Ingram Wendy Marie, Baker Anna M, Bauer Christopher R, Brown Jason P, Goes Fernando S, Larson Sharon, Zandi Peter P
Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Department of Psychiatry, Geisinger Health System, Danville, Pennsylvania, USA.
Neurol Psychiatry Brain Res. 2020 Jun;36:18-26. doi: 10.1016/j.npbr.2020.02.002. Epub 2020 Feb 21.
Major Depressive Disorder (MDD) is one of the most common mental illnesses and a leading cause of disability worldwide. Electronic Health Records (EHR) allow researchers to conduct unprecedented large-scale observational studies investigating MDD, its disease development and its interaction with other health outcomes. While there exist methods to classify patients as clear cases or controls, given specific data requirements, there are presently no simple, generalizable, and validated methods to classify an entire patient population into varying groups of depression likelihood and severity.
We have tested a simple, pragmatic electronic phenotype algorithm that classifies patients into one of five mutually exclusive, ordinal groups, varying in depression phenotype. Using data from an integrated health system on 278,026 patients from a 10-year study period we have tested the convergent validity of these constructs using measures of external validation, including patterns of psychiatric prescriptions, symptom severity, indicators of suicidality, comorbidity, mortality, health care utilization, and polygenic risk scores for MDD.
We found consistent patterns of increasing morbidity and/or adverse outcomes across the five groups, providing evidence for convergent validity.
The study population is from a single rural integrated health system which is predominantly white, possibly limiting its generalizability.
Our study provides initial evidence that a simple algorithm, generalizable to most EHR data sets, provides categories with meaningful face and convergent validity that can be used for stratification of an entire patient population.
重度抑郁症（MDD）是最常见的精神疾病之一，也是全球致残的主要原因。电子健康记录（EHR）使研究人员能够开展前所未有的大规模观察性研究，以调查MDD、其疾病发展过程以及它与其他健康结果的相互作用。虽然存在将患者分类为明确病例或对照的方法，但鉴于特定的数据要求，目前尚无简单、可推广且经过验证的方法将整个患者群体分类为不同抑郁可能性和严重程度的组。
我们测试了一种简单、实用的电子表型算法，该算法将患者分为五个相互排斥的有序组之一，这些组在抑郁表型上有所不同。利用一个综合医疗系统在10年研究期间收集的278,026名患者的数据，我们使用外部验证指标，包括精神科处方模式、症状严重程度、自杀倾向指标、合并症、死亡率、医疗保健利用率以及MDD的多基因风险评分，来测试这些结构的收敛效度。
我们在这五个组中发现了发病率和/或不良后果增加的一致模式，为收敛效度提供了证据。
研究人群来自一个主要为白人的单一农村综合医疗系统，这可能限制了其可推广性。
我们的研究提供了初步证据，表明一种可推广到大多数EHR数据集的简单算法能够提供具有有意义的表面效度和收敛效度的类别，可用于对整个患者群体进行分层。
