4.6 Article

Diagnostic Capability of Peripapillary Retinal Thickness in Glaucoma Using 3D Volume Scans

Journal

AMERICAN JOURNAL OF OPHTHALMOLOGY
Volume 159, Issue 3, Pages 545-556

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.ajo.2014.12.004

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Funding

  1. National Institutes of Health [UL 1RR 025758]

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PURPOSE: To determine the diagnostic capability of spectral-domain optical coherence tomography (SD OCT) peripapillary retinal thickness (RT) measurements from 3-dimensional (3D) volume scans for primary open-angle glaucoma (POAG). DESIGN: Cross-sectional study. METHODS: SETTING: Institutional. STUDY POPULATION: 156 patients (89 POAG and 67 normal subjects). OBSERVATION PROCEDURES: One eye of each subject was included. SD OCT peripapillary RT values from 3D volume scans were calculated for 4 quadrants of 3 different sized annuli. Peripapillary retinal nerve fiber layer (RNFL) thickness values were also determined. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (AUROC) values, sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios. RESULTS: The top 5 RT AUROCs for all glaucoma patients and for a subset of early glaucoma patients were for the inferior quadrant of outer circumpapillary annulus of circular grid (OCA) 1 (0.959, 0.939), inferior quadrant of OCA2 (0.945, 0.921), superior quadrant of OCA1 (0.890, 0.811), inferior quadrant of OCA3 (0.887, 0.854), and superior quadrant of OCA2 (0.879, 0.807). Smaller RT annuli OCA1 and OCA2 consistently showed better diagnostic performance than the larger RT annulus OCA3. For both RNFL and RT measurements, best AUROC values were found for inferior RT OCA1 and OCA2, followed by inferior and overall RNFL thickness. CONCLUSION: Peripapillary RT measurements from 3D volume scans showed excellent diagnostic performance for detecting both glaucoma and early glaucoma patients. Peripapillary RT values have the same or better diagnostic capability compared to peripapillary RNFL thickness measurements, while also having fewer algorithm errors. (C) 2015 by Elsevier Inc. All rights reserved.

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