ArticleJournal of glaucoma2024
Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on Unsupervised Machine Learning.
Article in Journal of glaucoma, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Loss to follow-up and increase in cup-to-disc ratio among glaucoma patients in the IRIS Registry.Canadian journal of ophthalmology. Journal canadien d'ophtalmologie · 2026Article
- Artificial Intelligence-Derived Biomechanical Index for Early Corneal Ectasia Detection: Advancing Beyond Tomography.Ophthalmology science · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
PRÉCIS: 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.
designCross-sectional and longitudinal study.
participantsA 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.
methodsWe 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 measureRates of SAP mean deviation (MD) change.
resultsThe 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.
conclusionsUnsupervised 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.
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