PsycheMERGE: Leveraging electronic health records and genomics for mental health research

神经精神障碍是美国残疾的主要原因， 死亡率（例如，通过自杀和与慢性疾病及其风险因素的关联）。证据 表明精神疾病的早期发现和治疗对改善长期结果至关重要 甚至可能在生物学水平上改变疾病轨迹。不幸的是，相当一部分病人 在接受适当的诊断和开始有效的治疗之前，要经历一个漫长的诊断过程。 努力改善新兴或隐匿性精神病理学的监测往往是复杂的，昂贵的， 产量有限。因此，公共卫生迫切需要改善对早期患者的临床决策支持。 在临床环境中检测精神疾病。越来越多的大型生物库连接 生物标本的EHR为精神病学创造了一个强大的，但相对尚未开发的机会， research. 2007年，NHGRI组织了电子医疗记录和基因组学（eMERGE）网络 该组织汇集了美国各地的研究人员，以促进基于EHR的基因组研究， 实施基因组医学。然而，迄今为止，基于EHR的风险预测和基因组学还没有 被广泛用于精神病学研究。为了弥补这一差距，我们建立了一个新的，大规模的 协作联盟-PsycheMERGE-利用资源和现有的基础设施， eMERGE网络、精神病基因组学联盟（PGC）以及当地EHR和生物库资源。在这 建议，我们的目标是：（1）表型和基因组验证和协调案件和控制表型 （2）建立临床上有用的情绪障碍风险监测模型， 利用跨机构的全基因组数据，以及（3）检查基于EHR和基因组的风险状况是否 与临床相关的健康结果有关。我们将进一步利用这些风险特征， 按年龄、性别和种族/民族划分的诊断延迟差异。由此产生的诊断和风险预测 算法将通过eMERGE网络提供给科学界。成功 这些目标的完成将是证明EHR资源在以下方面的效用的一个重大进展： 精确医学方法精神病学，提供了临床决策支持工具的第一步， 可以在卫生系统内实施，并为科学界创造宝贵的资源。

Neuropsychiatric disorders are the leading causes of disability in the US and are associated with increased mortality (e.g. through suicide and associations with chronic diseases and their risk factors). Evidence suggests that early detection and treatment of psychiatric illness is essential to improving long-term outcomes and may even modify illness trajectories at a biological level. Unfortunately, a substantial proportion of patients undergo a long diagnostic odyssey before receiving an appropriate diagnosis and initiating effective treatment. Efforts to improve surveillance for emerging or occult psychopathology are often complex, costly, and have limited yield. Thus, there is an urgent public health need to improve clinical decision support for the early detection of psychiatric disorders in clinical settings. The growing availability of large-scale biobanks linking EHRs to biospecimens has created a powerful, but still relatively untapped, opportunity for psychiatric research. In 2007, the NHGRI organized the Electronic Medical Records and Genomics (eMERGE) network which has brought together investigators around the U.S. to facilitate EHR-based genomic research and the implementation of genomic medicine. To date, however, EHR-based risk prediction and genomics have not been widely leveraged for psychiatric research. To address this gap, we have created a new, large-scale collaborative consortium—PsycheMERGE—which leverages the resources and existing infrastructure of the eMERGE network, the Psychiatric Genomics Consortium (PGC), and local EHR and biobank resources. In this proposal, we aim to: (1) phenotypically and genomically validate and harmonize case and control phenotypes across multiple disorders (2) build clinically-useful risk surveillance models for mood disorders that also leverage cross-institutional genomewide data, and (3) examine whether EHR- and genomic-based risk profiles are associated with clinically-relevant health outcomes. We will further use these risk profiles to examine disparities in diagnostic delay by age, sex and race/ethnicity. The resulting diagnostic and risk prediction algorithms will be made available to the scientific community through the eMERGE network. Successful completion of these aims would represent a major advance in demonstrating the utility of EHR resources for precision medicine approaches to psychiatry, provide the first step toward clinical decision support tools that can be implemented within health systems, and create an invaluable resource for the scientific community.