What Is the Amount of Visual Field Loss Associated With Disability in Glaucoma?
Alessandro A Jammal, Nara G Ogata, Fábio B Daga, Ricardo Y Abe, Vital P Costa, Felipe A Medeiros
Summary
Application of an LCA model allowed categorization of patient-reported outcomes and quantification of visual field levels associated with disability in glaucoma.
Abstract
PURPOSE
To propose a new methodology for classifying patient-reported outcomes in glaucoma and for quantifying the amount of visual field damage associated with disability in the disease.
DESIGN
Cross-sectional study.
METHODS
A total of 263 patients with glaucoma were included. Vision-related disability was assessed by the National Eye Institute Visual Function Questionnaire (NEI VFQ-25). A latent class analysis (LCA) model was applied to analyze NEI VFQ-25 data and patients were divided into mutually exclusive classes according to their responses to the questionnaires. Differences in standard automated perimetry (SAP) mean deviation (MD) and integrated binocular mean sensitivity (MS) values between classes were investigated. The optimal number of classes was defined based on goodness-of-fit criteria, interpretability, and clinical utility.
RESULTS
The model with 2 classes, disabled and nondisabled, had the best fit with an entropy of 0.965, indicating excellent separation of classes. The disabled group had 48 (18%) patients, whereas 215 (82%) patients were classified as nondisabled. The average MD of the better eye in the disabled group was -5.98 dB vs -2.51 dB in the nondisabled group (P < .001). For the worse eye, corresponding values were -13.36 dB and -6.05 dB, respectively (P < .001).
CONCLUSION
Application of an LCA model allowed categorization of patient-reported outcomes and quantification of visual field levels associated with disability in glaucoma. A damage of approximately -6 dB for SAP MD, indicating relatively early visual field loss, may already be associated with significant disability if occurring in the better eye.
More by Alessandro A Jammal
View full profile →From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs.
A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression.
Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans.
Top Research in Visual Field
Browse all →Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications.
Relationship between Optical Coherence Tomography Angiography Vessel Density and Severity of Visual Field Loss in Glaucoma.
Improving our understanding, and detection, of glaucomatous damage: An approach based upon optical coherence tomography (OCT).
Discussion
Comments and discussion will appear here in a future update.