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Usage of Electronic Health Record Phenotyping in American Adult Patients with Schizophrenia to Improve Detection of Type II Diabetes Mellitus


Objective: Prevalence of type II diabetes is higher in diagnosed patients with schizophrenia when compared with the general adult population. With the propagation of electronic health records (EHR), we assess whether electronic health record phenotyping can improve diabetes mellitus type 2 (T2DM) screening when compared to conventional diabetic screening metrics in patients with schizophrenia.

Method: EHR data from 1267 patients with schizophrenia was used to develop a pre-screening tool for current T2DM using logistic regression and random forest models. Three types of models were created: the first used conventional diabetic screening criteria (BMI, age, gender, etc.) and their interactions. The second utilized conventional factors plus prescribed medications. The third

utilized conventional factors, prescriptions, and diagnoses as determined by ICD-9 codes.

Results: EHR phenotyping models utilizing conventional factors, prescriptions, and diagnosis information (EHR-DX) had improved ability to detect T2DM, when compared to models containing conventional (EHR-conventional) or conventional & prescription metrics (EHR-RX). Conversely, there was not a statistically significant improvement in T2DM detection between models containing conventional metrics and models containing conventional & prescription metrics (likelihood ratio test, p < 0.001). The AUC for the full EHR DX, EHR RX, and conventional models using logistic regression were 85.8%, 74.8%, and 72.7% respectively.

Several ICD codes were shown to have a significant association with diabetes diagnosis, including several from established predictors or comorbidities, such as prediabetes and heart disease. Associations between antipsychotic drugs and diabetes diagnosis were generally insignificant and lacked a discernible positive or negative association trend.

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