TY - JOUR
T1 - Assessing electronic health record phenotypes against gold-standard diagnostic criteria for diabetes mellitus
AU - DDC Phenotype Group
AU - Spratt, Susan E.
AU - Pereira, Katherine
AU - Granger, Bradi B.
AU - Batch, Bryan C.
AU - Phelan, Matthew
AU - Pencina, Michael
AU - Miranda, Marie Lynn
AU - Boulware, Ebony
AU - Lucas, Joseph E.
AU - Nelson, Charlotte L.
AU - Neely, Benjamin
AU - Goldstein, Benjamin A.
AU - Barth, Pamela
AU - Richesson, Rachel L.
AU - Riley, Isaretta L.
AU - Corsino, Leonor
AU - McPeek Hinz, Eugenia R.
AU - Rusincovitch, Shelley
AU - Green, Jennifer
AU - Barton, Anna Beth
AU - Kelley, Carly
AU - Hyland, Kristen
AU - Tang, Monica
AU - Elliott, Amanda
AU - Ruel, Ewa
AU - Clark, Alexander
AU - Mabrey, Melanie
AU - Morrissey, Kay Lyn
AU - Rao, Jyothi
AU - Hong, Beatrice
AU - Pierre-Louis, Marjorie
AU - Kelly, Katherine
AU - Jelesoff, Nicole
N1 - Funding Information:
The projects and the work described in this article are supported in part by (1) Grant Number 1C1CMS331018-01-00 from the Department of Health and Human Services, Centers for Medicare & Medicaid Services, and in part by (2) the Bristol Myers Squibb Foundation Together on Diabetes program, (3) NIH T32 grant Endocrinology and Metabolism Research Training Program of the National Institutes of Health under award number NIH 5T32DK007012, and (4) Grant UG1DA040317 from the National Institute on Drug Abuse. The contents of this article are solely the responsibility of the authors and have not been approved by the Department of Health and Human Services, Centers for Medicare & Medicaid Services, or the NIH.
Publisher Copyright:
© 2016 The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
PY - 2017/4/1
Y1 - 2017/4/1
N2 - Objective: We assessed the sensitivity and specificity of 8 electronic health record (EHR)-based phenotypes for diabetes mellitus against gold-standard American Diabetes Association (ADA) diagnostic criteria via chart review by clinical experts. Materials and Methods: We identified EHR-based diabetes phenotype definitions that were developed for various purposes by a variety of users, including academic medical centers, Medicare, the New York City Health Department, and pharmacy benefit managers. We applied these definitions to a sample of 173 503 patients with records in the Duke Health System Enterprise Data Warehouse and at least 1 visit over a 5-year period (2007-2011). Of these patients, 22 679 (13%) met the criteria of 1 or more of the selected diabetes phenotype definitions. A statistically balanced sample of these patients was selected for chart review by clinical experts to determine the presence or absence of type 2 diabetes in the sample. Results: The sensitivity (62-94%) and specificity (95-99%) of EHR-based type 2 diabetes phenotypes (compared with the gold standard ADA criteria via chart review) varied depending on the component criteria and timing of observations and measurements. Discussion and Conclusions: Researchers using EHR-based phenotype definitions should clearly specify the characteristics that comprise the definition, variations of ADA criteria, and how different phenotype definitions and components impact the patient populations retrieved and the intended application. Careful attention to phenotype definitions is critical if the promise of leveraging EHR data to improve individual and population health is to be fulfilled.
AB - Objective: We assessed the sensitivity and specificity of 8 electronic health record (EHR)-based phenotypes for diabetes mellitus against gold-standard American Diabetes Association (ADA) diagnostic criteria via chart review by clinical experts. Materials and Methods: We identified EHR-based diabetes phenotype definitions that were developed for various purposes by a variety of users, including academic medical centers, Medicare, the New York City Health Department, and pharmacy benefit managers. We applied these definitions to a sample of 173 503 patients with records in the Duke Health System Enterprise Data Warehouse and at least 1 visit over a 5-year period (2007-2011). Of these patients, 22 679 (13%) met the criteria of 1 or more of the selected diabetes phenotype definitions. A statistically balanced sample of these patients was selected for chart review by clinical experts to determine the presence or absence of type 2 diabetes in the sample. Results: The sensitivity (62-94%) and specificity (95-99%) of EHR-based type 2 diabetes phenotypes (compared with the gold standard ADA criteria via chart review) varied depending on the component criteria and timing of observations and measurements. Discussion and Conclusions: Researchers using EHR-based phenotype definitions should clearly specify the characteristics that comprise the definition, variations of ADA criteria, and how different phenotype definitions and components impact the patient populations retrieved and the intended application. Careful attention to phenotype definitions is critical if the promise of leveraging EHR data to improve individual and population health is to be fulfilled.
KW - EHR phenotypes
KW - diabetes identification
KW - diabetes registries
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U2 - 10.1093/jamia/ocw123
DO - 10.1093/jamia/ocw123
M3 - Article
C2 - 27616701
AN - SCOPUS:85031024709
SN - 1067-5027
VL - 24
SP - e121-e128
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - e1
ER -