Date of Award
Master of Science
Thomas Edgar Wyatt
Primary open angle glaucoma (POAG) is an optic neuropathy characterized by progressive loss of retinal structure and visual function of the eye and may eventually lead to blindness. Standard Automated Perimetry (SAP) is a standard clinical procedure used to assess and quantify the sensitivity (in decibels) of various locations in the retina in response to light stimuli presented at the respective retinal locations. Therefore, to detect POAG and to assess efficacy of treatment procedures, it is necessary to assess progressive loss of visual sensitivity at all retinal locations simultaneously. In this work, we present a new approach to detect glaucomatous progression from localized visual function changes with statistical type I error controlled in a non-parametric framework called pointwise rate of visual function changes (PVF). The rate of visual sensitivity changes at each of the SAP retinal test locations was estimated using simple linear regression. For nonparametric analysis, regression errors were assumed to be independent and identically distributed (exchangeability criterion). The significance of rate of change (p-value) in each location was estimated using permutation tests with Monte Carlo sampling while accounting for multiple simultaneous comparison problem using Bonferroni correction. Using these p-values, glaucoma progression was detected once again, nonparametrically based on the significance (at a level of 5%) of the observed number of progressing locations. Study eyes with at least four 24-2 SAP exams (each exam with 54 retinal test locations) from the UCSD Diagnostic Innovations in Glaucoma Study (DIGS) were included. In the study eyes, 80 eyes of 74 participants were progressing based on retinal fundus photo evaluation; and in 84 eyes of 45 participants all SAP measurements were within 3 months (stable group). Performance of our new technique was compared to that of an existing methodology “Permutation of Pointwise Linear Regression—PoPLR”. Sensitivity (95% CI) of PVF and PoPLR methods in detecting glaucoma progression were 64% (53%, 75%) and 50% (38%, 62%) respectively. Specificity (95% CI) of PVF and PoPLR methods in correctly identifying the stable eyes were 98% (94%, 100%) and 94% (88%, 100%) respectively. In conclusion, correction for multiple comparison problem using Bonferroni correction provided a higher diagnostic accuracy of detecting glaucomatous progression than the existing method. While Bonferroni correction is generally conservative, it provided an optimal diagnostic accuracy due to relatively fewer number of locations simultaneously tested.
dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Rajaraman, Srinivas, "Functional Biomarkers for Detecting Glaucoma Progression" (2017). Electronic Theses and Dissertations. 1760.