Clinical Correlates of Computationally Derived Visual Field Defect Archetypes in Patients from a Glaucoma Clinic

Purpose: 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.Materials and Methods: 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.Results: 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).Conclusions: Shared clinical features between computationally derived VF archetypes and clinically observed VF patterns support the clinical validity of VF archetypal analysis.