Longitudinal visual field archetypal analysis of optic neuritis treated in a clinical setting

• Published in: BMJ open ophthalmology 2022-11, Vol.7 (1), p.e001136
• Main Authors: Branco, Joseph, Elze, Tobias, Wang, Jui-Kai, Pasquale, Louis R, Garvin, Mona K, Kardon, Randy, Kupersmith, Mark J
• Format: Article
• Language: English
• Subjects: Datasets; Decomposition; Machine learning; Neuro-Ophthalmology; Open source software; Optic Nerve; Software packages; Vision
• ISSN: 2397-3269; 2397-3269
• DOI: 10.1136/bmjophth-2022-001136

Background/aimsWe previously used archetypal analysis (AA) to create a model that quantified patterns (archetypes (ATs)) of visual field (VF) loss that can predict recovery and reveal residual VF deficits from eyes in the Optic Neuritis Treatment Trial (ONTT). We hypothesised that AA could produce similar results for ON VFs collected in clinical practice.MethodsWe applied AA to a retrospective data set of 486 VFs collected in 1 neuro-ophthalmology service from 141 eyes with acute ON and typical VF defects, to create a clinic-derived AT model. We also used the ONTT-derived AT model to analyse this new dataset. We compared the findings of both models by decomposing VFs into component ATs of varying per cent weight (PW), correlating presentation AT PW with mean deviation (MD) at final visits for each eye and identifying residual deficits in VFs considered normal.ResultsBoth models, each with 16 ATs, decomposed each presentation VF into 0–6 abnormal ATs representative of known patterns of ON-related VF loss. AT1, the normal pattern in both models, correlated strongly with MD for VFs collected at presentation (r=0.82; p