Abstract
purpose. To determine the spatial characteristics of glaucomatous visual field progression in persons with glaucomatous-appearing optic neuropathy (GON) from the Diagnostic Innovations in Glaucoma Study (DIGS).
methods. Changes in pattern deviation (PD) plot values from the average of two baseline examinations to two follow-up examinations were evaluated in test locations. All were eligible, full threshold, pattern 24-2, standard automated perimetry (SAP) examinations (Humphrey Field Analyzer II; Carl Zeiss Meditec, Inc., Dublin, CA) in visual field series from 200 patients with GON confirmed on two occasions by stereophoto review. The proportion of patients exhibiting PD plot progression was determined at each of 52 locations for patients with a baseline abnormal result (P < 5% or worse) in one or more of 52 PD locations in either the first or second baseline test for a total of 2704 location pairings for each possible level of negative PD change from −1 to −50 dB. Progression was defined as any worsening of PD plot value in the follow-up test relative to the average PD plot value in the baseline tests. Monte Carlo simulation was used to determine the significance of the observed patterns of PD plot progression.
results. Changes in PDs were dependent on their location relative to abnormal PD locations in the first test. Of those patients with an abnormality at a location at baseline (mean, 0.23 ± 0.07), the proportion of patients changing by −2 dB or more ranged between 0.09 and 0.55 (mean, 0.29 ± 0.06) across locations. For changes of −6 dB or more, the proportions ranged between 0.00 and 0.26 (mean, 0.08 ± 0.04) of patients. These proportions and the proportional probabilities for each of 2704 location pairings are reported for selected levels of change. The proportional probabilities are consistent with a map of the retinal nerve fiber layer bundles.
conclusions. Visual field progression occurs in retinotopically constrained patterns consistent with changes along the nerve fiber bundle.
Clinical intervention in glaucoma is concerned with halting or slowing progression of the disease. Integral to this goal is the tracking of disease progression. Clinically, visual field testing can be used to provide an assessment of functional changes. These functional changes are related to the underlying health of the retinal ganglion cells. Weber and Ulrich
1 quantified this relationship, building a map of locations whose defects were related to specific scotoma patterns, approximating the nerve fiber bundle projections. More recently, Garway-Heath et al.
2 have also developed a mapping of the relationship between the nerve fiber layer bundles and the visual field in patients with normal-tension glaucoma. The goal of the present study is to assess whether longitudinal changes also obey these projections such that change is more likely to occur in regions along the same nerve fiber bundles where damaged locations exist.
Determining the spatial patterns of visual field progression may help improve progression algorithms. Most visual field progression algorithms do not take into account the spatial pattern of changes. Statpac’s Glaucoma Change Probability
3 4 and the Glaucoma Progression Analysis (GPA), the latter born from the Early Manifest Glaucoma Trial (EMGT) progression algorithm,
5 rely on repeatability of multiple negative point-wise changes to thresholds on either the total deviation (TD) or pattern deviation (PD) plot, but do not require any additional spatial criteria for calling progression. Point-wise linear regression analysis (PLRA) techniques, such as that implemented by the Progressor software (Moorfields Eye Hospital, London, UK, Medisoft Ltd., Leeds, UK),
6 are also concerned with point-wise changes of the threshold plot, and similarly do not impose any spatial criteria for progression. The Collaborative Initial Glaucoma Treatment Study (CIGTS) uses a score obtained by summing the convolution of a scoring algorithm over a numerically transformed TD probability plot.
7 By virtue of the scoring procedure, the CIGTS score does not fully account for the relationship of distant defective locations and merely discounts singular defects that are not adjacent to TD defects. The algorithm that comes closest to accounting for spatial relationships is the Advanced Glaucoma Intervention Study (AGIS). It applies a complex scoring algorithm to defect patterns in separate regions of the visual field.
8 Though AGIS takes into account spatial relationships, the regions are few in number, rather broad, and nonspecific for any particular spatial pattern of progression. In addition, the obtained score is heuristic since the scoring method was developed subjectively. For a review of these progression algorithms, see Spry and Johnson.
9
Determining the spatial characteristics of visual field progression may help development of more sensitive progression algorithms. In a previous study of cross-sectional data, Monte Carlo simulation was used to illustrate common defect-patterns across a glaucomatous population (Hu A, et al. IOVS 2005;46:ARVO E-Abstract 3734). In this study, Monte Carlo simulation is used to quantify the spatial relationship of visual field changes in longitudinal series from patients with glaucoma, and these patterns are compared to Weber’s nerve fiber bundle map. Progression is evaluated between locations that exhibit defects on initial tests and the thresholds in follow-up tests across time for all test locations.
