Huseyin Simavli1, Christian John Que1, Mustafa Akduman1, Jennifer L Rizzo1, Edem Tsikata1, Johannes F de Boer2, Teresa C Chen3. 1. Department of Ophthalmology, Harvard Medical School, Glaucoma Service, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts. 2. Department of Physics and Astronomy, Vrije Universiteit, Amsterdam, Netherlands. 3. Department of Ophthalmology, Harvard Medical School, Glaucoma Service, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts. Electronic address: teresa_chen@meei.harvard.edu.
Abstract
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.
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 glaucomapatients and for a subset of early glaucomapatients 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 glaucomapatients. Peripapillary RT values have the same or better diagnostic capability compared to peripapillary RNFL thickness measurements, while also having fewer algorithm errors.
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