Development of electronic health record based algorithms to identify individuals with diabetic retinopathy

Abstract Objectives To develop, validate, and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHRs). Materials and Methods We developed and validated electronic health record (EHR)-based algorithms to identify DR cases and individual...

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Published in:Journal of the American Medical Informatics Association : JAMIA Vol. 31; no. 11; pp. 2560 - 2570
Main Authors: Breeyear, Joseph H, Mitchell, Sabrina L, Nealon, Cari L, Hellwege, Jacklyn N, Charest, Brian, Khakharia, Anjali, Halladay, Christopher W, Yang, Janine, Garriga, Gustavo A, Wilson, Otis D, Basnet, Til B, Hung, Adriana M, Reaven, Peter D, Meigs, James B, Rhee, Mary K, Sun, Yang, Lynch, Mary G, Sobrin, Lucia, Brantley, Milam A, Sun, Yan V, Wilson, Peter W, Iyengar, Sudha K, Peachey, Neal S, Phillips, Lawrence S, Edwards, Todd L, Giri, Ayush
Format: Journal Article
Language:English
Published: England Oxford University Press 01.11.2024
Subjects:
Adult
Aged
Algorithms
Case-Control Studies
Diabetes Mellitus, Type 1 - diagnosis
Diabetes Mellitus, Type 2 - diagnosis
Diabetic Retinopathy - diagnosis
Electronic Health Records
Female
Humans
International Classification of Diseases
Male
Middle Aged
algorithm validation
algorithm development
diabetes complications
diabetic retinopathy
ISSN:1067-5027, 1527-974X, 1527-974X
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Summary:Abstract Objectives To develop, validate, and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHRs). Materials and Methods We developed and validated electronic health record (EHR)-based algorithms to identify DR cases and individuals with type I or II diabetes without DR (controls) in 3 independent EHR systems: Vanderbilt University Medical Center Synthetic Derivative (VUMC), the VA Northeast Ohio Healthcare System (VANEOHS), and Massachusetts General Brigham (MGB). Cases were required to meet 1 of the following 3 criteria: (1) 2 or more dates with any DR ICD-9/10 code documented in the EHR, (2) at least one affirmative health-factor or EPIC code for DR along with an ICD9/10 code for DR on a different day, or (3) at least one ICD-9/10 code for any DR occurring within 24 hours of an ophthalmology examination. Criteria for controls included affirmative evidence for diabetes as well as an ophthalmology examination. Results The algorithms, developed and evaluated in VUMC through manual chart review, resulted in a positive predictive value (PPV) of 0.93 for cases and negative predictive value (NPV) of 0.91 for controls. Implementation of algorithms yielded similar metrics in VANEOHS (PPV = 0.94; NPV = 0.86) and lower in MGB (PPV = 0.84; NPV = 0.76). In comparison, the algorithm for DR implemented in Phenome-wide association study (PheWAS) in VUMC yielded similar PPV (0.92) but substantially reduced NPV (0.48). Implementation of the algorithms to the Million Veteran Program identified over 62 000 DR cases with genetic data including 14 549 African Americans and 6209 Hispanics with DR. Conclusions/Discussion We demonstrate the robustness of the algorithms at 3 separate healthcare centers, with a minimum PPV of 0.84 and substantially improved NPV than existing automated methods. We strongly encourage independent validation and incorporation of features unique to each EHR to enhance algorithm performance for DR cases and controls.
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ISSN:1067-5027
1527-974X
1527-974X
DOI:10.1093/jamia/ocae213