Data-driven subtyping in major depressive disorder

重度抑郁症的数据驱动亚型

基本信息

批准号：
10211310
负责人：
ROY H. Perlis
金额：
$ 83.22万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-16 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10211310
关键词：
Address Algorithms Area Biological Biology Cessation of life Classification Clinical Code Collaborations Data Data Set Dementia Development Diagnostic Dimensions Disease Disease remission Electronic Health Record Endocrine System Diseases Engineering Equilibrium Evidence based treatment Functional disorder Health Care Costs Health system Heart Diseases Heterogeneity Hospitals Human Individual Inflammatory Bowel Diseases Machine Learning Major Depressive Disorder Malignant Neoplasms Manuals Measures Medical Medicine Mental Depression Methods Modeling Mood Disorders Morbidity - disease rate National Institute of Mental Health Outcome Patients Performance Phenotype Prevalence Public Health Publications Quality of life Research Personnel Selection for Treatments Series Subgroup Suicide Supervision Symptoms System Therapeutic Variant Work base biomarker identification clinical investigation clinical subtypes cohort depressive symptoms disorder subtype experience ineffective therapies innovation machine learning method mortality mortality risk novel therapeutics patient subsets phenomenological models precision medicine risk stratification standard care success targeted treatment therapy resistant treatment response treatment strategy

Address Algorithms Area Biological Biology Cessation of life Classification Clinical Code Collaborations Data Data Set Dementia Development Diagnostic Dimensions Disease Disease remission Electronic Health Record Endocrine System Diseases Engineering Equilibrium Evidence based treatment Functional disorder Health Care Costs Health system Heart Diseases Heterogeneity Hospitals Human Individual Inflammatory Bowel Diseases Machine Learning Major Depressive Disorder Malignant Neoplasms Manuals Measures Medical Medicine Mental Depression Methods Modeling Mood Disorders Morbidity - disease rate National Institute of Mental Health Outcome Patients Performance Phenotype Prevalence Public Health Publications Quality of life Research Personnel Selection for Treatments Series Subgroup Suicide Supervision Symptoms System Therapeutic Variant Work base biomarker identification clinical investigation clinical subtypes cohort depressive symptoms disorder subtype experience ineffective therapies innovation machine learning method mortality mortality risk novel therapeutics patient subsets phenomenological models precision medicine risk stratification standard care success targeted treatment therapy resistant treatment response treatment strategy

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项目摘要

Abstract Major depressive disorder contributes substantially to morbidity, mortality, and health care cost. Standard treatments are ineffective for up to a third of patients, so new treatment options are needed along with strategies to make more effective use of existing treatments. However, progress in expanding therapeutic options has been hindered by heterogeneity in clinical presentation and course of depression. In other disorders such as inflammatory bowel disease, cancer, and dementia, identifying disease subtypes has led to therapeutic discoveries. In major depressive disorder, efforts to identify subtypes based on clinical observation have yielded limited success, primarily because of the lack of availability of adequate cohorts for replication, and because those features most apparent to clinicians may not be the most relevant for differentiating subgroups. Efforts to leverage large electronic health record data sets for subtyping address some of these challenges, but standard approaches may not yield human-interpretable features nor those with value in prediction. The investigators have developed methods for engineering features that balance utility in prediction with interpretability. Preliminary work by the investigators during a year of R56 support yielding 4 publications demonstrates that this approach indeed yields coherent topics without sacrificing predictive validity; electronic health records contain meaningful data that facilitates identification of interpretable patient subgroups. The present study draws on very large cohorts of individuals with major depression, defined by a validated algorithm, in electronic health records from two health systems. It will first apply methods developed by the investigators to identify MDD subtypes. These subtypes will then be examined in terms of predictive validity as well as interpretability by clinicians. The study builds on a productive collaboration between a team experienced in mood disorder phenotyping and clinical investigation, analysis of large-scale longitudinal electronic health records, and development and application of innovative methods in machine learning that yield interpretable models rather than black boxes. Data-driven disease subtyping will facilitate clinically useful risk stratification as well as biological study of mood disorders.

抽象的重度抑郁症对发病率，死亡率和医疗保健成本产生了重大贡献。标准疗法最多无需三分之一的患者，因此需要新的治疗选择以及更有效利用现有治疗方法的策略。但是，进展临床表现和课程中的异质性阻碍了扩大的治疗选择。抑郁症。在其他疾病中，例如炎症性肠病，癌症和痴呆症，鉴定疾病亚型导致了治疗性发现。在重度抑郁症中，努力确定基于临床观察的亚型取得了有限的成功，主要是因为缺乏适当的复制队列可用性，并且因为这些功能最明显临床医生可能与区分亚组最相关。努力利用大型亚型的电子健康记录数据集解决了其中一些挑战，但标准方法可能不会产生人类解剖特征，也不会产生具有预测价值的特征。调查人员开发了用于平衡实用程序的工程特征的方法具有解释性的预测。在R56支持的一年中，调查人员的初步工作产生4个出版物表明，这种方法确实产生了连贯的话题牺牲预测有效性；电子健康记录包含有意义的数据识别可解释的患者亚组。本研究借鉴了非常大的队列由经过验证的算法定义的重度抑郁症的人，在电子健康记录中两个卫生系统。它将首先应用调查人员开发的方法来识别MDD 亚型。然后，将根据预测有效性以及临床医生的解释性。该研究以情绪障碍经验丰富的团队之间的富有成效的合作为基础表型和临床研究，大规模纵向电子健康记录的分析，以及创新方法在机器学习中的开发和应用，以产生可解释的模型而不是黑匣子。数据驱动的疾病亚型将有助于临床上有用的风险分层以及情绪障碍的生物学研究。