanisms through which ELEX/TEZ/IVA acts on these outcomes would enable further refinements to our projections. Conclusions This work contributes to increased understanding of the changing healthcare needs of people with CF and illustrates how Registry data can be used in combination with RCT evidence to estimate population-level treatment impacts.
科研通智能强力驱动
Strongly Powered by AbleSci AI