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

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.

Therapeutic inertia in the management of dyslipidaemia and hypertension in incident type 2 diabetes and the resulting risk factor burden: Real‐world evidence from primary care

“…Patients were identified to have depression (a) with a diagnosis code for depression or (b) with a record of at least two prescriptions for antidepressants in the absence of clinical codes for other mental illnesses. 34 Antihypertensive therapy was identified using the Anatomical Therapeutic Classification and British National Formulary coding systems, and included diuretics, peripheral vasodilators (excluding nicotinic acid), beta blockers, calcium channel blockers, and agents acting on the renin-angiotensin system. LLT included statins, bile acid sequestrants, fibrates, nicotinic acid, proprotein convertase subtilisin/ kexin type 9 (PCSK9) inhibitors, and potent (≥1 g) forms of omega-3/ fish/krill oil.…”

Combining structured and unstructured data in EMRs to create clinically-defined EMR-derived cohorts

“…However, diagnostic codes alone are insufficient for identifying clinical EMR-derived cohorts due to strict rules adhered to for coding, underreporting and the complexity of diseases being assigned a single code [ 12 ]. The cohort identification process becomes even more challenging when diseases and conditions have heterogeneous aetiology and a spectrum of severity [ 13 – 15 ]. Approaches to data extraction from production EMR systems also have to consider whether there is ready access to production-level EMR environments (e.g.…”

“…Patients with possible acute coronary syndrome (ACS) were selected as an exemplar given its heterogeneous aetiology and spectrum of severity during presentation. This approach can be applied to other complex diseases/conditions including but not limited to mental illness, asthma, rheumatoid arthritis, chronic kidney disease [ 13 – 15 , 19 ].…”

Combining structured and unstructured data in eMRs to create clinically-defined eMR-derived cohorts

“…However, diagnostic codes alone are insufficient for identifying clinical eMRderived cohorts due to strict rules adhered to for coding, underreporting and the complexity of diseases being assigned a single code [10]. The cohort identification process becomes even more challenging when diseases and conditions have heterogeneous aetiology and a spectrum of severity [11][12][13].…”

“…Patients with possible acute coronary syndrome (ACS) were selected as an exemplar given it's heterogeneous aetiology and spectrum of severity during presentation. This approach can be applied to other complex diseases/conditions including but not limited to mental illness, asthma, rheumatoid arthritis, chronic kidney disease [11][12][13]17].…”