Multivariate Normative Comparison, a Novel Method for Improved Use of Retinal Nerve Fiber Layer Thickness to Detect Early Glaucoma.

Autor:	Chua J; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Republic of Singapore; SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore, Republic of Singapore., Schwarzhans F; Center for Medical Statistics Informatics and Intelligent Systems, Section for Medical Information Management, Medical University Vienna, Vienna, Austria., Wong D; SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore, Republic of Singapore; School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore, Republic of Singapore., Li C; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore, Republic of Singapore., Husain R; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Republic of Singapore., Crowston JG; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Republic of Singapore., Perera SA; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Republic of Singapore., Sng CCA; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Republic of Singapore., Nongpiur ME; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Republic of Singapore., Majithia S; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore., Tham YC; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Republic of Singapore., Thakur S; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore., Da Soh Z; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore., Cheng CY; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Republic of Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Republic of Singapore., Aung T; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Republic of Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Republic of Singapore., Fischer G; Center for Medical Statistics Informatics and Intelligent Systems, Section for Medical Information Management, Medical University Vienna, Vienna, Austria., Vass C; Department of Ophthalmology and Optometry, Medical University Vienna, Vienna, Austria., Schmetterer L; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Republic of Singapore; SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore, Republic of Singapore; School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore, Republic of Singapore; Department of Clinical Pharmacology, Medical University Vienna, Vienna, Austria; Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland. Electronic address: leopold.schmetterer@seri.com.sg.
Jazyk:	angličtina
Zdroj:	Ophthalmology. Glaucoma [Ophthalmol Glaucoma] 2022 May-Jun; Vol. 5 (3), pp. 359-368. Date of Electronic Publication: 2021 Oct 27.
DOI:	10.1016/j.ogla.2021.10.013
Abstrakt:	Purpose: Detection of early glaucoma remains limited with the conventional analysis of the retinal nerve fiber layer (RNFL). This study assessed whether compensating the RNFL thickness for multiple demographic and anatomic factors improves the detection of glaucoma. Design: Cross-sectional study. Participants: Three hundred eighty-seven patients with glaucoma and 2699 healthy participants. Methods: Two thousand six hundred ninety-nine healthy participants were enrolled to construct and test a multivariate compensation model, which then was applied in 387 healthy participants and 387 patients with glaucoma (early glaucoma, n = 219; moderate glaucoma, n = 97; and advanced glaucoma, n = 71). Participants underwent Cirrus spectral-domain OCT (Carl Zeiss Meditec) imaging of the optic disc and macular cubes. Compensated RNFL thickness was generated based on ethnicity, age, refractive error, optic disc (ratio, orientation, and area), fovea (distance and angle), and retinal vessel density. The RNFL thickness measurements and their corresponding areas under the receiver operating characteristic curve (AUCs) were obtained. Main Outcome and Measures: Measured and compensated RNFL thickness measurements. Results: After applying the Asian-specific compensation model, the standard deviation of RNFL thickness reduced, where the effect was greatest for Chinese participants (16.9%), followed by Malay participants (13.9%), and Indian participants (12.1%). Multivariate normative comparison outperformed measured RNFL for discrimination of early glaucoma (AUC, 0.90 vs. 0.85; P < 0.001), moderate glaucoma (AUC, 0.94 vs. 0.91; P < 0.001), and advanced glaucoma (AUC, 0.98 vs. 0.96; P < 0.001). Conclusions: The multivariate normative database of RNFL showed better glaucoma discrimination capability than conventional age-matched comparisons, suggesting that accounting for demographic and anatomic variance in RNFL thickness may have usefulness in improving glaucoma detection. (Copyright © 2021 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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