Spatial and Temporal Processing of Threshold Data for Detection of Progressive Glaucomatous Visual Field Loss

Abstract

To evaluate the effect of spatial and temporal filtering of threshold visual field data on the ability of pointwise linear regression (PLR) to detect progressive glaucomatous visual field loss. Methods: Longitudinal visual field data (Full-Threshold Program 30-2 test point pattern) were simulated using a computer model of glaucomatous visual field progression. This approach permitted construction of a "gold standard" because matching visual field data without variability could be generated and analyzed. Four clustered progressive defects were produced, consisting of 2, 3, 9, and 18 locations, respectively, each with progression rates of −1 and −2.5 dB/y. Pointwise linear regression was used to identify progressive test locations (criterion for progression of statistically significant slope of Յ−1 dB/y, PϽ.05). Each visual field series was analyzed after the following 3 procedures: (1) no filtering (unprocessed data), (2) Gaussian spatial possessing (3 ϫ 3 grid), and (3) temporal processing (2 field moving average). The effect of spatial and temporal processing on PLR discriminatory power for progression detection was quantified by comparison with the gold standard. Results: Spatial processing reduced PLR sensitivity to levels below that achieved for analysis of unprocessed data for small progressive defects (Յ9 locations) or at the low true progression rate (−1 dB/y). Under these conditions, spatial processing caused small PLR specificity improvement. Spatial processing only improved PLR sensitivity above unprocessed levels when progressive defects were large and changing rapidly (progression rate of −2.5 dB/y). Temporal processing gave consistent PLR improvement in sensitivity for all defect sizes and true progression rates. Pointwise linear regression sensitivity gain provided by temporal processing allowed progression to be detected 2 to 3 visual fields earlier than for analysis of raw data. Specificity dropped slightly as a result of temporal processing but remained at 89% or above for all conditions studied. Conclusions: Gaussian spatial processing reduces PLR discriminatory power with low true progression rates or small progressive defect sizes and, therefore, is of limited use for detection of progressive visual field loss. Temporal processing improves the sensitivity of PLR and reduces the number of tests required to detect progressive loss with minimal loss of specificity. Clinical Relevance: Image processing techniques can be applied to threshold visual field data to enhance sensitivity or specificity of PLR for the determination of progressive change. This investigation demonstrates that temporal processing may assist with the detection of significant progressive visual field loss with fewer test results than unprocessed data.

Pointwise Rates of Visual Field Progression Cluster according to Retinal Nerve Fiber Layer Bundles

“…Gaussian and non-Gaussian spatial filtering of threshold sensitivity at neighboring test locations has been used to improve performance of trend analyses. [16][17][18][19] We speculate that the correlation coefficients derived from the current study might be better suited for such spatial filtering compared with the cross-sectional weighting schemes used in prior studies. Gaussian and non-Gaussian filters are mostly based on information from neighboring points, while the correlation of visual field test locations normally goes beyond immediately adjacent points.…”

Pointwise Rates of Visual Field Progression Cluster according to Retinal Nerve Fiber Layer Bundles

“…Gaussian and non-Gaussian spatial filtering of threshold sensitivity at neighboring test locations has been used to improve performance of trend analyses. [16][17][18][19] We speculate that the correlation coefficients derived from the current study might be better suited for such spatial filtering compared with the cross-sectional weighting schemes used in prior studies. Gaussian and non-Gaussian filters are mostly based on information from neighboring points, while the correlation of visual field test locations normally goes beyond immediately adjacent points.…”

Spatial Modeling of Visual Field Data for Assessing Glaucoma Progression

“…Previously, a spatial filter was introduced to take a weighted average of the surrounding points. 11 While this method succeeded in reducing variation, it was also less sensitive, 12 and did not take into account the physiology of the eye, in that the VF locations along the temporal horizontal midline do not map in adjoining locations on the optic disc. Another spatial filter was created to explore spatial correlations in VFs in order to reduce noise.…”

Structure and Function in Glaucoma: The Relationship between a Functional Visual Field Map and an Anatomic Retinal Map

“…It is clear that measurement error should be attenuated by this approach, but its non-physiological nature results in blurring (or erasing) of established field defects. This was demonstrated by Spry et al, 12 who examined the effect of Gaussian filtering on detection of glaucomatous progression using point-wise linear regression analysis compared with raw threshold sensitivity data. Although Gaussian filtering was able to improve specificity by reducing variability of non-progressing locations, sensitivity was reduced as it also attenuated useful signal.…”

Pointwise Rates of Visual Field Progression Cluster according to Retinal Nerve Fiber Layer Bundles

“…Gaussian and non-Gaussian spatial filtering of threshold sensitivity at neighboring test locations has been used to improve performance of trend analyses. [16][17][18][19] We speculate that the correlation coefficients derived from the current study might be better suited for such spatial filtering compared with the cross-sectional weighting schemes used in prior studies. Gaussian and non-Gaussian filters are mostly based on information from neighboring points, while the correlation of visual field test locations normally goes beyond immediately adjacent points.…”

Spatial Modeling of Visual Field Data for Assessing Glaucoma Progression

“…Previously, a spatial filter was introduced to take a weighted average of the surrounding points. 11 While this method succeeded in reducing variation, it was also less sensitive, 12 and did not take into account the physiology of the eye, in that the VF locations along the temporal horizontal midline do not map in adjoining locations on the optic disc. Another spatial filter was created to explore spatial correlations in VFs in order to reduce noise.…”

Structure and Function in Glaucoma: The Relationship between a Functional Visual Field Map and an Anatomic Retinal Map

“…It is clear that measurement error should be attenuated by this approach, but its non-physiological nature results in blurring (or erasing) of established field defects. This was demonstrated by Spry et al, 12 who examined the effect of Gaussian filtering on detection of glaucomatous progression using point-wise linear regression analysis compared with raw threshold sensitivity data. Although Gaussian filtering was able to improve specificity by reducing variability of non-progressing locations, sensitivity was reduced as it also attenuated useful signal.…”

Pointwise Rates of Visual Field Progression Cluster according to Retinal Nerve Fiber Layer Bundles

“…Gaussian and non-Gaussian spatial filtering of threshold sensitivity at neighboring test locations has been used to improve performance of trend analyses. [16][17][18][19] We speculate that the correlation coefficients derived from the current study might be better suited for such spatial filtering compared with the cross-sectional weighting schemes used in prior studies. Gaussian and non-Gaussian filters are mostly based on information from neighboring points, while the correlation of visual field test locations normally goes beyond immediately adjacent points.…”

Spatial Modeling of Visual Field Data for Assessing Glaucoma Progression

“…Previously, a spatial filter was introduced to take a weighted average of the surrounding points. 11 While this method succeeded in reducing variation, it was also less sensitive, 12 and did not take into account the physiology of the eye, in that the VF locations along the temporal horizontal midline do not map in adjoining locations on the optic disc. Another spatial filter was created to explore spatial correlations in VFs in order to reduce noise.…”

Structure and Function in Glaucoma: The Relationship between a Functional Visual Field Map and an Anatomic Retinal Map

“…It is clear that measurement error should be attenuated by this approach, but its non-physiological nature results in blurring (or erasing) of established field defects. This was demonstrated by Spry et al, 12 who examined the effect of Gaussian filtering on detection of glaucomatous progression using point-wise linear regression analysis compared with raw threshold sensitivity data. Although Gaussian filtering was able to improve specificity by reducing variability of non-progressing locations, sensitivity was reduced as it also attenuated useful signal.…”