Purchase this article with an account.
Ryo Asaoka, Aiko Iwase, Kazunori Hirasawa, Hiroshi Murata, Makoto Araie; Identifying “Preperimetric” Glaucoma in Standard Automated Perimetry Visual Fields. Invest. Ophthalmol. Vis. Sci. 2014;55(12):7814-7820. doi: https://doi.org/10.1167/iovs.14-15120.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
To compare the visual fields (VFs) of preperimetric open angle glaucoma (OAG) patients (preperimetric glaucoma VFs, PPGVFs) with the VFs of healthy eyes, and to discriminate these two groups by using the Random Forests machine-learning method.
All VFs before a first diagnosis of manifest glaucoma (Anderson-Patella's criteria) were classified as PPGVFs. Series of VFs were obtained with the Humphrey Field Analyzer 30-2 program from 171 PPGVFs from 53 eyes in 51 OAG or OAG suspect patients and 108 healthy eyes of 87 normal subjects. The area under the receiver operating characteristic curve (AROC) in discriminating between PPGVFs and healthy VFs was calculated by using the Random Forests method, with 52 total deviation (TD) values, mean deviation (MD), and pattern standard deviation (PSD) as predictors.
There was a significant difference in MD between healthy VFs and PPGVFs (−0.03 ± 1.11 and −0.91 ± 1.56 dB [mean ± standard deviation], respectively; P < 0.001, linear mixed model) and in PSD (1.56 ± 0.33 and 1.97 ± 0.43 dB, respectively; P < 0.001). A significant difference was observed in the TD values between healthy VFs and PPGVFs at 25 (P < 0.001) of 52 test points (linear mixed model). The AROC obtained by using the Random Forests method was 79.0% (95% confidence interval, 73.5%–84.5%).
Differences exist between healthy VFs and VFs of preperimetric glaucoma eyes, which go on to develop manifest glaucoma; these two groups of VFs could be well distinguished by using the Random Forests classifier.
This PDF is available to Subscribers Only