title: Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study.
abstract: OBJECTIVE
  An estimated 25% of type two diabetes mellitus (DM2) patients in the United States are undiagnosed due to inadequate screening, because it is prohibitive to administer laboratory tests to everyone. We assess whether electronic health record (EHR) phenotyping could improve DM2 screening compared to conventional models, even when records are incomplete and not recorded systematically across patients and practice locations, as is typically seen in practice.
  
  
  METHODS
  In this cross-sectional, retrospective study, EHR data from 9948 US patients were used to develop a pre-screening tool to predict current DM2, using multivariate logistic regression and a random-forests probabilistic model for out-of-sample validation. We compared (1) a full EHR model containing commonly prescribed medications, diagnoses (as ICD9 categories), and conventional predictors, (2) a restricted EHR DX model which excluded medications, and (3) a conventional model containing basic predictors and their interactions (BMI, age, sex, smoking status, hypertension).
  
  
  RESULTS
  Using a patient's full EHR or restricted EHR was superior to using basic covariates alone for detecting individuals with diabetes (hierarchical X(2) test, p<0.001). Migraines, depot medroxyprogesterone acetate, and cardiac dysrhythmias were associated negatively with DM2, while sexual and gender identity disorder diagnosis, viral and chlamydial infections, and herpes zoster were associated positively. Adding EHR phenotypes improved classification; the AUC for the full EHR Model, EHR DX model, and conventional model using logistic regression, were 84.9%, 83.2%, and 75.0% respectively. For random forest machine learning out-of-sample prediction, accuracy also was improved when using EHR phenotypes; the AUC values were 81.3%, 79.6%, and 74.8%, respectively. Improved AUCs reflect better performance for most thresholds that balance sensitivity and specificity.
  
  
  CONCLUSIONS
  EHR phenotyping resulted in markedly superior detection of DM2, even in the face of missing and unsystematically recorded data, based on the ROC curves. EHR phenotypes could more efficiently identify which patients do require, and don't require, further laboratory screening. When applied to the current number of undiagnosed individuals in the United States, we predict that incorporating EHR phenotype screening would identify an additional 400,000 patients with active, untreated diabetes compared to the conventional pre-screening models.
authors.affiliation: Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, United States.
authors.forename: Ariana E
authors.initials: AE
authors.lastname: Anderson
authors.affiliation: Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, United States; Department of Biomathematics, David Geffen School of Medicine at UCLA, United States. Electronic address: WesleyTK@UCLA.edu.
authors.forename: Wesley T
authors.initials: WT
authors.lastname: Kerr
authors.affiliation: Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, United States.
authors.forename: April
authors.initials: A
authors.lastname: Thames
authors.affiliation: Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, United States.
authors.forename: Tong
authors.initials: T
authors.lastname: Li
authors.affiliation: Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, United States.
authors.forename: Jiayang
authors.initials: J
authors.lastname: Xiao
authors.affiliation: Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, United States; Departments of Psychology, Neurology, Radiology, Biomedical Engineering, Biomedical Physics, University of California, Los Angeles, United States; California NanoSystems Institute, University of California, Los Angeles, United States.
authors.forename: Mark S
authors.initials: MS
authors.lastname: Cohen
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D003430:Cross-Sectional Studies; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis* / Q000453:epidemiology; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D008490:Medical Informatics / Q000379:methods*; D008875:Middle Aged; D015233:Models, Statistical; D010641:Phenotype; D012372:ROC Curve; D012189:Retrospective Studies; D012307:Risk Factors; D014481:United States
keywords: Electronic health records; Evidence-based medicine; Phenotype; Population screening; Type 2 diabetes