使用电子病历定义重度抑郁症队列:基于ICD-9代码和用药顺序的多种表型

Wendy Marie Ingram , Anna M. Baker , Christopher R. Bauer , Jason P. Brown , Fernando S. Goes , Sharon Larson , Peter P. Zandi
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引用次数: 0

摘要

重度抑郁症(MDD)是世界上最常见的精神疾病之一,也是导致残疾的主要原因之一。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,调查重度抑郁症、其疾病发展及其与其他健康结果的相互作用。虽然存在将患者划分为明确病例或对照的方法,但鉴于特定的数据要求,目前还没有简单、通用和有效的方法将整个患者群体划分为抑郁可能性和严重程度不同的组。方法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种互斥的、顺序的、不同抑郁表型的组。使用来自一个综合卫生系统的数据,从一个10年的研究期间,278,026名患者,我们使用外部验证的措施,包括精神病处方模式,症状严重程度,自杀指标,合并症,死亡率,医疗保健利用和多基因风险评分,测试了这些结构的趋同效度。结果:我们发现在五组中发病率和/或不良结果增加的一致模式,为趋同效度提供了证据。研究人群来自单一的农村综合卫生系统,主要是白人,可能限制了其普遍性。我们的研究提供了初步证据,证明一个简单的算法,可推广到大多数电子病历数据集,提供具有有意义的面孔和收敛有效性的分类,可用于整个患者群体的分层。
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Defining major depressive disorder cohorts using the EHR: Multiple phenotypes based on ICD-9 codes and medication orders

Background

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.

Methods

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.

Results

We found consistent patterns of increasing morbidity and/or adverse outcomes across the five groups, providing evidence for convergent validity.

Limitations

The study population is from a single rural integrated health system which is predominantly white, possibly limiting its generalizability.

Conclusion

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.

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期刊介绍: Neurology, Psychiatry & Brain Research publishes original papers and reviews in biological psychiatry, brain research, neurology, neuropsychiatry, neuropsychoimmunology, psychopathology, psychotherapy. The journal has a focus on international and interdisciplinary basic research with clinical relevance. Translational research is particularly appreciated. Authors are allowed to submit their manuscript in their native language as supplemental data to the English version. Neurology, Psychiatry & Brain Research is related to the oldest German speaking journal in this field, the Centralblatt fur Nervenheilkunde, Psychiatrie und gerichtliche Psychopathologie, founded in 1878. The tradition and idea of previous famous editors (Alois Alzheimer and Kurt Schneider among others) was continued in modernized form with Neurology, Psychiatry & Brain Research. Centralblatt was a journal of broad scope and relevance, now Neurology, Psychiatry & Brain Research represents a journal with translational and interdisciplinary perspective, focusing on clinically oriented research in psychiatry, neurology and neighboring fields of neurosciences and psychology/psychotherapy with a preference for biologically oriented research including basic research. Preference is given for papers from newly emerging fields, like clinical psychoimmunology/neuroimmunology, and ideas.
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