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

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.

Design

Database study.

Participants

Three thousand nine hundred fourteen eyes from 1957 patients at 2 Sight OUtcomes Research CollaborativE (SOURCE) Ophthalmology Data Repository sites.

Methods

We developed enhanced phenotype identification (EPI) algorithms that search EHR fie

SUBMITTER: Stein JD 

PROVIDER: S-EPMC11985028 | biostudies-literature | 2025 Jul-Aug

REPOSITORIES: biostudies-literature