Diagnosing Glaucoma Progression With Visual Field Data Using a Spatiotemporal Boundary Detection Method

Abstract

Diagnosing glaucoma progression is critical for limiting irreversible vision loss. A common method for assessing glaucoma progression uses a longitudinal series of visual fields (VFs) acquired at regular intervals. VF data are characterized by a complex spatiotemporal structure due to the data generating process and ocular anatomy. Thus, advanced statistical methods are needed to make clinical determinations regarding progression status. We introduce a spatiotemporal boundary detection model that allows the underlying anatomy of the optic disc to dictate the spatial structure of the VF data across time. We show that our new method provides novel insight into vision loss that improves diagnosis of glaucoma progression using data from the Vein Pulsation Study Trial in Glaucoma and the Lions Eye Institute trial registry. Simulations are presented, showing the proposed methodology is preferred over existing spatial methods for VF data. Supplementary materials for this article are available online and the method is implemented in the R package womblR.

Keywords:

• Areal wombling
• Bayesian methods
• Conditional autoregressive models
• Dissimilarity metric

Acknowledgments

The authors thank the Lions Eye Institute for Figure 1(b) and Wallace L. M. Alward (Ophthalmology and Visual Sciences, University of Iowa Carver College of Medicine) for Figure 1(c); and also Brigid D. Betz-Stablein (School of Medical Sciences, University of New South Wales), William H. Morgan (Lions Eye Institute, University of Western Australia), Philip H. House (Lions Eye Institute, University of Western Australia), and Martin L. Hazelton (Institute of Fundamental Sciences, Massey University) for providing the dataset from their original study for use in this analysis.

Additional information

Funding

This work was partially supported by the National Institute of Environmental Health Sciences (SIB; T32ES007018), the Research to Prevent Blindness (DO, UNC-CH), and the National Center for Advancing Translational Science (JLW; UL1 TR001863, KL2 TR001862).