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

ABSTRACT

The availability of electronic health record (EHR)-linked biobank data for research presents opportunities to better understand complex ocular diseases. Developing accurate computable phenotypes for ocular diseases for which gold standard diagnosis includes imaging remains inaccessible in most biobank-linked EHRs. The objective of this study was to develop and validate a computable phenotype to identify primary open-angle glaucoma (POAG) through accessing the Department of Veterans Affairs (VA) Computerized Patient Record System (CPRS) and Million Veteran Program (MVP) biobank. Accessing CPRS clinical ophthalmology data from VA Medical Center Eye Clinic (VAMCEC) patients, we developed and iteratively refined POAG case and control algorithms based on clinical, prescription, and structured diagnosis data (ICD-CM codes). Refinement was performed via detailed chart review, initially at a single VAMCEC (n = 200) and validated at two additional VAMCECs (n = 100 each). Positive and negative predictive values (PPV, NPV) were computed as the proportion of CPRS patients correctly classified with POAG or without POAG, respectively, by the algorithms, validated by ophthalmologists and optometrists with access to gold-standard clinical diagnosis data. The final algorithms performed better than previously reported approaches in assuring the accuracy and reproducibility of POAG classification (PPV >83% and NPV >97%) with consistent performance in Black or African American and in White Veterans. Applied to the MVP to identify cases and controls, genetic analysis of a known POAG-associated locus further validated the algorithms. We conclude that ours is a viable approach to use combined EHR-genetic data to study patients with complex diseases that require imaging confirmation.

KEYWORDS:

• Glaucoma
• administrative database
• validation
• primary open-angle glaucoma
• computable phenotype
• million veteran program

Acknowledgments

This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by a VA Merit Review (I01 BX003364), the Cleveland Institute for Computational Biology, NIH Core Grants (P30 EY025585, P30 EY011373), the Clinical and Translational Science Collaborative of Cleveland (UL1TR002548) from the National Center for Advancing Translational Sciences (NCATS) component of the NIH and NIH Roadmap for Medical Research, and unrestricted grants from Research to Prevent Blindness to Case Western Reserve University, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, and the University of Buffalo. This publication is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This publication does not represent the views of the Department of Veterans Affairs or the United States Government. We are grateful to the VINCI and GENISIS support teams, and to the MVP Core Statistical Analysis team. We are especially grateful to the Veterans who contributed to the MVP. This work was also supported by the Harper-Inglis Memorial for Eye Research, The Peierls Foundation, and That Man May See.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

Full summary statistics relating to the Million Veteran Program (MVP) are publically available and may be accessed here with the accession code phs001672.v1.p1: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001672.v1.p1

Supplementary Material

Supplemental data for this article can be accessed on the publisher’s website.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.