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Study: EHR Phenotype Data Can Improve Detection of Type 2 Diabetes

February 17, 2016
by Heather Landi
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Researchers from the University of California Los Angeles’ Department of Psychiatry and Biobehavioral Sciences found that the use of electronic health record (EHR) phenotyping to screen for Type 2 diabetes out-performed conventional screening methods, according to a new study published in the Journal of Biomedical Informatics.

An estimated 25 percent 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, the researchers wrote in the study report.

The research team examined the use of EHR phenotyping to improve DM2 screening compared to conventional models. EHRs contain many phenotypes, such as specific traits or the presence of disease.

In the cross-sectional, retrospective study, EHR data from 9,948 U.S. patients were used to develop a pre-screening tool to predict current DM2. The researchers compared a full EHR model containing commonly prescribed medications, diagnoses (as ICD9 categories), and conventional predictors, and also compared a restricted EHR DX model which excluded medications, as well as a conventional model containing basic predictors and their interactions (body mass index, age, sex, smoking status, hypertension).

The research team found that a diagnosis of “sexual and gender identity disorders” and “viral infections” increased the risk of the disease.

According to the study report, researchers found that EHR phenotyping “resulted in markedly superior detection of DM2, even in the face of missing and unsystematically recorded data, based on the receiver operating characteristic (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,” the researchers wrote.

 

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