​​Location-based Binocular VF Loss Archetypes Could Help Personalize Management

The strong associations between specific archetypes and various vision-related QoL aspects highlight the need for a personalized approach to glaucoma management—some patients may suffer more from a certain pattern of VF loss than others, because they have other needs. Tailoring interventions based on the patient’s specific pattern of VF loss may optimize outcomes and enhance VR-QoL.  Photo: Gazanchian M, et al. Invest Ophthalmol Vis Sci. 2026;67(3):28. Click image to enlarge. Previous research has shown that the binocular visual field (VF) is a better predictor of patients’ vision-related quality of life (QoL); therefore, investigating the relationship between different patterns of binocular VF defects and various aspects of vision-related QoL is essential for understanding the multifaceted impact of glaucoma on patients’ lives. Archetypal analysis, a statistical method for identifying prototypical patterns within complex datasets, is a promising way to elucidate spatial patterns of VF loss. This unsupervised machine learning technique has been used to quantify patterns of monocular VF loss in glaucoma, but no study has yet examined archetypes for binocular VF loss in glaucoma. After they defined 12 binocular VF loss archetypes, researchers from the Netherlands and the United Kingdom sought to analyze their relationship between the different aspects of vision-related QoL. Widespread VF loss (archetype 12) had a significant impact on QoL. In contrast, inferior paracentral loss (archetype 11) did not show an independent effect in models adjusted for foveal sensitivity. However, when foveal sensitivity was not included as a covariate, archetype 11 became highly associated with several aspects of vision-related QoL. Additionally, vision loss in the inferior hemifield (archetype 8) tended to have a greater impact on daily life than loss in the superior hemifield (archetype 9). In this study that was published in Investigative Ophthalmology & Visual Science, the research team included 7,305 pairs of reliable standard automated perimetry (24-2 SITA fast and standard) test results of patients from five glaucoma clinics in England in their archetypal analysis on the corresponding integrated VFs (estimates of the binocular VFs from pairs of monocular VFs). Then, they used these archetypes to deconstruct the binocular VF of 269 patients with glaucoma from the Netherlands that had completed four different vision-related QoL questionnaires. Subscales of the questionnaires were groups of related questions involving driving, near activities, distant activities and social functioning.“Our study identified a significant and strong relationship between global VF loss (represented by archetype 12) and driving performance. Importantly, archetype 11 (inferior paracentral loss) was not associated with driving in the primary analysis but became highly significant when the analysis was not adjusted for foveal sensitivity,” the study authors pointed out in their paper. “Whereas the impact of VF defects on driving is well-established, the specific influence of defect location remains unresolved.”The researchers emphasized that these predictions should be interpreted with caution because this prediction model was, unlike the archetypes themselves, trained and tested on the same dataset, which inflates its apparent performance. Their objective was to investigate associations, not to generate accurate predictions of vision-related QoL. They suggested that future research on this topic could benefit from a longitudinal study design, which could track changes in binocular VF loss archetypes and vision-related QoL over time, helping to establish causal links.Click here for the journal source. Gazanchian M, Nejad A, Crabb DP, et al. Archetypes of binocular visual field loss and their impact on vision-related quality of life in glaucoma patients. Invest Ophthalmol Vis Sci. 2026;67(3):28.  This article was developed by the editorial staff in conjunction with experts in the field. In the process, AI may have been among the editorial tools used to meet the goals of human editors, who approved all content.