Kazunori Hirasawa1, Hiroshi Murata2, Hiroyo Hirasawa2, Chihiro Mayama2, Ryo Asaoka2. 1. Department of Ophthalmology, The University of Tokyo, Graduate School of Medicine, Tokyo, Japan Orthopic and Visual Science, Department of Rehabilitation, School of Allied Health Sciences, Kitasato University, Kanagawa, Japan. 2. Department of Ophthalmology, The University of Tokyo, Graduate School of Medicine, Tokyo, Japan.
Abstract
PURPOSE: To develop new visual field (VF) sectors based on pointwise rates of glaucomatous VF progression ("progression regions") and to evaluate their usefulness for predicting future progression. METHODS: A training dataset consisting of 10 VFs from each of 412 eyes in 412 open-angle glaucoma patients and a validation dataset consisting of 15 VFs from each of 71 eyes in 45 patients were investigated. First, using the training dataset, the VF was divided into small regions, according to the rates of progression of all 52 test points in the VF. Then, using the initial four VFs of the validation dataset, total deviation (TD) values in the 10th VF were predicted by applying linear regression analysis in derived regions and the absolute prediction error was calculated. The analysis was iterated, predicting TD values of the 10th VF, but each time including an additional VF in the regression (from five to nine VFs). Absolute prediction errors were then compared with conventional pointwise linear regression (PLR) and regression based on Nouri-Mahdavi (NM) sectors. RESULTS: Twenty-three progression regions were derived. In general, absolute prediction errors were significantly smaller for regression based on these regions compared with PLR and NM sectors. CONCLUSIONS: Predictions of VF progression can be improved by dividing the VF into small regions based on clusters of test points with similar progression rates. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.
PURPOSE: To develop new visual field (VF) sectors based on pointwise rates of glaucomatous VF progression ("progression regions") and to evaluate their usefulness for predicting future progression. METHODS: A training dataset consisting of 10 VFs from each of 412 eyes in 412 open-angle glaucomapatients and a validation dataset consisting of 15 VFs from each of 71 eyes in 45 patients were investigated. First, using the training dataset, the VF was divided into small regions, according to the rates of progression of all 52 test points in the VF. Then, using the initial four VFs of the validation dataset, total deviation (TD) values in the 10th VF were predicted by applying linear regression analysis in derived regions and the absolute prediction error was calculated. The analysis was iterated, predicting TD values of the 10th VF, but each time including an additional VF in the regression (from five to nine VFs). Absolute prediction errors were then compared with conventional pointwise linear regression (PLR) and regression based on Nouri-Mahdavi (NM) sectors. RESULTS: Twenty-three progression regions were derived. In general, absolute prediction errors were significantly smaller for regression based on these regions compared with PLR and NM sectors. CONCLUSIONS: Predictions of VF progression can be improved by dividing the VF into small regions based on clusters of test points with similar progression rates. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.
Entities:
Keywords:
clustering; glaucoma; static perimetry; visual field