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Multivariable Logistic Regression Model: A Novel Mathematical Model that Predicts Visual Field Sensitivity from Macular Ganglion Cell Complex Thickness in Glaucoma

PURPOSE: To design a mathematical model that can predict the relationship between the ganglion cell complex (GCC) thickness and visual field sensitivity (VFS) in glaucoma patients. DESIGN: Retrospective cross-sectional case series. METHOD: Within 3 months from VFS measurements by the Humphrey field...

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Autores principales: Shiba, Daisuke, Hatou, Shin, Ono, Takeshi, Hosoda, Shingo, Tanabe, Sachiko, Ozeki, Naoki, Yuki, Kenya, Shimoyama, Masaru, Fukagawa, Kazumi, Shimmura, Shigeto, Tsubota, Kazuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4136731/
https://www.ncbi.nlm.nih.gov/pubmed/25133512
http://dx.doi.org/10.1371/journal.pone.0104126
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author Shiba, Daisuke
Hatou, Shin
Ono, Takeshi
Hosoda, Shingo
Tanabe, Sachiko
Ozeki, Naoki
Yuki, Kenya
Shimoyama, Masaru
Fukagawa, Kazumi
Shimmura, Shigeto
Tsubota, Kazuo
author_facet Shiba, Daisuke
Hatou, Shin
Ono, Takeshi
Hosoda, Shingo
Tanabe, Sachiko
Ozeki, Naoki
Yuki, Kenya
Shimoyama, Masaru
Fukagawa, Kazumi
Shimmura, Shigeto
Tsubota, Kazuo
author_sort Shiba, Daisuke
collection PubMed
description PURPOSE: To design a mathematical model that can predict the relationship between the ganglion cell complex (GCC) thickness and visual field sensitivity (VFS) in glaucoma patients. DESIGN: Retrospective cross-sectional case series. METHOD: Within 3 months from VFS measurements by the Humphrey field analyzer 10-2 program, 83 eyes underwent macular GCC thickness measurements by spectral-domain optical coherence tomography (SD-OCT). Data were used to construct a multiple logistic model that depicted the relationship between the explanatory variables (GCC thickness, age, sex, and spherical equivalent of refractive errors) determined by a regression analysis and the mean VFS corresponding to the SD-OCT scanned area. Analyses were performed in half or 8 segmented local areas as well as in whole scanned areas. A simple logistic model that included GCC thickness as the single explanatory variable was also constructed. The ability of the logistic models to depict the real GCC thickness/VFS in SAP distribution was analyzed by the χ(2) test of goodness-of-fit. The significance of the model effect was analyzed by analysis of variance (ANOVA). RESULTS: Scatter plots between the GCC thickness and the mean VFS showed sigmoid curves. The χ(2) test of goodness-of-fit revealed that the multiple logistic models showed a good fit for the real GCC thickness/VFS distribution in all areas except the nasal-inferior-outer area. ANOVA revealed that all of the multiple logistic models significantly predicted the VFS based on the explanatory variables. Although simple logistic models also exhibited significant VFS predictability based on the GCC thickness, the model effect was less than that observed for the multiple logistic models. CONCLUSIONS: The currently proposed logistic models are useful methods for depicting relationships between the explanatory variables, including the GCC thickness, and the mean VFS in glaucoma patients.
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spelling pubmed-41367312014-08-20 Multivariable Logistic Regression Model: A Novel Mathematical Model that Predicts Visual Field Sensitivity from Macular Ganglion Cell Complex Thickness in Glaucoma Shiba, Daisuke Hatou, Shin Ono, Takeshi Hosoda, Shingo Tanabe, Sachiko Ozeki, Naoki Yuki, Kenya Shimoyama, Masaru Fukagawa, Kazumi Shimmura, Shigeto Tsubota, Kazuo PLoS One Research Article PURPOSE: To design a mathematical model that can predict the relationship between the ganglion cell complex (GCC) thickness and visual field sensitivity (VFS) in glaucoma patients. DESIGN: Retrospective cross-sectional case series. METHOD: Within 3 months from VFS measurements by the Humphrey field analyzer 10-2 program, 83 eyes underwent macular GCC thickness measurements by spectral-domain optical coherence tomography (SD-OCT). Data were used to construct a multiple logistic model that depicted the relationship between the explanatory variables (GCC thickness, age, sex, and spherical equivalent of refractive errors) determined by a regression analysis and the mean VFS corresponding to the SD-OCT scanned area. Analyses were performed in half or 8 segmented local areas as well as in whole scanned areas. A simple logistic model that included GCC thickness as the single explanatory variable was also constructed. The ability of the logistic models to depict the real GCC thickness/VFS in SAP distribution was analyzed by the χ(2) test of goodness-of-fit. The significance of the model effect was analyzed by analysis of variance (ANOVA). RESULTS: Scatter plots between the GCC thickness and the mean VFS showed sigmoid curves. The χ(2) test of goodness-of-fit revealed that the multiple logistic models showed a good fit for the real GCC thickness/VFS distribution in all areas except the nasal-inferior-outer area. ANOVA revealed that all of the multiple logistic models significantly predicted the VFS based on the explanatory variables. Although simple logistic models also exhibited significant VFS predictability based on the GCC thickness, the model effect was less than that observed for the multiple logistic models. CONCLUSIONS: The currently proposed logistic models are useful methods for depicting relationships between the explanatory variables, including the GCC thickness, and the mean VFS in glaucoma patients. Public Library of Science 2014-08-18 /pmc/articles/PMC4136731/ /pubmed/25133512 http://dx.doi.org/10.1371/journal.pone.0104126 Text en © 2014 Shiba et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Shiba, Daisuke
Hatou, Shin
Ono, Takeshi
Hosoda, Shingo
Tanabe, Sachiko
Ozeki, Naoki
Yuki, Kenya
Shimoyama, Masaru
Fukagawa, Kazumi
Shimmura, Shigeto
Tsubota, Kazuo
Multivariable Logistic Regression Model: A Novel Mathematical Model that Predicts Visual Field Sensitivity from Macular Ganglion Cell Complex Thickness in Glaucoma
title Multivariable Logistic Regression Model: A Novel Mathematical Model that Predicts Visual Field Sensitivity from Macular Ganglion Cell Complex Thickness in Glaucoma
title_full Multivariable Logistic Regression Model: A Novel Mathematical Model that Predicts Visual Field Sensitivity from Macular Ganglion Cell Complex Thickness in Glaucoma
title_fullStr Multivariable Logistic Regression Model: A Novel Mathematical Model that Predicts Visual Field Sensitivity from Macular Ganglion Cell Complex Thickness in Glaucoma
title_full_unstemmed Multivariable Logistic Regression Model: A Novel Mathematical Model that Predicts Visual Field Sensitivity from Macular Ganglion Cell Complex Thickness in Glaucoma
title_short Multivariable Logistic Regression Model: A Novel Mathematical Model that Predicts Visual Field Sensitivity from Macular Ganglion Cell Complex Thickness in Glaucoma
title_sort multivariable logistic regression model: a novel mathematical model that predicts visual field sensitivity from macular ganglion cell complex thickness in glaucoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4136731/
https://www.ncbi.nlm.nih.gov/pubmed/25133512
http://dx.doi.org/10.1371/journal.pone.0104126
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