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Geographic atrophy phenotype identification by cluster analysis

BACKGROUND/AIMS: To identify ocular phenotypes in patients with geographic atrophy secondary to age-related macular degeneration (GA) using a data-driven cluster analysis. METHODS: This was a retrospective analysis of data from a prospective, natural history study of patients with GA who were follow...

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Autores principales: Monés, Jordi, Biarnés, Marc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5867406/
https://www.ncbi.nlm.nih.gov/pubmed/28729371
http://dx.doi.org/10.1136/bjophthalmol-2017-310268
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author Monés, Jordi
Biarnés, Marc
author_facet Monés, Jordi
Biarnés, Marc
author_sort Monés, Jordi
collection PubMed
description BACKGROUND/AIMS: To identify ocular phenotypes in patients with geographic atrophy secondary to age-related macular degeneration (GA) using a data-driven cluster analysis. METHODS: This was a retrospective analysis of data from a prospective, natural history study of patients with GA who were followed for ≥6 months. Cluster analysis was used to identify subgroups within the population based on the presence of several phenotypic features: soft drusen, reticular pseudodrusen (RPD), primary foveal atrophy, increased fundus autofluorescence (FAF), greyish FAF appearance and subfoveal choroidal thickness (SFCT). A comparison of features between the subgroups was conducted, and a qualitative description of the new phenotypes was proposed. The atrophy growth rate between phenotypes was then compared. RESULTS: Data were analysed from 77 eyes of 77 patients with GA. Cluster analysis identified three groups: phenotype 1 was characterised by high soft drusen load, foveal atrophy and slow growth; phenotype 3 showed high RPD load, extrafoveal and greyish FAF appearance and thin SFCT; the characteristics of phenotype 2 were midway between phenotypes 1 and 3. Phenotypes differed in all measured features (p≤0.013), with decreases in the presence of soft drusen, foveal atrophy and SFCT seen from phenotypes 1 to 3 and corresponding increases in high RPD load, high FAF and greyish FAF appearance. Atrophy growth rate differed between phenotypes 1, 2 and 3 (0.63, 1.91 and 1.73 mm(2)/year, respectively, p=0.0005). CONCLUSION: Cluster analysis identified three distinct phenotypes in GA. One of them showed a particularly slow growth pattern.
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spelling pubmed-58674062018-03-27 Geographic atrophy phenotype identification by cluster analysis Monés, Jordi Biarnés, Marc Br J Ophthalmol Clinical Science BACKGROUND/AIMS: To identify ocular phenotypes in patients with geographic atrophy secondary to age-related macular degeneration (GA) using a data-driven cluster analysis. METHODS: This was a retrospective analysis of data from a prospective, natural history study of patients with GA who were followed for ≥6 months. Cluster analysis was used to identify subgroups within the population based on the presence of several phenotypic features: soft drusen, reticular pseudodrusen (RPD), primary foveal atrophy, increased fundus autofluorescence (FAF), greyish FAF appearance and subfoveal choroidal thickness (SFCT). A comparison of features between the subgroups was conducted, and a qualitative description of the new phenotypes was proposed. The atrophy growth rate between phenotypes was then compared. RESULTS: Data were analysed from 77 eyes of 77 patients with GA. Cluster analysis identified three groups: phenotype 1 was characterised by high soft drusen load, foveal atrophy and slow growth; phenotype 3 showed high RPD load, extrafoveal and greyish FAF appearance and thin SFCT; the characteristics of phenotype 2 were midway between phenotypes 1 and 3. Phenotypes differed in all measured features (p≤0.013), with decreases in the presence of soft drusen, foveal atrophy and SFCT seen from phenotypes 1 to 3 and corresponding increases in high RPD load, high FAF and greyish FAF appearance. Atrophy growth rate differed between phenotypes 1, 2 and 3 (0.63, 1.91 and 1.73 mm(2)/year, respectively, p=0.0005). CONCLUSION: Cluster analysis identified three distinct phenotypes in GA. One of them showed a particularly slow growth pattern. BMJ Publishing Group 2018-03 2017-07-20 /pmc/articles/PMC5867406/ /pubmed/28729371 http://dx.doi.org/10.1136/bjophthalmol-2017-310268 Text en © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted. This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/
spellingShingle Clinical Science
Monés, Jordi
Biarnés, Marc
Geographic atrophy phenotype identification by cluster analysis
title Geographic atrophy phenotype identification by cluster analysis
title_full Geographic atrophy phenotype identification by cluster analysis
title_fullStr Geographic atrophy phenotype identification by cluster analysis
title_full_unstemmed Geographic atrophy phenotype identification by cluster analysis
title_short Geographic atrophy phenotype identification by cluster analysis
title_sort geographic atrophy phenotype identification by cluster analysis
topic Clinical Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5867406/
https://www.ncbi.nlm.nih.gov/pubmed/28729371
http://dx.doi.org/10.1136/bjophthalmol-2017-310268
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