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Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models

PURPOSE: Develop a hierarchical longitudinal regression model for estimating local rates of change of macular ganglion cell complex (GCC) measurements with optical coherence tomography (OCT). METHODS: We enrolled 112 eyes with four or more macular OCT images and ≥2 years of follow-up. GCC thickness...

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Autores principales: Mohammadzadeh, Vahid, Su, Erica, Heydar Zadeh, Sepideh, Law, Simon K., Coleman, Anne L., Caprioli, Joseph, Weiss, Robert E., Nouri-Mahdavi, Kouros
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8054624/
https://www.ncbi.nlm.nih.gov/pubmed/34003991
http://dx.doi.org/10.1167/tvst.10.4.15
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author Mohammadzadeh, Vahid
Su, Erica
Heydar Zadeh, Sepideh
Law, Simon K.
Coleman, Anne L.
Caprioli, Joseph
Weiss, Robert E.
Nouri-Mahdavi, Kouros
author_facet Mohammadzadeh, Vahid
Su, Erica
Heydar Zadeh, Sepideh
Law, Simon K.
Coleman, Anne L.
Caprioli, Joseph
Weiss, Robert E.
Nouri-Mahdavi, Kouros
author_sort Mohammadzadeh, Vahid
collection PubMed
description PURPOSE: Develop a hierarchical longitudinal regression model for estimating local rates of change of macular ganglion cell complex (GCC) measurements with optical coherence tomography (OCT). METHODS: We enrolled 112 eyes with four or more macular OCT images and ≥2 years of follow-up. GCC thickness measurements within central 6 × 6 superpixels were extracted from macular volume scans. We fit data from each superpixel separately with several hierarchical Bayesian random-effects models. Models were compared with the Watanabe–Akaike information criterion. For our preferred model, we estimated population and individual slopes and intercepts (baseline thickness) and their correlation. RESULTS: Mean (SD) follow-up time and median (interquartile range) baseline 24-2 visual field mean deviation were 3.6 (0.4) years and −6.8 (−12.2 to −4.3) dB, respectively. The random intercepts and slopes model with random residual variance was the preferred model. While more individual and population negative slopes were observed in the paracentral and papillomacular superpixels, superpixels in the superotemporal and inferior regions displayed the highest correlation between baseline thickness and rates of change (r = –0.43 to –0.50 for the top five correlations). CONCLUSIONS: A Bayesian linear hierarchical model with random intercepts/slopes and random variances is an optimal initial model for estimating GCC slopes at population and individual levels. This novel model is an efficient method for estimating macular rates of change and probability of glaucoma progression locally. TRANSLATIONAL RELEVANCE: The proposed Bayesian hierarchical model can be applied to various macular outcomes from different OCT devices and to superpixels of variable sizes to estimate local rates of change and progression probability.
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spelling pubmed-80546242021-05-05 Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models Mohammadzadeh, Vahid Su, Erica Heydar Zadeh, Sepideh Law, Simon K. Coleman, Anne L. Caprioli, Joseph Weiss, Robert E. Nouri-Mahdavi, Kouros Transl Vis Sci Technol Article PURPOSE: Develop a hierarchical longitudinal regression model for estimating local rates of change of macular ganglion cell complex (GCC) measurements with optical coherence tomography (OCT). METHODS: We enrolled 112 eyes with four or more macular OCT images and ≥2 years of follow-up. GCC thickness measurements within central 6 × 6 superpixels were extracted from macular volume scans. We fit data from each superpixel separately with several hierarchical Bayesian random-effects models. Models were compared with the Watanabe–Akaike information criterion. For our preferred model, we estimated population and individual slopes and intercepts (baseline thickness) and their correlation. RESULTS: Mean (SD) follow-up time and median (interquartile range) baseline 24-2 visual field mean deviation were 3.6 (0.4) years and −6.8 (−12.2 to −4.3) dB, respectively. The random intercepts and slopes model with random residual variance was the preferred model. While more individual and population negative slopes were observed in the paracentral and papillomacular superpixels, superpixels in the superotemporal and inferior regions displayed the highest correlation between baseline thickness and rates of change (r = –0.43 to –0.50 for the top five correlations). CONCLUSIONS: A Bayesian linear hierarchical model with random intercepts/slopes and random variances is an optimal initial model for estimating GCC slopes at population and individual levels. This novel model is an efficient method for estimating macular rates of change and probability of glaucoma progression locally. TRANSLATIONAL RELEVANCE: The proposed Bayesian hierarchical model can be applied to various macular outcomes from different OCT devices and to superpixels of variable sizes to estimate local rates of change and progression probability. The Association for Research in Vision and Ophthalmology 2021-04-15 /pmc/articles/PMC8054624/ /pubmed/34003991 http://dx.doi.org/10.1167/tvst.10.4.15 Text en Copyright 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
spellingShingle Article
Mohammadzadeh, Vahid
Su, Erica
Heydar Zadeh, Sepideh
Law, Simon K.
Coleman, Anne L.
Caprioli, Joseph
Weiss, Robert E.
Nouri-Mahdavi, Kouros
Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models
title Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models
title_full Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models
title_fullStr Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models
title_full_unstemmed Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models
title_short Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models
title_sort estimating ganglion cell complex rates of change with bayesian hierarchical models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8054624/
https://www.ncbi.nlm.nih.gov/pubmed/34003991
http://dx.doi.org/10.1167/tvst.10.4.15
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