PURPOSE: In order to reduce noise and account for spatial correlation, we applied disease mapping techniques to visual field (VF) data. We compared our calculated rates of progression to other established techniques. METHODS: Conditional autoregressive (CAR) priors, weighted to account for physiologic correlations, were employed to describe spatial and spatiotemporal correlation over the VF. Our model is extended to account for several physiologic features, such as the nerve fibers serving adjacent loci on the VF not mapping to the adjacent optic disc regions, the presence of the blind spot, and large measurement fluctuation. The models were applied to VFs from 194 eyes and fitted within a Bayesian framework using Metropolis-Hastings algorithms. RESULTS: Our method (SPROG for Spatial PROGgression) showed progression in 42% of eyes. Using a clinical reference, our method had the best receiver operating characteristics compared with the point-wise linear regression methods. Because our model intrinsically accounts for the large variation of VF data, by adjusting for spatial correlation, the effects of outliers are minimized, and spurious trends are avoided. CONCLUSIONS: by using CAR priors, we have modeled the spatial correlation in the eye. combining this with physiologic information, we are able to provide a novel method for VF analysis. model diagnostics, sensitivity, and specificity show our model to be apparently superior to CURRENT POINT-wise linear regression methods. (http://www.anzctr.org.au number, ACTRN12608000274370.).
PURPOSE: In order to reduce noise and account for spatial correlation, we applied disease mapping techniques to visual field (VF) data. We compared our calculated rates of progression to other established techniques. METHODS: Conditional autoregressive (CAR) priors, weighted to account for physiologic correlations, were employed to describe spatial and spatiotemporal correlation over the VF. Our model is extended to account for several physiologic features, such as the nerve fibers serving adjacent loci on the VF not mapping to the adjacent optic disc regions, the presence of the blind spot, and large measurement fluctuation. The models were applied to VFs from 194 eyes and fitted within a Bayesian framework using Metropolis-Hastings algorithms. RESULTS: Our method (SPROG for Spatial PROGgression) showed progression in 42% of eyes. Using a clinical reference, our method had the best receiver operating characteristics compared with the point-wise linear regression methods. Because our model intrinsically accounts for the large variation of VF data, by adjusting for spatial correlation, the effects of outliers are minimized, and spurious trends are avoided. CONCLUSIONS: by using CAR priors, we have modeled the spatial correlation in the eye. combining this with physiologic information, we are able to provide a novel method for VF analysis. model diagnostics, sensitivity, and specificity show our model to be apparently superior to CURRENT POINT-wise linear regression methods. (http://www.anzctr.org.au number, ACTRN12608000274370.).
Authors: Siamak Yousefi; Michael H Goldbaum; Madhusudhanan Balasubramanian; Tzyy-Ping Jung; Robert N Weinreb; Felipe A Medeiros; Linda M Zangwill; Jeffrey M Liebmann; Christopher A Girkin; Christopher Bowd Journal: IEEE Trans Biomed Eng Date: 2014-04 Impact factor: 4.538
Authors: Samuel I Berchuck; Jean-Claude Mwanza; Angelo P Tanna; Donald L Budenz; Joshua L Warren Journal: Sci Rep Date: 2019-03-15 Impact factor: 4.379
Authors: Joanne C Wen; Cecilia S Lee; Pearse A Keane; Sa Xiao; Ariel S Rokem; Philip P Chen; Yue Wu; Aaron Y Lee Journal: PLoS One Date: 2019-04-05 Impact factor: 3.240