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


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.


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.
Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Susan E
SE
Spratt
Duke University School of Nursing, Durham, NC, USA.
Katherine
K
Pereira
Duke University School of Nursing, Durham, NC, USA.
Bradi B
BB
Granger
Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Bryan C
BC
Batch
Duke Clinical Research Institute, Durham, NC, USA.
Matthew
M
Phelan
Duke Clinical Research Institute, Durham, NC, USA.|Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Michael
M
Pencina
Rice University and Baylor College of Medicine, Houston, TX, USA.
Marie Lynn
ML
Miranda
Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Ebony
E
Boulware
Department of Statistical Science, Duke University, Durham, NC, USA.
Joseph E
JE
Lucas
Duke Clinical Research Institute, Durham, NC, USA.
Charlotte L
CL
Nelson
Duke Clinical Research Institute, Durham, NC, USA.
Benjamin
B
Neely
Duke Clinical Research Institute, Durham, NC, USA.|Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Benjamin A
BA
Goldstein
Duke Translational Medicine Institute, Durham, NC, USA.
Pamela
P
Barth
Duke University School of Nursing, Durham, NC, USA.
Rachel L
RL
Richesson
Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Isaretta L
IL
Riley
Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Leonor
L
Corsino
Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Eugenia R
ER
McPeek Hinz
Duke Translational Medicine Institute, Durham, NC, USA.
Shelley
S
Rusincovitch
Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Jennifer
J
Green
Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Anna Beth
AB
Barton
Carly
C
Kelley
Kristen
K
Hyland
Monica
M
Tang
Amanda
A
Elliott
Ewa
E
Ruel
Alexander
A
Clark
Melanie
M
Mabrey
Kay Lyn
KL
Morrissey
Jyothi
J
Rao
Beatrice
B
Hong
Marjorie
M
Pierre-Louis
Katherine
K
Kelly
Nicole
N
Jelesoff
D000465:Algorithms; D003920:Diabetes Mellitus / Q000097:blood / Q000175:diagnosis*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000175:diagnosis; D057286:Electronic Health Records; D006442:Glycated Hemoglobin / Q000032:analysis; D006801:Humans; D010641:Phenotype; D012680:Sensitivity and Specificity
D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human
EHR phenotypes; diabetes identification; diabetes registries