ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2016
Searching in the Dark: Phenotyping Diabetic Retinopathy in a De-Identified Electronic Medical Record Sample of African Americans.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Genetically-guided algorithm development and sample size optimization for age-related macular degeneration cases and controls in electronic health records from the VA Million Veteran Program.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- iT2DMS: a Standard-Based Diabetic Disease Data Repository and its Pilot Experiment on Diabetic Retinopathy Phenotyping and Examination Results Integration.Journal of medical systems · 2018Article
- Enhanced Phenotype Identification of Common Ocular Diseases in Real-World Datasets.Ophthalmology scienceArticle
- Variations in Electronic Health Record-Based Definitions of Diabetic Retinopathy Cohorts: A Literature Review and Quantitative Analysis.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
A hurdle to EMR-based studies is the characterization and extraction of complex phenotypes not readily defined by single diagnostic/procedural codes. Here we developed an algorithm utilizing data mining techniques to identify a diabetic retinopathy (DR) cohort of type-2 diabetic African Americans from the Vanderbilt University de-identified EMR system. The algorithm incorporates a combination of diagnostic codes, current procedural terminology billing codes, medications, and text matching to identify DR when gold-standard digital photography results were unavailable. DR cases were identified with a positive predictive value of 75.3% and an accuracy of 84.8%. Controls were classified with a negative predictive value of 1.0% as could be assessed. Limited studies of DR have been performed in African Americans who are at an elevated risk of DR. Identification of EMR-based African American cohorts may help stimulate new biomedical studies that could elucidate differences in risk for the development of DR and other complex diseases.
Identifiers
27570675PMC5001772W2514714966What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.