  Objectives: In the United States, 25% of people with type 2 diabetes are
undiagnosed. Conventional screening models use limited demographic information
to assess risk. We evaluated whether electronic health record (EHR) phenotyping
could improve diabetes screening, even when records are incomplete and data are
not recorded systematically across patients and practice locations. Methods: In
this cross-sectional, retrospective study, data from 9,948 US patients between
2009 and 2012 were used to develop a pre-screening tool to predict current type
2 diabetes, using multivariate logistic regression. We compared (1) a full EHR
model containing prescribed medications, diagnoses, and traditional predictive
information, (2) a restricted EHR model where medication information was
removed, and (3) a conventional model containing only traditional predictive
information (BMI, age, gender, hypertensive and smoking status). We
additionally used a random-forests classification model to judge whether
including additional EHR information could increase the ability to detect
patients with Type 2 diabetes on new patient samples. Results: Using a
patient's full or restricted EHR to detect diabetes was superior to using basic
covariates alone (p<0.001). The random forests model replicated on out-of-bag
data. Migraines and cardiac dysrhythmias were negatively associated with type 2
diabetes, while acute bronchitis and herpes zoster were positively associated,
among other factors. Conclusions: EHR phenotyping resulted in markedly superior
detection of type 2 diabetes in a general US population, could increase the
efficiency and accuracy of disease screening, and are capable of picking up
signals in real-world records.
