循证医学:寻找证据的新领域

E. V. Verbitskaya, D. Yu. Belousov, A. S. Kolbin
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引用次数: 0

摘要

这篇综述的重点是讨论糖尿病的新趋势,重新思考循证医学,重点是展示新的治疗药物和医疗技术的有效性和安全性,从基因编辑到人工智能算法。与此同时,药物的临床试验也没有停滞不前,有积极的发展,新的途径、方法和设计正在被开发出来。任何药物的开发、注册和上市过程都需要相当长的一段时间,需要高昂的财务成本和人力资源。循证医学的金标准最初被认为是随机对照试验,但同时也存在客观的缺点:样本量有限,无法控制所有因素;随访时间不足,对参与者产生负面影响,无法确定因果关系;有限的测量方法,有限的预算和其他。本文综述了临床研究方法学的发展趋势。新设计的积极实施,如适应性临床试验、“主”协议(包括伞形研究、篮子研究、平台研究、主观察试验)和单患者试验(N-of-1试验)的增加使用,使研究变得更有效、更相关、更适用于现实生活中的医疗保健实践环境和以患者为中心。在回顾中,我们还研究了在临床试验和虚拟临床试验中扩大使用生物标志物和数字终点的利弊,它们使用数学模型来研究患者异质性及其对各种治疗问题的影响。专业知识和监管审批的过程继续减缓药物进入制药市场的速度。审查考虑了监管机构(FDA, EMEA和俄罗斯卫生部)对药物注册程序方法的变化。COVID-19大流行和制裁导致需要扩大经授权加速注册的药物清单。然而,加速注册提出了许多关于证据水平充分性和安全性的问题。正如我们所看到的,要实现下一代循证医学,需要对所有现有数据进行深入的综合和整合。未来二十年的主要挑战将是通过提取、整理和生成疾病自然过程、基因组学和所有其他组学分析、所有已发表的临床试验、RWD等大型数据集,开发多维证据生成的潜力,以提供下一代证据。
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Evidence-based medicine: new in the search for evidence
The review is focused on discussing new trends in DM, rethinking the evidence base medicine with an emphasis on demonstrating the efficacy and safety of new therapeutic agents and medical technologies, from gene editing to AI algorithms. At the same time, clinical trials of drugs are also not standing still, there is active development, new approaches, methods and designs are being developed. The process of drug development, registration and market launch of any drug takes a significant period of time, requires high financial costs and human resources. The gold standard of evidence-based medicine was initially considered to be RCTs, but at the same time, they have objective disadvantages: limited sample, inability to control all factors; insufficient follow-up time,negative impact on participants, inability to determine cause-and-effect relationships; limited measurement methods, limited budget and others. This review discusses the trends in the methodology of clinical research. The active implementation of new designs such as Adaptive Clinical Trials, "Master" protocols including Umbrella Study, Basket Study, Platform Studies, Master Observational Trials and the increased use of Single Patient Trials (N-of-1 trials) are allowing studies to become more efficient, relevant and applicable to real-life health care practice settings and patient-centered. In the review, we also look at both the pros and cons of the expanded use of biomarkers and Digital Endpoints in clinical trials and virtual clinical trials, which use mathematical models to study patient heterogeneity and its impact on various therapeutic questions. The process of expertise and regulatory approval continues to slow down the entry of drugs into the pharmaceutical market. The review considers changes in the approaches of regulators (FDA, EMEA and Russian Ministry of Health) to the drug registration procedure. The COVID-19 pandemic and sanctions have led to the need to expand the list of drugs with authorized accelerated registration. However, accelerated registration raises many questions regarding the sufficiency of the level of evidence and safety. As we can see a deep synthesis and integration of all available data is needed to achieve the next generation of evidence-based medicine. The major challenge in the next two decades will be to exploit the potential of multidimensional evidence generation by extracting, collating and generating large data sets of natural course of disease, genomics and all other omics analyses, all published clinical trials, RWD to provide next generation evidence.
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