Development and Validation of a Model to Predict Fast Progression in Glaucoma

The purpose of this study is to obtain genetic data on a well-defined population of patients with glaucoma that had accurate measurements of rates of structural and functional change over time, develop a predictive model for fast progression in glaucoma, and then subsequently assess the efficacy of the predictive model in a prospective cohort of patients.

The investigators hypothesize that incorporating genetic and clinical data will lead to a powerful model to predict rates of change in the disease.

Glaucoma is a neurodegenerative disease caused by progressive retinal ganglion cell (RGC) loss associated with characteristic structural changes in the optic nerve and retinal nerve fiber layer (RNFL). The neural insult can result in irreversible loss of vision and disability. Detection of progression plays a central role in the diagnosis and management of glaucoma. However, although most glaucoma patients will show some evidence of progression if followed long enough, the rate of deterioration can be highly variable among them. While most patients progress relatively slowly, others have aggressive disease with fast deterioration, which can eventually result in disability unless appropriate interventions take place.

Many risk factors have been shown to be associated with glaucoma progression, such as older age, high intraocular pressure (IOP), thin corneas, presence of disc hemorrhage, among others. However, previous attempts to develop models that can predict future rates of disease progression have been limited. For example, a "risk calculator" for progressive glaucoma developed by De Moraes et al had an R2 of only 13% to explain rates of disease progression. Such limited predictive ability may be largely explained by different inherent susceptibility of individual eyes to risk factors such as IOP. In fact, it is well-known that certain eyes can develop rapid deterioration despite relatively low IOP levels and vice-versa.

The development of accurate model to predict risk of fast progression in glaucoma would fulfill an extremely important unmet need by allowing tailoring treatment aggressiveness and monitoring according to risk. In addition, it would lead to a tool that could be used to identify patients that would most likely benefit from therapeutic interventions designed to protect the optic nerve, i.e, neuroprotective. Use of predictive statistical models could also lead to more effective clinical trials investigating new neuroprotective therapies, by targeting patient populations at most need.