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Classification and Statistical Trend Analysis in Detecting Glaucomatous Visual Field Progression
AIM: To evaluate the agreement between different methods in detection of glaucomatous visual field progression using two classification-based methods and four statistical approaches based on trend analysis. METHODS: This is a retrospective and longitudinal study. Twenty Caucasian patients (mean age...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6558616/ https://www.ncbi.nlm.nih.gov/pubmed/31275629 http://dx.doi.org/10.1155/2019/1583260 |
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author | Valente, Cristiana D'Alessandro, Elisa Iester, Michele |
author_facet | Valente, Cristiana D'Alessandro, Elisa Iester, Michele |
author_sort | Valente, Cristiana |
collection | PubMed |
description | AIM: To evaluate the agreement between different methods in detection of glaucomatous visual field progression using two classification-based methods and four statistical approaches based on trend analysis. METHODS: This is a retrospective and longitudinal study. Twenty Caucasian patients (mean age 73.8 ± 13.43 years) with open-angle glaucoma were recruited in the study. Each visual field was assessed by Humphrey Field Analyzer, program SITA standard 30-2 or 24-2 (Carl Zeiss Meditec, Inc., Dublin, CA). Full threshold strategy was also accepted for baseline tests. Progression was analyzed by using Hodapp–Parrish–Anderson classification and the Advanced Glaucoma Intervention Study visual field defect score. For the statistical analysis, linear regression (r(2)) was calculated for mean deviation (MD), pattern standard deviation (PSD), and visual field index (VFI), and when it was significant, each series of visual field was considered progressive. We also used Progressor to look for a significant progression of each visual field series. The agreement between methods, based on statistical analysis and classification, was evaluated using a weighted kappa statistic. RESULTS: Thirty-eight visual field series were analyzed. The mean follow-up time was 6.2 ± 1.53 years (mean ± standard deviation). At baseline, the mean MD was −7.34 ± 7.18 dB; at the end of the follow-up, the mean MD was −9.25 ± 8.65 dB; this difference was statistically significant (p < 0.001). The agreement to detect progression was fair between all methods based on statistical analysis and classification except for PSD r(2). A substantial agreement (κ = 0.698 ± 0.126) was found between MD r(2) and VFI r(2). With the use of all the statistical analysis, there was a better time-saving. CONCLUSIONS: The best agreement to detect progression was found between MD r(2) and VFI r(2). VFI r(2) showed the best agreement with all the other methods. GPA2 can help ophthalmologists to detect glaucoma progression and to help in treatment decisions. PSD r(2) was the worse method to detect progression. |
format | Online Article Text |
id | pubmed-6558616 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-65586162019-07-02 Classification and Statistical Trend Analysis in Detecting Glaucomatous Visual Field Progression Valente, Cristiana D'Alessandro, Elisa Iester, Michele J Ophthalmol Research Article AIM: To evaluate the agreement between different methods in detection of glaucomatous visual field progression using two classification-based methods and four statistical approaches based on trend analysis. METHODS: This is a retrospective and longitudinal study. Twenty Caucasian patients (mean age 73.8 ± 13.43 years) with open-angle glaucoma were recruited in the study. Each visual field was assessed by Humphrey Field Analyzer, program SITA standard 30-2 or 24-2 (Carl Zeiss Meditec, Inc., Dublin, CA). Full threshold strategy was also accepted for baseline tests. Progression was analyzed by using Hodapp–Parrish–Anderson classification and the Advanced Glaucoma Intervention Study visual field defect score. For the statistical analysis, linear regression (r(2)) was calculated for mean deviation (MD), pattern standard deviation (PSD), and visual field index (VFI), and when it was significant, each series of visual field was considered progressive. We also used Progressor to look for a significant progression of each visual field series. The agreement between methods, based on statistical analysis and classification, was evaluated using a weighted kappa statistic. RESULTS: Thirty-eight visual field series were analyzed. The mean follow-up time was 6.2 ± 1.53 years (mean ± standard deviation). At baseline, the mean MD was −7.34 ± 7.18 dB; at the end of the follow-up, the mean MD was −9.25 ± 8.65 dB; this difference was statistically significant (p < 0.001). The agreement to detect progression was fair between all methods based on statistical analysis and classification except for PSD r(2). A substantial agreement (κ = 0.698 ± 0.126) was found between MD r(2) and VFI r(2). With the use of all the statistical analysis, there was a better time-saving. CONCLUSIONS: The best agreement to detect progression was found between MD r(2) and VFI r(2). VFI r(2) showed the best agreement with all the other methods. GPA2 can help ophthalmologists to detect glaucoma progression and to help in treatment decisions. PSD r(2) was the worse method to detect progression. Hindawi 2019-05-28 /pmc/articles/PMC6558616/ /pubmed/31275629 http://dx.doi.org/10.1155/2019/1583260 Text en Copyright © 2019 Cristiana Valente et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Valente, Cristiana D'Alessandro, Elisa Iester, Michele Classification and Statistical Trend Analysis in Detecting Glaucomatous Visual Field Progression |
title | Classification and Statistical Trend Analysis in Detecting Glaucomatous Visual Field Progression |
title_full | Classification and Statistical Trend Analysis in Detecting Glaucomatous Visual Field Progression |
title_fullStr | Classification and Statistical Trend Analysis in Detecting Glaucomatous Visual Field Progression |
title_full_unstemmed | Classification and Statistical Trend Analysis in Detecting Glaucomatous Visual Field Progression |
title_short | Classification and Statistical Trend Analysis in Detecting Glaucomatous Visual Field Progression |
title_sort | classification and statistical trend analysis in detecting glaucomatous visual field progression |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6558616/ https://www.ncbi.nlm.nih.gov/pubmed/31275629 http://dx.doi.org/10.1155/2019/1583260 |
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