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：
10393687
负责人：
ROY H. Perlis
金额：
$ 77.2万
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/10393687
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

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.

抽象的重度抑郁症极大地增加了发病率、死亡率和医疗保健费用。标准治疗对多达三分之一的患者无效，因此需要新的治疗选择以及更有效地利用现有治疗方法的策略。然而，进展临床表现和病程的异质性阻碍了扩大治疗选择抑郁症。在炎症性肠病、癌症和痴呆等其他疾病中，识别疾病亚型导致了治疗的发现。在重度抑郁症中，努力识别基于临床观察的亚型取得的成功有限，主要是因为缺乏足够的队列可供复制，并且因为这些特征对于临床医生对于区分亚组可能不是最相关的。努力发挥大杠杆作用用于子分类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可解释的特征，也不会产生具有预测价值的特征。研究人员开发了一些工程特征的方法，可以平衡实用性具有可解释性的预测。研究人员在一年的 R56 支持期间所做的初步工作产生 4 篇出版物表明，这种方法确实产生了连贯的主题，而无需牺牲预测有效性；电子健康记录包含有意义的数据，有助于识别可解释的患者亚组。本研究借鉴了非常大的群体电子健康记录中通过经过验证的算法定义的重度抑郁症患者两个卫生系统。它将首先应用研究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可解释性。该研究建立在经历过情绪障碍的团队之间富有成效的合作基础上表型分析和临床调查、大规模纵向电子健康记录分析、机器学习创新方法的开发和应用，产生可解释的结果模型而不是黑匣子。数据驱动的疾病亚型将促进临床有用的风险情绪障碍的分层以及生物学研究。