Spatial Modeling of Visual Field Data for Assessing Glaucoma Progression

Abstract

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.

Glaucoma Progression Detection Using Structural Retinal Nerve Fiber Layer Measurements and Functional Visual Field Points

“…4 with Fig 3(a)]. As can be seen, this AUROC is much higher than the AUROC reported in [24] which is 0.69.…”

“…RVM performs equal to, or better, than other statistical techniques [23]. In [24], the authors proposed spatial modeling of visual fields to enhance glaucoma progression detection accuracy. By properly modeling visual field dependencies, the authors reached a reasonable degree of accuracy detecting glaucomatous progression.…”

Optimizing Structure–Function Relationship by Maximizing Correspondence Between Glaucomatous Visual Fields and Mathematical Retinal Nerve Fiber Models

“…2,9,10,20 As mentioned above, and in contrast to other authors, 14,26 we do not aim to produce an individualized RNFL map, but a general model that can provide a sufficient approximation to the RNFL structure, as shown in Figure 4. Our optimized RNFL model captures the variability in the individual RNFL structures in the RMSE, which contains the interindividual variability as well as the error made by approximating the real RNFL structure with the model.…”

“…Gardiner et al 2 pursued a similar approach by applying regression with some constraints on the covariances to derive weights. BetzStablein et al 10 used weights that were based on adjacency in the regular grid of test locations as well as physical adjacency to incorporate the spatial structure of glaucomatous damage into their model.…”

Evaluation of Visual Field Progression in Glaucoma

“…This approach seems to minimize the effect of outlier locations that show deterioration or improvement due to artifact [15].…”