Data-driven subtyping in major depressive disorder

摘要 重度抑郁症对发病率、死亡率和卫生保健费用有很大影响。 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 沿着更有效地利用现有治疗方法的策略。然而， 临床表现和病程的异质性阻碍了治疗选择的扩展 抑郁症 在其他疾病如炎症性肠病、癌症和痴呆症中， 疾病亚型导致了治疗发现。在重度抑郁症中， 基于临床观察的亚型取得了有限的成功，主要是因为缺乏 有足够的队列进行复制，因为这些特征对 临床医生对于区分亚组可能不是最相关的。努力利用大型 用于分型的电子健康记录数据集解决了其中一些挑战，但标准 这些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。 研究人员已经开发出工程特征的方法， 可解释性预测。调查人员在R56支持一年期间的初步工作 产生4篇出版物表明，这种方法确实产生了连贯的主题， 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。本研究利用了大量的 由经过验证的算法定义的重度抑郁症患者，在电子健康记录中， 两个卫生系统。它将首先应用研究者开发的方法来识别MDD 亚型然后将根据预测有效性以及 临床医生的可解释性。 这项研究建立在一个有情绪障碍经验的团队之间的富有成效的合作之上。 表型和临床调查，分析大规模纵向电子健康记录， 以及机器学习中创新方法的开发和应用， 而不是黑盒子。数据驱动的疾病亚型将促进临床有用的风险 分层以及情绪障碍的生物学研究。

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.