211-LB: Developing "Gold Standard" Diagnoses for Type 1 and Type 2 Diabetes in Adults from Electronic Health Record Data

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 211-LB ◽  
Author(s):  
M. SUE KIRKMAN ◽  
JENNIFER G. NOONEY ◽  
STEPHEN R. BENOIT ◽  
KATHERINE A. BERGAMO ◽  
KAI M. BULLARD ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Electronic Health Record ◽  
Gold Standard ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

scholarly journals Automated Detection and Classification of Type 1 Versus Type 2 Diabetes Using Electronic Health Record Data

Diabetes Care ◽  
2012 ◽  
Vol 36 (4) ◽  
pp. 914-921 ◽  
Author(s):  
M. Klompas ◽  
E. Eggleston ◽  
J. McVetta ◽  
R. Lazarus ◽  
L. Li ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Electronic Health Record ◽  
Automated Detection ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

scholarly journals Pharmaceutical cost and multimorbidity with type 2 diabetes mellitus using electronic health record data

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Carla Sancho-Mestre ◽  
David Vivas-Consuelo ◽  
Luis Alvis-Estrada ◽  
Martin Romero ◽  
Ruth Usó-Talamantes ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Pharmaceutical Cost ◽  
Record Data ◽  
Electronic Health

scholarly journals Validation of an algorithm for identifying type 1 diabetes in adults based on electronic health record data

2018 ◽  
Vol 27 (10) ◽  
pp. 1053-1059 ◽  
Author(s):  
Emily B. Schroeder ◽  
William T. Donahoo ◽  
Glenn K. Goodrich ◽  
Marsha A. Raebel
Keyword(s):  
Type 1 Diabetes ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

scholarly journals Variants in STAU2 associate with metformin response in a type 2 diabetes cohort: a pharmacogenomics study using real-world electronic health record data

2020 ◽  
Author(s):  
Yanfei Zhang ◽  
Ying Hu ◽  
Kevin Ho ◽  
Dustin N. Hartzel ◽  
Vida Abedi ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Glycemic Control ◽  
Electronic Health Record ◽  
Missense Variant ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Short And Long Term ◽  
Electronic Health

AbstractType 2 diabetes mellitus (T2DM) is a major health and economic burden because of the seriousness of the disease and its complications. Improvements in short- and long-term glycemic control is the goal of diabetes treatment. To investigate the longitudinal management of T2DM at Geisinger, we interrogated the electronic health record (EHR) information and identified a T2DM cohort including 125,477 patients using the Electronic Medical Records and Genomics Network (eMERGE) T2DM phenotyping algorithm. We investigated the annual anti-diabetic medication usage and the overall glycemic control using hemoglobin A1c (HbA1c). Metformin remains the most frequently medication despite the availability of the new classes of anti-diabetic medications. Median value of HbA1c decreased to 7% in 2002 and since remained stable, indicating a good glycemic management in Geisinger population. Using metformin as a pilot study, we identified three groups of patients with distinct HbA1c trajectories after metformin treatment. The variabilities in metformin response is mainly explained by the baseline HbA1c. The pharmacogenomic analysis of metformin identified a missense variant rs75740279 (Leu/Val) for STAU2 associated with the metformin response. This strategy can be applied to study other anti-diabeticmedications. Such research will facilitate the translational healthcare for better T2DM management.

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

2016 ◽  
Vol 24 (e1) ◽  
pp. e121-e128 ◽  
Author(s):  
Susan E Spratt ◽  
Katherine Pereira ◽  
Bradi B Granger ◽  
Bryan C Batch ◽  
Matthew Phelan ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
New York ◽  
Electronic Health Record ◽  
Diagnostic Criteria ◽  
Gold Standard ◽  
Chart Review ◽  
Health Record ◽  
Electronic Health

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.

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