Development and validation of an associative model for the detection of glaucoma using pupillography

Purpose To develop and validate an associative model using pupillography that best discriminates those with and without glaucoma. Design A prospective case-control study. Methods We enrolled 148 patients with glaucoma (mean age 67 ± 11) and 71 controls (mean age 60 ± 10) in a clinical setting. This...

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Main Authors: Dolly S. Chang, Karun S. Arora, Michael V. Boland, Wasu Supakontanasan, David S. Friedman
Other Authors: The Wilmer Eye Institute at Johns Hopkins
Format: Article
Published: 2018
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/32064
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spelling th-mahidol.320642018-10-19T12:11:22Z Development and validation of an associative model for the detection of glaucoma using pupillography Dolly S. Chang Karun S. Arora Michael V. Boland Wasu Supakontanasan David S. Friedman The Wilmer Eye Institute at Johns Hopkins Johns Hopkins Bloomberg School of Public Health The Johns Hopkins School of Medicine Mahidol University Medicine Purpose To develop and validate an associative model using pupillography that best discriminates those with and without glaucoma. Design A prospective case-control study. Methods We enrolled 148 patients with glaucoma (mean age 67 ± 11) and 71 controls (mean age 60 ± 10) in a clinical setting. This prototype pupillometer is designed to record and analyze pupillary responses at multiple, controlled stimulus intensities while using varied stimulus patterns and colors. We evaluated three approaches: (1) comparing the responses between the two eyes; (2) comparing responses to stimuli between the superonasal and inferonasal fields within each eye; and (3) calculating the absolute pupil response of each individual eye. Associative models were developed using stepwise regression or forward selection with Akaike information criterion and validated by fivefold cross-validation. We assessed the associative model using sensitivity, specificity and the area-under-the-receiver operating characteristic curve. Results Persons with glaucoma had more asymmetric pupil responses in the two eyes (P < 0.001); between superonasal and inferonasal visual field within the same eye (P = 0.014); and smaller amplitudes, slower velocities and longer latencies of pupil responses compared to controls (all P < 0.001). A model including age and these three components resulted in an area-under-the-receiver operating characteristic curve of 0.87 (95% CI 0.83 to 0.92) with 80% sensitivity and specificity in detecting glaucoma. This result remained robust after cross-validation. Conclusions Using pupillography, we were able to discriminate among persons with glaucoma and those with normal eye examinations. With refinement, pupil testing may provide a simple approach for glaucoma screening. © 2013 BY ELSEVIER INC. ALL RIGHTS RESERVED. 2018-10-19T05:11:22Z 2018-10-19T05:11:22Z 2013-12-01 Article American Journal of Ophthalmology. Vol.156, No.6 (2013) 10.1016/j.ajo.2013.07.026 18791891 00029394 2-s2.0-84887883389 https://repository.li.mahidol.ac.th/handle/123456789/32064 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84887883389&origin=inward
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Medicine
spellingShingle Medicine
Dolly S. Chang
Karun S. Arora
Michael V. Boland
Wasu Supakontanasan
David S. Friedman
Development and validation of an associative model for the detection of glaucoma using pupillography
description Purpose To develop and validate an associative model using pupillography that best discriminates those with and without glaucoma. Design A prospective case-control study. Methods We enrolled 148 patients with glaucoma (mean age 67 ± 11) and 71 controls (mean age 60 ± 10) in a clinical setting. This prototype pupillometer is designed to record and analyze pupillary responses at multiple, controlled stimulus intensities while using varied stimulus patterns and colors. We evaluated three approaches: (1) comparing the responses between the two eyes; (2) comparing responses to stimuli between the superonasal and inferonasal fields within each eye; and (3) calculating the absolute pupil response of each individual eye. Associative models were developed using stepwise regression or forward selection with Akaike information criterion and validated by fivefold cross-validation. We assessed the associative model using sensitivity, specificity and the area-under-the-receiver operating characteristic curve. Results Persons with glaucoma had more asymmetric pupil responses in the two eyes (P < 0.001); between superonasal and inferonasal visual field within the same eye (P = 0.014); and smaller amplitudes, slower velocities and longer latencies of pupil responses compared to controls (all P < 0.001). A model including age and these three components resulted in an area-under-the-receiver operating characteristic curve of 0.87 (95% CI 0.83 to 0.92) with 80% sensitivity and specificity in detecting glaucoma. This result remained robust after cross-validation. Conclusions Using pupillography, we were able to discriminate among persons with glaucoma and those with normal eye examinations. With refinement, pupil testing may provide a simple approach for glaucoma screening. © 2013 BY ELSEVIER INC. ALL RIGHTS RESERVED.
author2 The Wilmer Eye Institute at Johns Hopkins
author_facet The Wilmer Eye Institute at Johns Hopkins
Dolly S. Chang
Karun S. Arora
Michael V. Boland
Wasu Supakontanasan
David S. Friedman
format Article
author Dolly S. Chang
Karun S. Arora
Michael V. Boland
Wasu Supakontanasan
David S. Friedman
author_sort Dolly S. Chang
title Development and validation of an associative model for the detection of glaucoma using pupillography
title_short Development and validation of an associative model for the detection of glaucoma using pupillography
title_full Development and validation of an associative model for the detection of glaucoma using pupillography
title_fullStr Development and validation of an associative model for the detection of glaucoma using pupillography
title_full_unstemmed Development and validation of an associative model for the detection of glaucoma using pupillography
title_sort development and validation of an associative model for the detection of glaucoma using pupillography
publishDate 2018
url https://repository.li.mahidol.ac.th/handle/123456789/32064
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