Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on Unsupervised Machine Learning.

Autor:	Huang X; Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN., Poursoroush A; Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN., Sun J; Integrated Data Sciences Section, Research Technologies Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), MD, Bethesda., Boland MV; Department of Ophthalmology, Massachusetts Eye and Ear, MA, Boston., Johnson CA; Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, IA., Yousefi S; Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN.; Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN.
Jazyk:	angličtina
Zdroj:	Journal of glaucoma [J Glaucoma] 2024 Nov 01; Vol. 33 (11), pp. 815-822. Date of Electronic Publication: 2024 Aug 05.
DOI:	10.1097/IJG.0000000000002472
Abstrakt:	Prcis: We developed unsupervised machine learning models to identify different subtypes of patients with ocular hypertension in terms of visual field (VF) progression and discovered 4 subtypes with different trends of VF worsening. We then identified factors associated with fast VF progression. 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 5 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 (cluster 1), stables (cluster 2), slow progressors (cluster 3), and fast progressors (cluster 4) based on their mean of MD decline rate, 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 being male, heart disease history, diabetes history, African American race, and stroke history. Conclusions: 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 and diabetes. Fast progressors were more from African American race, 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. (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)
Databáze:	MEDLINE
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