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

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journal contribution
posted on 25.11.2021, 17:20 by Cari L. Nealon, Christopher W. Halladay, Tyler G. Kinzy, Piana Simpson, Rachael L. Canania, Scott A. Anthony, David P. Roncone, Lea R. Sawicki Rogers, Jenna N. Leber, Jacquelyn M. Dougherty, Jack M. Sullivan, Wen-Chih Wu, Paul B. Greenberg, Sudha K. Iyengar, Dana C. Crawford, Neal S. Peachey, Jessica N. Cooke Bailey

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.


This work was supported by the Harper-Inglis Memorial for Eye Research, The Peierls Foundation, That Man May See, and Research to Prevent Blindness; National Institutes of Health [P30 EY011373, P30 EY025585, UL1TR002548]; Cleveland Institute for Computational Biology; U.S. Department of Veterans Affairs [I01 BX003364].