Enhanced Phenotype Identification of Common Ocular Diseases in Real-World Datasets.

Journal: Ophthalmology science
Published Date: Jan 24, 2025

Abstract

OBJECTIVE: For studies using real-world data, accurately identifying patients with phenotypes of interest is challenging. To identify cohorts of interest, most studies exclusively use the International Classification of Diseases (ICD) billing codes, which can be limiting. We developed a method to accurately identify the presence or absence of 3 common ocular diseases (diabetic retinopathy [DR], age-related macular degeneration [AMD], and glaucoma) using electronic health record (EHR) data.

Authors

  • Joshua D Stein
    Department of Ophthalmology & Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, Michigan.
  • Hong Su An
    Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan.
  • Chris A Andrews
    From the W.K. Kellogg Eye Center, Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA (J.D.S., Y.Z., C.A.A., J.B.).
  • Suzann Pershing
    Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
  • Tushar Mungle
    School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, India.
  • Amanda K Bicket
    Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan.
  • Julie M Rosenthal
    Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan.
  • Amy D Zhang
    Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan.
  • Wen-Shin Lee
    Department of Ophthalmology and Visual Sciences, Byers Eye Institute, Stanford University, Palo Alto, California.
  • Cassie Ludwig
    Department of Ophthalmology, Byers Eye Institute, Stanford University, Stanford, California.
  • Bethlehem Mekonnen
    Department of Ophthalmology and Visual Sciences, Byers Eye Institute, Stanford University, Palo Alto, California.
  • Tina Hernandez-Boussard
    Stanford Center for Biomedical Informatics Research, Stanford, California 94305, USA.

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