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

The importance of methodology when conducting high-quality health behavior research cannot be overstated. Electronic Health Records (EHR) allow researchers to conduct unprecedented large-scale studies. However, careful consideration must be given to how to define patient cohorts, specifically when utilizing EHR for examining patients with depression. Because depression has been linked to increased morbidity and mortality in many disease groups and leads to higher health care utilization, better methods for identifying patients with depression must be developed in order to rigorously study the impact of depression on these outcomes. Identifying patients using only ICD9 codes for depression may result in inclusion of clinically depressed patients in comparison groups. Thus, more nuanced electronic phenotypes may better delineate patients that are receiving treatment for depression. We demonstrate the utility of a new method involving multiple depression phenotypes on a 10.75-year cohort from an integrated health system (n=287,281). Here we recommend a novel and easily adaptable method of categorizing patients. In this method, four groups are identified using ICD-9 codes and medication orders from an EHR which have varying levels of depression likelihood and severity: Dep ICD9, Rx no ICD9, Rx non-dep, and No Dep. We then measure a variety of EHR-based features including utilization patterns, medication orders, comorbidities, mortality data and symptom assessment scores to establish convergent validity of these groups. This superior and simple method allows for large scale studies of depressed patients, while accounting for the limitations associated with using specific electronic phenotypes for analysis of data from the EHR.