The Diagnostic Innovations in Glaucoma Study (DIGS): Structural Assessment, funded since 1995, has led to significant improvements in our ability to detect glaucomatous optic disc damage, and a better understanding of the complex relationship between optic disc damage and corresponding visual field loss. The overall goal of this DIGS competitive renewal is to improve the detection and prediction of glaucomatous progression. Longitudinal monkey and human spectral domain optical coherence tomography (SDOCT), confocal scanning laser ophthalmoscopy (CSLO) and scanning laser polarimetry (GDx) data and images will be used to address the following 3 specific aims: 1) to improve our understanding of macular, retinal nerve fiber layer, optic nerve head, pre-laminar and laminar change in normal aging and glaucoma, 2) to optimize testing protocols and imaging analysis techniques for detecting change to reduce testing required, and 3) to predict which individuals are at a high risk of progression, and which are likely to progress rapidly.
In Specific Aim 1, we address several hypotheses designed to determine how best to utilize macula, optic nerve head, retinal nerve fiber layer thickness and pre-lamina and laminar measurements to differentiate between small physiologic age-related change and small-pathologic OAG related change. Measuring the velocity of these changes is emphasized. Our preliminary results suggest that computational and statistical techniques can reduce the number of CSLO images required to reproducibly detect change.
Specific Aim 2 focuses on the hypothesis that these techniques when applied to SDOCT can shorten the testing required to verify change, and thereby reduce the costs of glaucoma management and clinical trials for new glaucoma therapy. To address Specific Aim 3, baseline imaging-based structural parameters and relevant clinical and demographic predictive factors will be included in multivariable Cox proportional hazards models for predicting who will develop photograph based and/or visual field based progression and who will progress rapidly. This project addresses the current National Eye Institute National Plan for Eye and Vision Research glaucoma program objectives to "develop improved diagnostic measures to detect optic nerve disease, progression, and treatment effectiveness." By identifying the most appropriate structural measures, reducing the number of tests required, and developing prediction models, this proposal will improve our ability to manage glaucoma patients with the ultimate goals of reducing both the likelihood of visual function loss and the costs of glaucoma management.
Primary open angle glaucoma is a leading cause of blindness in the United States and worldwide;over 2.2 million Americans have glaucoma and that over 130,000 are legally blind from the disease. The overall goal of this competitive renewal entitled "Diagnostic Innovations in Glaucoma Study (DIGS): Structural Assessment is to improve the detection of glaucomatous progression so that the individuals that are at the highest risk of going blind from the disease are identified early and treatment initiated. Specifically, we will continue to follow a group of healthy individuals, individuals with glaucoma and those at high risk of developing the disease with the latest generation of ophthalmic diagnostic imaging instruments to 1) improve our ability to differentiate between glaucomatous changes and changes due to normal aging, 2) shorten the time to detect change and reduce costs of both glaucoma care and clinical trials of new glaucoma therapies, and 3) predict which individuals are at a high risk of progression, and which are likely to progress rapidly.
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|Bailey, Jessica N Cooke; Yaspan, Brian L; Pasquale, Louis R et al. (2014) Hypothesis-independent pathway analysis implicates GABA and acetyl-CoA metabolism in primary open-angle glaucoma and normal-pressure glaucoma. Hum Genet 133:1319-30|
|Yousefi, Siamak; Goldbaum, Michael H; Balasubramanian, Madhusudhanan et al. (2014) Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements. IEEE Trans Biomed Eng 61:2112-24|
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