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Related Experiment Videos

Development and Evaluation of a Rules-based Algorithm for Primary Open-Angle Glaucoma in the VA Million Veteran

Cari L Nealon1, Christopher W Halladay2, Tyler G Kinzy3,4,5

  • 1Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, OH, USA.

Ophthalmic Epidemiology
|November 25, 2021
PubMed

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A Primary Open-Angle Glaucoma Locus Near Transcription Factor <i>PRRX1</i> Identified in the Million Veteran Program.

Ophthalmology science·2026
Summary

Researchers developed a computable phenotype using electronic health records to accurately identify primary open-angle glaucoma (POAG) in veterans. This method, validated with genetic data, enables better study of complex eye diseases.

Area of Science:

  • Ophthalmology
  • Genetics
  • Health Informatics

Background:

  • Electronic health record (EHR)-linked biobank data offers research potential for complex ocular diseases.
  • Accurate computable phenotypes for imaging-diagnosed ocular conditions are often unavailable in EHRs.
  • Primary open-angle glaucoma (POAG) diagnosis typically requires imaging, posing a challenge for EHR-based research.

Purpose of the Study:

  • To develop and validate a computable phenotype for identifying POAG using the Department of Veterans Affairs (VA) Computerized Patient Record System (CPRS) and Million Veteran Program (MVP) biobank.
  • To create and refine algorithms for POAG case and control identification based on clinical, prescription, and diagnosis data.
  • To assess the accuracy and reproducibility of the developed algorithms in classifying POAG.
Keywords:
Glaucomaadministrative databasecomputable phenotypemillion veteran programprimary open-angle glaucomavalidation

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Main Methods:

  • Utilized CPRS clinical ophthalmology data from VA Medical Center Eye Clinic (VAMCEC) patients.
  • Developed and iteratively refined POAG algorithms using clinical, prescription, and ICD-CM codes.
  • Validated algorithms through chart review at multiple VAMCECs and assessed predictive values (PPV, NPV) with expert clinical diagnosis data.

Main Results:

  • The final algorithms demonstrated high accuracy and reproducibility for POAG classification, with positive predictive values (PPV) greater than 83% and negative predictive values (NPV) greater than 97%.
  • Algorithms performed consistently across different racial groups, including Black or African American and White Veterans.
  • Application to the MVP biobank and subsequent genetic analysis of a known POAG locus further validated the algorithms' effectiveness.

Conclusions:

  • The developed computable phenotype using combined EHR and genetic data is a viable approach for studying complex diseases like POAG.
  • This method enhances the accuracy and reproducibility of disease classification in large biobank datasets.
  • The approach facilitates research into complex ocular diseases by overcoming limitations in EHR data accessibility for imaging-dependent diagnoses.