Participants were from a longitudinal study of visual function in glaucoma, The Diagnostic Innovations in Glaucoma Study (DIGS), at the Hamilton Glaucoma Center, University of California, San Diego. This study conforms to the tenets of the Declaration of Helsinki and the Health Insurance Portability and Accountability Act of 1996 (HIPAA) and was approved by the Human Subjects Committee at the University of California, San Diego. The nature of the procedures was explained, and informed consent was obtained from all participants.
Inclusion Criteria for DIGS.
All participants underwent complete ophthalmic examination including slit lamp biomicroscopy, intraocular pressure measurement, dilated stereoscopic fundus examination, and stereophotography of the optic nerve heads. Simultaneous stereoscopic photographs were obtained in all participants and were of adequate quality for the participant to be included. At study entry, all participants had open angles, a best corrected acuity of 20/40 or better, a spherical refraction within ±5.0 D, and cylinder correction within ±3.0 D. A family history of glaucoma was allowed.
Exclusion Criteria for DIGS.
Participants were excluded if they had a history of intraocular surgery (except uncomplicated cataract surgery). Also excluded were all participants with elevated intraocular pressure secondary to causes other than glaucoma (e.g., iridocyclitis, trauma), other intraocular eye disease, other diseases affecting the visual field (e.g., pituitary lesions, demyelinating diseases, HIV or AIDS, or diabetic retinopathy), medications known to affect visual field sensitivity or problems other than glaucoma affecting color vision.
Inclusion Criteria for this Study.
DIGS includes healthy eyes and eyes with ocular hypertension, suspected glaucoma, and diagnosed glaucoma. For this study, only the DIGS participants with at least four reliable full-threshold, pattern 24-2, standard automated perimetry (SAP) tests performed on the Humphrey Field Analyzer (Carl-Zeiss Meditec, Dublin, CA) and at least two stereophotographs indicative of glaucomatous optic neuropathy (GON) any time during follow-up were considered for inclusion in this study.
Four, reliable SAP full-threshold examinations were chosen for inclusion, with two serving as baseline examinations and the other two serving as follow-up examinations. If more than four examinations in a visual field series from a patient were eligible for inclusion, the earliest two tests performed within the shortest amount of time of each other were chosen for inclusion as baseline examinations and the latest two examinations were chosen as follow-up examinations. If only four examinations were eligible, the first two examinations were chosen as the baseline examinations, and the last two were chosen as the follow-up examinations. SAP reliability was defined as fewer than 33% fixation loss or false-positive or -negative errors. Because PD plots would be used for subsequent analyses, persons with baseline visual fields that had severe or end-stage loss as defined by the Hodapp-Anderson-Parrish criteria
10 were excluded because the PD plot can appear normal at the more advanced stages of the disease.
11
GON diagnoses were based on masked, independent review of simultaneous color stereophotographs of the optic nerve head by two trained graders from the Optic Disc Reading Center, UCSD. When graders disagreed, a third expert adjudicated. GON was defined as the presence of excavation, neuroretinal rim thinning or notching, nerve fiber layer defects, or an asymmetry of the vertical cup-to-disc ratio ≥0.2 between the two eyes. Stereophotographs were obtained (TRC-SS camera; Topcon Instrument Corp. of America, Paramus, NH) after maximum pupil dilation and were assessed with a stereoscopic viewer (Pentax Stereo Viewer II; Asahi Optical Co., Tokyo, Japan). Intraocular pressure was not used for inclusion or exclusion of subjects.
Exclusion Criteria for this Study.
The purpose of this study was to quantify the likelihood of future progression in all visual field locations relative to an initial baseline defect in patients with confirmed GON. There is no gold standard for glaucoma progression. Current progression algorithms were developed using test–retest confidence intervals from baseline tests in clinical trials
7 8 or patients with “stable” glaucoma
4 who were tested within a short period when glaucoma is not likely to progress. Therefore, the definitions of progression for these algorithms are based on confidence intervals for stability and not physiological progression. These algorithms do not capitalize on the spatial patterns of change, which could enhance their sensitivity for change. Gardiner et al.
