Defining major depressive disorder cohorts using the EHR: Multiple phenotypes based on ICD-9 codes and medication orders

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

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)18-26
Number of pages9
JournalNeurology Psychiatry and Brain Research
Volume36
DOIs
StatePublished - Jun 2020

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Keywords

  • Clinical informatics
  • Depression
  • Electronic health records
  • Phenotypic algorithms

ASJC Scopus subject areas

  • Neuroscience(all)
  • Clinical Neurology
  • Psychiatry and Mental health

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