Defining Depression Cohorts Using the EHR: Multiple Phenotypes Based on ICD-9 Codes and Medication Orders

Objective: 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. Materials and Methods: We propose an 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 demonstrate the convergent validity of these phenotype constructs by presenting multiple lines of evidence associated with depression. Results: Convergent validity is derived from expected patterns in health care utilization, psychiatric prescriptions, indicators of suicidality, diagnoses of serious comorbidity, mortality, symptom severity, and finally, polygenic risk scores. Discussion: The algorithm is generalizable to most EHR data sets because it requires only International Classification of Diseases (ICD) diagnostic codes and medication orders and can be used for stratification of an entire patient population. Conclusion: Careful consideration must be given to the definitions of patient cohorts when utilizing EHR data, particularly when classifying subjects with heterogenous disorders such as MDD. This algorithm may prove useful to others that wish to study depression in entire patient populations with EHR data.