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治疗特发性肺纤维化的药物疗法:系统评价、贝叶斯网络荟萃分析及成本效益分析。

Drug therapies for treatment of idiopathic pulmonary fibrosis: a systematic review, Bayesian network meta-analysis, and cost-effectiveness analysis.

作者信息

Zhao Chunyang, Yin Yan, Zhu Chengrui, Zhu Min, Ji Tianlong, Li Zhonghao, Cai Jiayi

机构信息

School of Pharmacy, China Medical University, Shenyang, China.

Department of Pharmacy, The First Hospital of China Medical University, Shenyang, China.

出版信息

EClinicalMedicine. 2023 Jun 29;61:102071. doi: 10.1016/j.eclinm.2023.102071. eCollection 2023 Jul.

Abstract

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with poor prognosis and a high economic burden for individuals and healthcare resources. Studies of the costs associated with the efficiency of IPF medications are scarce. We aimed to conduct a network meta-analysis (NMA) and cost-effectiveness analysis to identify the optimum pharmacological strategy among all currently available IPF regimens.

METHODS

We first performed a systematic review and network meta-analysis. We searched eight databases for eligible randomised controlled trials (RCTs) published, in any language, between January 1, 1992 and July 31, 2022, that investigated the efficacy or tolerability (or both) of drug therapies for the treatment of IPF. The search was updated on February 1, 2023. Eligible RCTs were enrolled, with no restriction on dose, duration, or length of follow-up, if they included at least one of: all-cause mortality, acute exacerbation rate, disease progression rate, serious adverse events, and any adverse events under investigation. A subsequent Bayesian NMA within random-effects models was performed, followed by a cost-effectiveness analysis using the data obtained from our NMA, by developing a Markov model from the US payer's perspective. Assumptions were checked by deterministic and probabilistic sensitivity approaches to identify sensitive factors. We prospectively registered the protocol (CRD42022340590) in PROSPERO.

FINDINGS

51 publications comprising 12,551 participants with IPF were analysed for the NMA, and the findings indicated that pirfenidone and -acetylcysteine (NAC) + pirfenidone were the most efficacious and tolerable. The pharmacoeconomic analysis showed that NAC + pirfenidone was associated with the highest potentiality of being cost-effective at willingness-to-pay (WTP) thresholds of US$150,000 and $200,000, on the basis of quality-adjusted life years (QALYs), disability-adjusted life years (DALYs) and mortality, with the probability ranging from 53% to 92%. NAC was the minimum cost agent. Compared with placebo, NAC + pirfenidone improved effectiveness by increasing QALYs by 7.02, and reducing DALYs by 7.10 and deaths by 8.40, whilst raising overall costs by $516,894.

INTERPRETATION

This NMA and cost-effectiveness analysis suggests that NAC + pirfenidone is the most cost-effective option for treatment of IPF at WTP thresholds of $150,000 and $200,000. However, given that clinical practice guidelines have not addressed the application of this therapy, large well-designed and multicentre trials are warranted to provide a better picture of IPF management.

FUNDING

None.

摘要

背景

特发性肺纤维化(IPF)是一种进行性间质性肺病,预后较差,给个人和医疗资源带来高昂的经济负担。关于IPF药物疗效相关成本的研究较少。我们旨在进行一项网络荟萃分析(NMA)和成本效益分析,以确定所有现有IPF治疗方案中的最佳药物治疗策略。

方法

我们首先进行了系统评价和网络荟萃分析。我们检索了八个数据库,以查找1992年1月1日至2022年7月31日期间以任何语言发表的符合条件的随机对照试验(RCT),这些试验研究了药物治疗IPF的疗效或耐受性(或两者)。检索于2023年2月1日更新。纳入符合条件的RCT,如果它们至少包括以下一项:全因死亡率、急性加重率、疾病进展率、严重不良事件以及正在研究的任何不良事件,则对剂量、持续时间或随访长度没有限制。随后在随机效应模型中进行贝叶斯NMA,然后使用从我们的NMA获得的数据进行成本效益分析,从美国支付方的角度建立马尔可夫模型。通过确定性和概率敏感性方法检查假设,以确定敏感因素。我们在PROSPERO中前瞻性地注册了该方案(CRD42022340590)。

结果

对51篇包含12551名IPF患者的出版物进行了NMA分析,结果表明吡非尼酮以及N - 乙酰半胱氨酸(NAC)+吡非尼酮是最有效且耐受性最好的。药物经济学分析表明,基于质量调整生命年(QALY)、伤残调整生命年(DALY)和死亡率,在支付意愿(WTP)阈值为15万美元和20万美元时,NAC +吡非尼酮具有最高的成本效益潜力,概率范围为53%至92%。NAC是成本最低的药物。与安慰剂相比,NAC +吡非尼酮通过增加7.02个QALY、减少7.10个DALY和8.40例死亡来提高疗效,同时使总成本增加516,894美元。

解读

这项NMA和成本效益分析表明,在支付意愿阈值为15万美元和20万美元时,NAC +吡非尼酮是治疗IPF最具成本效益的选择。然而,鉴于临床实践指南尚未涉及该疗法的应用,有必要进行大型精心设计的多中心试验,以更好地了解IPF的管理。

资金来源

无。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd37/10331814/308a9f7c0fc3/gr1.jpg

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