Assessing electronic health record phenotypes against gold-standard diagnostic criteria for diabetes mellitus.

Autor:	Spratt SE; Department of Medicine, Duke University School of Medicine, Durham, NC, USA., Pereira K; Duke University School of Nursing, Durham, NC, USA., Granger BB; Duke University School of Nursing, Durham, NC, USA., Batch BC; Department of Medicine, Duke University School of Medicine, Durham, NC, USA., Phelan M; Duke Clinical Research Institute, Durham, NC, USA., Pencina M; Duke Clinical Research Institute, Durham, NC, USA.; Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA., Miranda ML; Rice University and Baylor College of Medicine, Houston, TX, USA., Boulware E; Department of Medicine, Duke University School of Medicine, Durham, NC, USA., Lucas JE; Department of Statistical Science, Duke University, Durham, NC, USA., Nelson CL; Duke Clinical Research Institute, Durham, NC, USA., Neely B; Duke Clinical Research Institute, Durham, NC, USA., Goldstein BA; Duke Clinical Research Institute, Durham, NC, USA.; Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA., Barth P; Duke Translational Medicine Institute, Durham, NC, USA., Richesson RL; Duke University School of Nursing, Durham, NC, USA., Riley IL; Department of Medicine, Duke University School of Medicine, Durham, NC, USA., Corsino L; Department of Medicine, Duke University School of Medicine, Durham, NC, USA., McPeek Hinz ER; Department of Medicine, Duke University School of Medicine, Durham, NC, USA., Rusincovitch S; Duke Translational Medicine Institute, Durham, NC, USA., Green J; Department of Medicine, Duke University School of Medicine, Durham, NC, USA., Barton AB; Department of Medicine, Duke University School of Medicine, Durham, NC, USA., Kelley C, Hyland K, Tang M, Elliott A, Ruel E, Clark A, Mabrey M, Morrissey KL, Rao J, Hong B, Pierre-Louis M, Kelly K, Jelesoff N
Jazyk:	angličtina
Zdroj:	Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2017 Apr 01; Vol. 24 (e1), pp. e121-e128.
DOI:	10.1093/jamia/ocw123
Abstrakt:	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. (© 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)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu