Data-driven subtyping in major depressive disorder

摘要 严重抑郁障碍对发病率、死亡率和医疗费用有很大影响。 标准疗法对多达三分之一的患者无效，因此需要新的治疗方案。 以及更有效地利用现有治疗方法的策略。然而，在这方面的进展 临床表现和病程的异质性阻碍了扩大治疗选择 抑郁的症状。 在其他疾病中，如炎症性肠病、癌症和痴呆症，识别 疾病亚型导致了治疗方面的发现。在严重的抑郁障碍中，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 是否有足够的可用于复制的队列，以及因为这些特征对 临床医生可能不是区分亚组的最相关因素。努力利用大笔资金 用于分型的电子健康记录数据集解决了其中的一些挑战，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。 研究人员已经开发出工程特性的方法，以平衡 具有可解释性的预测。调查人员在R56支持一年中所做的初步工作 4份出版物表明，这种方法确实产生了连贯的主题，而不是 牺牲预测有效性；电子健康记录包含有意义的数据，有助于 可解释的患者亚组的识别。目前的研究利用了非常大的一组 由经过验证的算法定义的患有严重抑郁症的个人，在来自 两个医疗系统。它将首先应用调查人员开发的方法来识别MDD 子类型。然后将根据预测效度以及 临床医生的可解释性。 这项研究建立在一个在情绪障碍方面经验丰富的团队之间富有成效的合作基础上 表型和临床调查，大规模纵向电子健康记录分析， 以及机器学习中产生可解释的创新方法的开发和应用 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 情绪障碍的分层和生物学研究。

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.