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

PsycheMERGE：利用电子健康记录和基因组学进行心理健康研究

基本信息

批准号：
10066366
负责人：
Lea K Davis
金额：
$ 75.04万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-02-15 至 2022-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10066366
关键词：
Address Adult Age Algorithms Attention deficit hyperactivity disorder Biological Bipolar Disorder Childhood Chronic Disease Clinical Clinical Research Communities Complex Computerized Medical Record Data Diagnosis Diagnostic Disease Early Diagnosis Early treatment Eating Disorders Electronic Health Record Electronic Medical Records and Genomics Network Ethnic Origin Genetic Genetic Research Genomic medicine Genomics Gilles de la Tourette syndrome Goals Health Health system Heritability Individual Infrastructure Insurance Coverage Link Machine Learning Major Depressive Disorder Mental Health Mental disorders Methods Modeling Mood Disorders National Human Genome Research Institute Obsessive-Compulsive Disorder Outcome Patients Performance Phenotype Probability Process Psychiatry Psychopathology Public Health Race Research Research Personnel Resources Risk Risk Factors Schizophrenia Site Suicide Surveillance Modeling Testing Training Validation Variant autism spectrum disorder base biobank case control clinical decision support clinical risk clinically relevant comorbidity cost data resource delay sex disability disorder risk effective therapy genome wide association study genome-wide genomic predictors health care service utilization high risk improved indexing mortality neuropsychiatric disorder phenotyping algorithm polygenic risk score precision medicine prediction algorithm predictive modeling psychiatric genomics random forest risk prediction sex support tools support vector machine

项目摘要

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.

在美国，神经精神疾病是导致残疾的主要原因，并与增加的死亡率有关