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

在美国，神经精神障碍是导致残疾的主要原因，并与 死亡率(例如自杀以及与慢性病及其风险因素的关系)。证据 提示早期发现和治疗精神疾病对于改善长期预后至关重要。 甚至可能在生物水平上改变疾病轨迹。不幸的是，相当大比例的患者 在接受适当的诊断和开始有效的治疗之前，要经历一段漫长的诊断旅程。 改善对新出现或神秘的精神病理学的监测的努力通常是复杂的、昂贵的，并具有 有限收益。因此，公共卫生迫切需要改善早期临床决策支持。 在临床环境中检测精神障碍。大规模生物库链接的可用性越来越高 EHR对生物标本的研究为精神病学创造了一个强大但相对尚未开发的机会 研究。2007年，NHGRI组织了电子病历和基因组学(Emerge)网络 它将美国各地的研究人员聚集在一起，以促进基于EHR的基因组研究和 实施基因组医学。然而，到目前为止，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。为了解决这一差距，我们创建了一个新的、大规模的 协作联盟-利用 Emerge网络，精神病学基因组联盟(PGC)，以及当地的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病例和对照表型 跨多种障碍(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和基因组的风险概况 与临床相关的健康结果有关。我们将进一步使用这些风险概况来检查 年龄、性别和种族/民族在诊断延迟方面的差异。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 完成这些目标将是在展示电子健康记录资源在以下方面的用途方面的重大进步 精准医学的精神病学方法，为临床决策支持工具提供了第一步， 可在卫生系统内实施，并为科学界创造无价资源。

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.