title: Clinical Correlates of Computationally Derived Visual Field Defect Archetypes in Patients from a Glaucoma Clinic.
abstract: To assess the clinical validity of visual field (VF) archetypal analysis, a previously developed machine learning method for decomposing any Humphrey VF (24-2) into a weighted sum of clinically recognizable VF loss patterns.
  
  
  
  For each of 16 previously identified VF loss patterns ("archetypes," denoted AT1 through AT16), we screened 30,995 reliable VFs to select 10-20 representative patients whose VFs had the highest decomposition coefficients for each archetype. VF global indices and patient ocular and demographic features were extracted retrospectively. Based on resemblances between VF archetypes and clinically observed VF patterns, hypotheses were generated for associations between certain VF archetypes and clinical features, such as an association between AT6 (central island, representing severe VF loss) and large cup-to-disk ratio (CDR). Distributions of the selected clinical features were compared between representative eyes of certain archetypes and all other eyes using the two-tailed t-test or Fisher exact test.
  
  
  
  243 eyes from 243 patients were included, representative of AT1 through AT16. CDR was more often ≥ 0.7 among eyes representative of AT6 (central island; p = 0.002), AT10 (inferior arcuate defect; p = 0.048), AT14 (superior paracentral defect; p = 0.016), and AT16 (inferior paracentral defect; p = 0.016) than other eyes. CDR was more often < 0.7 among eyes representative of AT1 (no focal defect; p < 0.001) and AT2 (superior defect; p = 0.027), which was also associated with ptosis (p < 0.001). AT12 (temporal hemianopia) was associated with history of stroke (p = 0.022). AT11 (concentric peripheral defect) trended toward association with trial lens correction > 6D (p = 0.069).
  
  
  
  Shared clinical features between computationally derived VF archetypes and clinically observed VF patterns support the clinical validity of VF archetypal analysis.
authors: a Department of Ophthalmology , Harvard Medical School, Massachusetts Eye and Ear , Boston , MA , USA.
authors: Sophie
authors: S
authors: Cai
authors: b Department of Ophthalmology, Harvard Medical School , Schepens Eye Research Institute , Boston , MA , USA.|c Max Planck Institute for Mathematics in the Sciences , Leipzig , Germany.
authors: Tobias
authors: T
authors: Elze
authors: b Department of Ophthalmology, Harvard Medical School , Schepens Eye Research Institute , Boston , MA , USA.|d Department of Psychology , Northeastern University , Boston , MA , USA.
authors: Peter J
authors: PJ
authors: Bex
authors: a Department of Ophthalmology , Harvard Medical School, Massachusetts Eye and Ear , Boston , MA , USA.
authors: Janey L
authors: JL
authors: Wiggs
authors: a Department of Ophthalmology , Harvard Medical School, Massachusetts Eye and Ear , Boston , MA , USA.|e Channing Division of Network Medicine , Brigham and Women's Hospital , Boston , MA , USA.
authors: Louis R
authors: LR
authors: Pasquale
authors: a Department of Ophthalmology , Harvard Medical School, Massachusetts Eye and Ear , Boston , MA , USA.
authors: Lucy Q
authors: LQ
authors: Shen
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D005902:Glaucoma, Open-Angle / Q000145:classification / Q000175:diagnosis*; D006801:Humans; D007429:Intraocular Pressure / Q000502:physiology; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009898:Optic Disk / Q000473:pathology*; D009901:Optic Nerve Diseases / Q000145:classification / Q000175:diagnosis*; D012189:Retrospective Studies; D014786:Vision Disorders / Q000145:classification / Q000175:diagnosis*; D014792:Visual Acuity / Q000502:physiology; D058609:Visual Field Tests; D014794:Visual Fields
keywords: Archetype; glaucoma; machine learning; statistical models; visual field