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网络荟萃分析在指导克罗恩病患者生物制剂定位中的作用:前景与风险。

Network meta-analysis to inform positioning of biologics in patients with Crohn's disease: Promise and perils.

机构信息

Division of Gastroenterology, University of California San Diego, La Jolla, CA, USA; Division of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.

出版信息

Best Pract Res Clin Gastroenterol. 2019 Feb-Apr;38-39:101614. doi: 10.1016/j.bpg.2019.05.001. Epub 2019 Jun 3.

Abstract

With availability of several different classes of biologic agents with variable efficacy and safety profiles for moderate-severe Crohn's disease (CD), positioning of different agents in treatment course is an important question for clinicians. Though in an ideal world, positioning would be personalized and driven by likelihood of response to different agents based on biomarkers in individual patients, that is still far from reality, and decisions are empiric. In the absence of head-to-head trials of different medications, decisions on treatment choice and positioning are primarily based on clinician experience, opinion-based treatment algorithms, patient preference and insurance reimbursement. Understandably, in the absence of guidance, there is considerable practice variability on optimal choice of first- and second-line biologics in the treatment of patients with CD. In the absence of direct evidence from head-to-head trials, network meta-analysis can help assess comparative efficacy of several interventions and synthesize evidence across a network of randomized controlled trials. In this review, we discuss what network meta-analyses, what do they tell us about positioning different agents, and strengths and limitations of such an approach.

摘要

对于中重度克罗恩病(CD),有多种不同类别的生物制剂,其疗效和安全性特征各异,因此在治疗过程中如何定位不同的药物是临床医生关注的重要问题。虽然在理想情况下,根据个体患者的生物标志物,根据不同药物的反应可能性来进行个性化定位,但这仍远远不现实,目前的决策主要还是基于经验。在缺乏不同药物头对头试验的情况下,治疗选择和定位的决策主要基于临床医生的经验、基于观点的治疗算法、患者偏好和保险报销。可以理解的是,在缺乏指导的情况下,对于 CD 患者的一线和二线生物制剂的最佳选择存在相当大的实践变异性。在缺乏头对头试验的直接证据的情况下,网络荟萃分析可以帮助评估几种干预措施的相对疗效,并综合一系列随机对照试验中的证据。在这篇综述中,我们讨论了网络荟萃分析是什么,它们告诉我们关于定位不同药物的信息,以及这种方法的优缺点。

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