Stanford Prevention Research Center, Meta-Research Innovation Center at Stanford (METRICS), and Departments of Medicine, Health Research and Policy, Biomedical Data Science, and Statistics, Stanford University, Stanford, CA, USA.
Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Hum Mol Genet. 2018 May 1;27(R1):R2-R7. doi: 10.1093/hmg/ddy065.
Genomic and other related big data (Big Genomic Data, BGD for short) are ushering a new era of precision medicine. This overview discusses whether principles of evidence-based medicine hold true for BGD and how they should be operationalized in the current era. Major evidence-based medicine principles include the systematic identification, description and analysis of the validity and utility of BGD, the combination of individual clinical expertise with individual patient needs and preferences, and the focus on obtaining experimental evidence, whenever possible. BGD emphasize information of single patients with an overemphasis on N-of-1 trials to personalize treatment. However, large-scale comparative population data remain indispensable for meaningful translation of BGD personalized information. The impact of BGD on population health depends on its ability to affect large segments of the population. While several frameworks have been proposed to facilitate and standardize decision making for use of genomic tests, there are new caveats that arise from BGD that extend beyond the limitations that were applicable for more simple genetic tests. Non-evidence-based use of BGD may be harmful and result in major waste of healthcare resources. Randomized controlled trials will continue to be the strongest arbitrator for the clinical utility of genomic technologies, including BGD. Research on BGD needs to focus not only on finding robust predictive associations (clinical validity) but also more importantly on evaluating the balance of health benefits and potential harms (clinical utility), as well as implementation challenges. Appropriate features of such useful research on BGD are discussed.
基因组和其他相关大数据（简称大基因组数据，Big Genomic Data，BGD）正在开启精准医学的新时代。本文综述了循证医学的原则是否适用于 BGD，以及在当前时代应如何实施这些原则。循证医学的主要原则包括系统地识别、描述和分析 BGD 的有效性和实用性，将个体临床专业知识与个体患者的需求和偏好相结合，并侧重于尽可能获得实验证据。BGD 强调单个患者的信息，过分强调 N-of-1 试验以实现个体化治疗。然而，大规模的比较人群数据仍然是将 BGD 个体化信息转化为实际应用不可或缺的。BGD 对人群健康的影响取决于其影响人口中较大部分的能力。虽然已经提出了几个框架来促进和标准化基因组测试的使用决策，但 BGD 带来了新的警告，这些警告超出了更简单的遗传测试适用的限制。对 BGD 的非循证使用可能是有害的，并导致医疗保健资源的大量浪费。随机对照试验将继续是评估基因组技术（包括 BGD）临床实用性的最强仲裁者。BGD 的研究不仅需要关注发现稳健的预测关联（临床有效性），还需要更重要的是评估健康益处和潜在危害的平衡（临床实用性），以及实施挑战。本文讨论了对 BGD 进行此类有用研究的适当特征。
