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Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on

Xiaoqin Huang1, Asma Poursoroush1, Jian Sun2

  • 1Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN.

Journal of Glaucoma
|August 2, 2024
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Summary
This summary is machine-generated.

Unsupervised machine learning identified four subtypes of ocular hypertension (OHT) patients based on visual field (VF) progression. Fast VF worsening was linked to specific demographic and clinical factors, aiding targeted treatment strategies.

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Area of Science:

  • Ophthalmology
  • Data Science
  • Medical Research

Background:

  • Ocular hypertension (OHT) is a precursor to glaucoma, characterized by elevated intraocular pressure (IOP).
  • Predicting visual field (VF) progression in OHT is crucial for timely intervention.
  • Identifying distinct patient subtypes can refine risk stratification and treatment approaches.

Purpose of the Study:

  • To classify ocular hypertension (OHT) patient subtypes based on visual field (VF) progression patterns using unsupervised machine learning.
  • To identify demographic, clinical, and ocular factors associated with rapid VF decline in OHT.

Main Methods:

  • A latent class mixed model (LCMM) was employed to analyze standard automated perimetry (SAP) mean deviation (MD) trajectories in 3133 eyes from 1568 OHTS participants.
  • Subtypes were characterized by baseline factors, and generalized estimating equation (GEE) identified predictors of fast VF progression.

Main Results:

  • Four distinct OHT subtypes were identified: improvers, stables, slow progressors, and fast progressors, with mean MD decline rates of 0.08, -0.06, -0.21, and -0.45 dB/year, respectively.
  • Fast VF progression was associated with higher baseline age, IOP, pattern standard deviation (PSD), and refractive error (RE), and lower central corneal thickness (CCT).
  • Factors such as male sex, history of heart disease, diabetes, African American race, and stroke history were linked to faster progression.

Conclusions:

  • Unsupervised clustering objectively identifies OHT subtypes, including those with rapid VF worsening.
  • Fast VF progression is associated with specific risk factors including stroke, heart disease, diabetes, African American race, and male sex.
  • Subtyping facilitates personalized treatment strategies to mitigate vision loss and enhance patient quality of life.