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Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning

PURPOSE: To identify ocular hypertension (OHT) subtypes with different trends of visual field (VF) progression based on unsupervised machine learning and to discover factors associated with fast VF progression. DESIGN: Cross-sectional and longitudinal study. PARTICIPANTS: A total of 3133 eyes of 156...

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Autores principales: Huang, Xiaoqin, Poursoroush, Asma, Sun, Jian, Boland, Michael V., Johnson, Chris, Yousefi, Siamak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557795/
https://www.ncbi.nlm.nih.gov/pubmed/37808089
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author Huang, Xiaoqin
Poursoroush, Asma
Sun, Jian
Boland, Michael V.
Johnson, Chris
Yousefi, Siamak
author_facet Huang, Xiaoqin
Poursoroush, Asma
Sun, Jian
Boland, Michael V.
Johnson, Chris
Yousefi, Siamak
author_sort Huang, Xiaoqin
collection PubMed
description PURPOSE: To identify ocular hypertension (OHT) subtypes with different trends of visual field (VF) progression based on unsupervised machine learning and to discover factors associated with fast VF progression. DESIGN: Cross-sectional and longitudinal study. PARTICIPANTS: A total of 3133 eyes of 1568 ocular hypertension treatment study (OHTS) participants with at least five follow-up VF tests were included in the study. METHODS: We used a latent class mixed model (LCMM) to identify OHT subtypes using standard automated perimetry (SAP) mean deviation (MD) trajectories. We characterized the subtypes based on demographic, clinical, ocular, and VF factors at the baseline. We then identified factors driving fast VF progression using generalized estimating equation (GEE) and justified findings qualitatively and quantitatively. MAIN OUTCOME MEASURE: Rates of SAP mean deviation (MD) change. RESULTS: The LCMM model discovered four clusters (subtypes) of eyes with different trajectories of MD worsening. The number of eyes in clusters were 794 (25%), 1675 (54%), 531 (17%) and 133 (4%). We labeled the clusters as Improvers, Stables, Slow progressors, and Fast progressors based on their mean of MD decline, which were 0.08, −0.06, −0.21, and −0.45 dB/year, respectively. Eyes with fast VF progression had higher baseline age, intraocular pressure (IOP), pattern standard deviation (PSD) and refractive error (RE), but lower central corneal thickness (CCT). Fast progression was associated with calcium channel blockers, being male, heart disease history, diabetes history, African American race, stroke history, and migraine headaches. CONCLUSION: Unsupervised clustering can objectively identify OHT subtypes including those with fast VF worsening without human expert intervention. Fast VF progression was associated with higher history of stroke, heart disease, diabetes, and history of more using calcium channel blockers. Fast progressors were more from African American race and males and had higher incidence of glaucoma conversion. Subtyping can provide guidance for adjusting treatment plans to slow vision loss and improve quality of life of patients with a faster progression course.
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spelling pubmed-105577952023-10-07 Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning Huang, Xiaoqin Poursoroush, Asma Sun, Jian Boland, Michael V. Johnson, Chris Yousefi, Siamak ArXiv Article PURPOSE: To identify ocular hypertension (OHT) subtypes with different trends of visual field (VF) progression based on unsupervised machine learning and to discover factors associated with fast VF progression. DESIGN: Cross-sectional and longitudinal study. PARTICIPANTS: A total of 3133 eyes of 1568 ocular hypertension treatment study (OHTS) participants with at least five follow-up VF tests were included in the study. METHODS: We used a latent class mixed model (LCMM) to identify OHT subtypes using standard automated perimetry (SAP) mean deviation (MD) trajectories. We characterized the subtypes based on demographic, clinical, ocular, and VF factors at the baseline. We then identified factors driving fast VF progression using generalized estimating equation (GEE) and justified findings qualitatively and quantitatively. MAIN OUTCOME MEASURE: Rates of SAP mean deviation (MD) change. RESULTS: The LCMM model discovered four clusters (subtypes) of eyes with different trajectories of MD worsening. The number of eyes in clusters were 794 (25%), 1675 (54%), 531 (17%) and 133 (4%). We labeled the clusters as Improvers, Stables, Slow progressors, and Fast progressors based on their mean of MD decline, which were 0.08, −0.06, −0.21, and −0.45 dB/year, respectively. Eyes with fast VF progression had higher baseline age, intraocular pressure (IOP), pattern standard deviation (PSD) and refractive error (RE), but lower central corneal thickness (CCT). Fast progression was associated with calcium channel blockers, being male, heart disease history, diabetes history, African American race, stroke history, and migraine headaches. CONCLUSION: Unsupervised clustering can objectively identify OHT subtypes including those with fast VF worsening without human expert intervention. Fast VF progression was associated with higher history of stroke, heart disease, diabetes, and history of more using calcium channel blockers. Fast progressors were more from African American race and males and had higher incidence of glaucoma conversion. Subtyping can provide guidance for adjusting treatment plans to slow vision loss and improve quality of life of patients with a faster progression course. Cornell University 2023-09-26 /pmc/articles/PMC10557795/ /pubmed/37808089 Text en https://creativecommons.org/licenses/by-nc-sa/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. If you remix, adapt, or build upon the material, you must license the modified material under identical terms.
spellingShingle Article
Huang, Xiaoqin
Poursoroush, Asma
Sun, Jian
Boland, Michael V.
Johnson, Chris
Yousefi, Siamak
Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning
title Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning
title_full Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning
title_fullStr Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning
title_full_unstemmed Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning
title_short Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning
title_sort identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557795/
https://www.ncbi.nlm.nih.gov/pubmed/37808089
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