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Combining Information From 3 Anatomic Regions in the Diagnosis of Glaucoma With Time-Domain Optical Coherence Tomography

Wang, Mingwu MD, PhD*; Lu, Ake Tzu-Hui PhD; Varma, Rohit MD, MPH; Schuman, Joel S. MD; Greenfield, David S. MD§; Huang, David MD, PhDAdvanced Imaging for Glaucoma Study Group

doi: 10.1097/IJG.0b013e318264b941
Original Studies

Purpose: To improve the diagnosis of glaucoma by combining time-domain optical coherence tomography (TD-OCT) measurements of the optic disc, circumpapillary retinal nerve fiber layer (RNFL), and macular retinal thickness.

Patients and Methods: Ninety-six age-matched normal and 96 perimetric glaucoma participants were included in this observational, cross-sectional study. Or-logic, support vector machine, relevance vector machine, and linear discrimination function were used to analyze the performances of combined TD-OCT diagnostic variables.

Results: The area under the receiver-operating curve (AROC) was used to evaluate the diagnostic accuracy and to compare the diagnostic performance of single and combined anatomic variables. The best RNFL thickness variables were the inferior (AROC=0.900), overall (AROC=0.892), and superior quadrants (AROC=0.850). The best optic disc variables were horizontal integrated rim width (AROC=0.909), vertical integrated rim area (AROC=0.908), and cup/disc vertical ratio (AROC=0.890). All macular retinal thickness variables had AROCs of 0.829 or less. Combining the top 3 RNFL and optic disc variables in optimizing glaucoma diagnosis, support vector machine had the highest AROC, 0.954, followed by or-logic (AROC=0.946), linear discrimination function (AROC=0.946), and relevance vector machine (AROC=0.943). All combination diagnostic variables had significantly larger AROCs than any single diagnostic variable. There are no significant differences among the combination diagnostic indices.

Conclusions: With TD-OCT, RNFL and optic disc variables had better diagnostic accuracy than macular retinal variables. Combining top RNFL and optic disc variables significantly improved diagnostic performance. Clinically, or-logic classification was the most practical analytical tool with sufficient accuracy to diagnose early glaucoma.

*Department of Ophthalmology and Vision Science, University of Arizona College of Medicine, Tucson, AZ

Doheny Eye Institute and the Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA

University of Pittsburgh Medical Center, Pittsburgh, PA

§Bascom Palmer Eye Institute, University of Miami, Miami, FL

Casey Eye Institute, Oregon Health & Science University, Portland, OR

For the Advanced Imaging for Glaucoma Study Group, see http://http://www.AIGStudy.net.

Disclosure: Supported by NIH grants R01 EY013516 and P30 EY03040 and by a grant from Research to Prevent Blindness. D.H. and J.S.S. receive royalties from the Massachusetts Institute of Technology derived from an optical coherence tomography patent licensed to Carl Zeiss Meditec Inc. D.H. has a significant financial interest in Optovue Inc., a company that may have a commercial interest in the results of this research and technology. This potential individual conflict of interest has been reviewed and managed by Oregon Health & Science University. Other authors do not have financial interest in the subject of this article.

Reprints: David Huang, MD, PhD, Casey Eye Institute, Oregon Health & Science University, 3375S.W. Terwilliger Blvd., Portland, OR 97239-4197 (e-mail: davidhuang@alum.mit.edu).

Received December 15, 2011

Accepted May 29, 2012

© 2014 by Lippincott Williams & Wilkins.