Integrating Health Records, Genomic, and Social Data to Stratify Adolescent Depression Risk

整合健康记录、基因组和社会数据对青少年抑郁症风险进行分层

• 批准号: 10284131
• 负责人: Karmel Choi
• 金额: $ 19.77万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10284131
• 关键词: Adolescence; Adolescent; Adult; Age; Algorithms; Area; Award; Big Data; Bipolar Disorder; Body mass index; Calibration; Caring; Censuses; Clinical; Clinical Data; Code; Cognitive; Data; Data Science; Data Set; Detection; Development; Diagnostic; Early Intervention; Electronic Health Record; Environment; Epidemiology; Etiology; Event; Foundations; Genetic; Genetic Predisposition to Disease; Genome; Genomics; Genotype; Goals; Gold; Grant; Guide prevention; Health system; Healthcare Systems; Impairment; Individual; Intervention; K-Series Research Career Programs; Knowledge; Label; Learning; Life; Link; Logistic Regressions; Maps; Measures; Mental Depression; Mental Health; Mental disorders; Mentors; Methods; Modeling; Modernization; Morbidity - disease rate; National Institute of Mental Health; Neurotic Disorders; Outcome; Patients; Performance; Pharmaceutical Preparations; Phenotype; Population; Positioning Attribute; Predictive Analytics; Predictive Value; Prevalence; Prevention; Prevention strategy; Procedures; Provider; Psychiatric epidemiology; Psychiatric therapeutic procedure; Recurrence; Research; Research Personnel; Research Training; Resources; Risk; Risk Factors; Sampling; Schizophrenia; Science; Screening procedure; Site; Solid; Stratification; Structure; Supervision; Symptoms; System; Testing; Training; United States; Validation; Work; World Health; base; behavioral health; biobank; biomedical informatics; bridge program; brief prevention intervention; career; child depression; cohort; depression prevention; depressive symptoms; deprivation; design; disability; epidemiology study; experience; genetic epidemiology; genomic data; health care service utilization; health record; high dimensionality; high risk; improved; machine learning method; multidisciplinary; non-genomic; novel; phenotyping algorithm; predictive modeling; prevent; prospective; random forest; research study; resilience; risk stratification; social; social determinants; social genomics; statistical and machine learning; stress related disorder; substance use; suicidal behavior; support vector machine; tool; translational research program; unsupervised learning

PROJECT ABSTRACT One in five adolescents in the United States will experience a depressive episode before age 18. Early prevention could offset a lifetime of morbidity including work and social impairment, substance use, and suicidal behavior. A critical step to preventing adolescent depression at a population level is the efficient detection of individuals who could benefit most from targeted intervention. However, known risk factors (e.g., subthreshold symptoms, cognitive styles, interpersonal factors) are often not widely assessed in practice until young people are presenting for psychiatric care, and prospective risk screening tools built in traditional research studies remain poorly implemented at scale in clinical settings where it may not be feasible for providers to routinely collect or integrate additional measures. Large-scale, routine electronic health records (EHRs) from major health systems present a powerful opportunity to overcome these prior limitations but have not yet been harnessed for adolescent depression and often lack environmental and genetic data that may inform etiological understanding and risk stratification. The overall aim of this K08 Career Development Award is to leverage large-scale EHR data with linked genomic and social determinants to enhance the systematic identification of young people at elevated risk of depression in real-world health settings. In this project, the candidate will develop and validate a novel phenotype algorithm for identifying adolescent depression cases from a major healthcare system in the United States containing up to 20 years of longitudinal EHR data for over six million individuals (Aim 1); integrate and comprehensively assess a range of potential social and genomic determinants for EHR-based adolescent depression (Aim 2); and apply modern statistical and machine learning methods to train and evaluate an initial prospective risk stratification model for adolescent depression based on routine EHR data (Aim 3). Improving the phenotyping and stratification of adolescent depression in EHRs will facilitate new avenues of research that will be the basis of subsequent R-level grants that include external validation across health systems, refinement of risk stratification and clinical trajectory models, and brief preventive interventions to enhance resilience in those at risk. Supported by a solid foundation in psychiatric and genetic epidemiology and a multidisciplinary team of world-class experts in an ideal environment, the candidate will acquire new expertise in predictive analytics, biomedical informatics (specifically EHR-exposome-genome integration), adolescent depression and prevention science through intensive mentored research and supervised training and professional development activities. This Award will provide the necessary training for the candidate to develop into a fully independent clinically informed investigator with a translational research program that bridges data science, statistical genetics, and developmental epidemiology to inform actionable strategies for early depression prevention and resilience promotion.

项目摘要 美国五分之一的青少年在 18 岁之前会经历抑郁症。早期预防 可以抵消一生的发病率，包括工作和社交障碍、药物滥用和自杀行为。 在人群层面预防青少年抑郁症的关键一步是有效检测个体 谁可以从有针对性的干预中受益最多。然而，已知的危险因素（例如阈下症状、 认知风格、人际因素）在实践中通常不会得到广泛评估，直到年轻人表现出来 精神科护理方面，传统研究中建立的前瞻性风险筛查工具仍然很差 在临床环境中大规模实施，而提供者可能无法定期收集或整合 额外措施。来自主要卫生系统的大规模常规电子健康记录 (EHR) 这是克服这些先前限制的有力机会，但尚未为青少年所利用 抑郁症，并且往往缺乏可能有助于了解病因和风险的环境和遗传数据 分层。 K08 职业发展奖的总体目标是利用大规模 EHR 数据 将基因组和社会决定因素联系起来，以加强对高风险年轻人的系统识别 现实世界健康环境中的抑郁症。在这个项目中，候选人将开发并验证一种新颖的 用于从美国主要医疗保健系统中识别青少年抑郁症病例的表型算法 包含超过 600 万人长达 20 年的纵向 EHR 数据的国家（目标 1）；整合和 全面评估基于 EHR 的青少年的一系列潜在社会和基因组决定因素 抑郁症（目标 2）；并应用现代统计和机器学习方法来训练和评估初始 基于常规 EHR 数据的青少年抑郁症前瞻性风险分层模型（目标 3）。改善 电子病历中青少年抑郁症的表型和分层将促进新的研究途径 将成为后续 R 级拨款的基础，包括跨卫生系统的外部验证、改进 风险分层和临床轨迹模型，以及简短的预防干预措施，以增强恢复能力 那些处于危险之中的人。以精神病学和遗传流行病学的坚实基础以及多学科的支持 世界一流的专家团队在理想的环境中，候选人将获得预测方面的新专业知识 分析、生物医学信息学（特别是 EHR-暴露组-基因组整合）、青少年抑郁症和 通过深入指导研究、监督培训和专业发展进行预防科学 活动。该奖项将为候选人提供必要的培训，使其发展成为完全独立的人 具有临床知识的研究者，具有将数据科学、统计学联系起来的转化研究项目 遗传学和发展流行病学为早期抑郁症预防和治疗提供可行的策略 复原力提升。