Abstract
Purpose :
Optical coherence tomography (OCT) is used widely diagnostically in adult glaucoma. However, despite hand-held OCT being available, its clinical use in childhood glaucoma is limited. Our aim was to generate a model based on optic nerve and circumpapillary retinal nerve fibre layer (cpRNFL) parameters derived from hand-held OCT images for clinical use in childhood glaucoma.
Methods :
Hand-held OCT volumetric images were acquired from 28 children with primary glaucoma (mean age ± S.D. - 5.1 ± 3.5 years), 39 with secondary glaucoma (8.0 ± 4.7 years), and 77 age-matched controls (6.7 ± 4.1 years). Factor analysis was used to identify the contribution of parameters to glaucomatous changes or order to generate binary logistic regression models.
Results :
Factor analysis revealed two main clusters consisting of: (i) cpRNFL and rim parameters and (ii) cup and cup-to-disc ratio parameters. Using the two principal factors to generate binary logistic regression model resulted in a ROC for detecting childhood glaucoma where the AUC was 0.951 (lower and upper C.I.: 0.915, 0.987) for primary and 0.918 (0.861, 0.976) for secondary glaucoma. A similar result was obtained by using two representative parameters from each cluster (inferior quadrant of cpRNFL and cup area, where AUC was 0.945 (0.903, 0.987) and 0.926 (0.883, 0.969), respectively).
Conclusions :
The model suggests two distinct grouping of parameters, possibly because of different stages of disease progression. Combining the parameters derived from the optic nerve using hand-held OCT imaging into a simple model has important diagnostic potential for detection and monitoring of disease progression in childhood glaucoma.
This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.