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Glaucomatous Maculopathy: Thickness Differences on Inner and Outer Macular Layers between Ocular Hypertension and Early Primary Open-Angle Glaucoma Using 8 × 8 Posterior Pole Algorithm of SD-OCT
Open AccessArticle

Clinical-Evolutionary Staging System of Primary Open-Angle Glaucoma Using Optical Coherence Tomography

1
Department of Ophthalmology, Policlínica Baza, 18800 Baza (Granada), Spain
2
CEINDO, San Pablo CEU University, 28668 Madrid, Spain
3
Department of Statistics and Applied Maths, San Pablo CEU University, 28668 Madrid, Spain
4
Department of Ophthalmology, General University Hospital Morales Meseguer, 30100 Murcia, Spain
5
Department of Ophthalmology and Optometry, University of Murcia, 30100 Murcia, Spain
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(5), 1530; https://doi.org/10.3390/jcm9051530
Received: 11 March 2020 / Revised: 30 April 2020 / Accepted: 13 May 2020 / Published: 19 May 2020
(This article belongs to the Special Issue Update on Glaucoma: Pathogenesis and Treatment)
Background: Primary open-angle glaucoma (POAG) is considered one of the main causes of blindness. Detection of POAG at early stages and classification into evolutionary stages is crucial to blindness prevention. Methods: 1001 patients were enrolled, of whom 766 were healthy subjects and 235 were ocular hypertensive or glaucomatous patients in different stages of the disease. Spectral domain optical coherence tomography (SD-OCT) was used to determine Bruch’s membrane opening-minimum rim width (BMO-MRW) and the thicknesses of peripapillary retinal nerve fibre layer (RNFL) rings with diameters of 3.0, 4.1 and 4.7 mm centred on the optic nerve. The BMO-MRW rim and RNFL rings were divided into seven sectors (G-T-TS-TI-N-NS-NI). The k-means algorithm and linear discriminant analysis were used to classify patients into disease stages. Results: We defined four glaucoma stages and provided a new model for classifying eyes into these stages, with an overall accuracy greater than 92% (88% when including healthy eyes). An online application was also implemented to predict the probability of glaucoma stage for any given eye. Conclusions: We propose a new objective algorithm for classifying POAG into clinical-evolutionary stages using SD-OCT. View Full-Text
Keywords: primary open-angle glaucoma; retinal disease; spectral domain-optical coherence tomography; retinal imaging; evolutionary stages primary open-angle glaucoma; retinal disease; spectral domain-optical coherence tomography; retinal imaging; evolutionary stages
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MDPI and ACS Style

Parra-Blesa, A.; Sanchez-Alberca, A.; Garcia-Medina, J.J. Clinical-Evolutionary Staging System of Primary Open-Angle Glaucoma Using Optical Coherence Tomography. J. Clin. Med. 2020, 9, 1530.

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