15 have demonstrated that applying physiologically derived spatial filters to individual visual fields within a series can increase sensitivity to progressive changes. Although, in both research and clinical practice, looking for adjacent locations to worsen to be certain of progression has been evaluated, the method presented herein provides a valid approach for assessing the importance of both proximal and distal points along a nerve fiber bundle for their relevance in observing glaucomatous change in visual fields. In addition, future scoring methods can use these spatial relationships to predict the most likely relationship of future defects relative to the defects found in baseline visual fields. Furthermore, these patterns may serve as a guide for adding test points to individualize follow-up testing by using a condensed perimetric grid within the regions most likely to progress (Paetzold J, et al.
IOVS 2005;46:ARVO E-Abstract 636).
The results of this study show a spatial relationship between initially defective locations found at baseline and those found on subsequent testing and were similar to those observed in a Monte Carlo simulation that was used to analyze fields from a cross-sectional group of patients with visual function defects (Hu A, et al. IOVS 2005;46:ARVO E-Abstract 3734). In addition to being consistent with clinical impressions, the Monte Carlo methods used were able to quantify these spatial relationships. In this study population, superior defects are related to progression in superior locations, with defects related to nerve fiber bundle patterns. The inferior defects also demonstrated specific patterns of progression, but not as clearly as the superior defects. The nasal step region is an important region, as it progressed relative to a number of locations that exhibited a defect in initial visual field tests. These results may not hold for a sample with more advanced glaucomatous defects where change in these locations may already be at the end of the testable range of the perimeter.
The proportion of patients with progressive disease did not appear to differ across locations as distinctly as the proportional probabilities. This may be due to the variability of measurements affecting the proportions. Variability may cause locations to appear to change across patients, masking the true proportions of change. The Monte Carlo analysis solves this dilemma: When locations are scrambled, unrelated normal locations may be replaced by worse PDs, resulting in progression in the scrambled-field series. Comparing the actual unscrambled fields to this distribution reveals the likelihood that the observed proportion of progression could be obtained by chance. This method is especially useful in peripheral locations where the variability is greater than in locations closer to fixation. Assessment of these probabilities facilitates the ability to see true relationships between the test locations, as shown in
Figure 8 . Thus, while the proportions of progression may appear homogenous across the field, it is the pattern of the proportional probabilities that allows separation of progression from variability.
The use of Monte Carlo simulation is perhaps the least biased method for determining the relationships between locations with changing PDs. Since each location that was defective was compared to changes in every other location in the visual field, neither neighboring locations nor distal locations were considered differently, but rather they were given an equal a priori likelihood of having a spatial relationship to the defective location in the initial test. The observed spatial relationships “fall out” of the data and are not due to manipulations or assumptions placed on the data. It is noteworthy, therefore, that the observed patterns are similar to the retinal nerve fiber layer patterns calculated from numerous visual fields by Weber et al.,
1 14 since they selected for physiological defects and applied spatial constraints to the functional data when deriving their map, whereas this study did not use any such specific defects or spatial constraints to construct the observed patterns. The patterns of progression found in this study could be improved by stratifying the results by the amount or magnitude of defect in baseline examinations, but this would require a larger number of patients who exhibit the requisite defect depth or defect patterns at baseline.
The results of the Monte Carlo simulation may also, in part, reflect a perimetry-specific component of change in addition to the actual changes due to glaucoma. The SAP full-threshold 24-2 algorithm uses a growth pattern in which the thresholds of seed locations influence the start value of the staircase thresholding procedure in adjacent locations, which in turn can influence the obtained thresholds. Although it is not possible to tease out this perimetry-specific component from the results, it is worth noting the possibility of its existence as it may have utility in the development or improvement of perimetric techniques for tracking progression.
Earlier work defined progression using longitudinal assessment of stereophotographs (Pascual JP, et al.
IOVS 2005;46:ARVO E-Abstract 3712)
16 ; however, the number of patients in our study exhibiting such progression was not enough to allow Monte Carlo analysis. For this reason, the earliest and latest visual field test results in patients with confirmed GON were used. It was hypothesized that the tests bracketed a time frame where true progression occurred in some proportion of patients.
The spatial resolution afforded by the pattern 24-2 grid is not adequate to determine the actual boundaries of progressing regions. The limited spatial resolution of the current grid may miss early signs of progression that occur as a deepening of existing scotomas or as an expansion of a scotoma to locations proximal to existing defects.
17 18 If this area were better quantified, progression might be observed earlier. The results and techniques of this study will be used to guide the development of a new thresholding algorithm, SCotoma Oriented PErimetry (SCOPE) that condenses testing locations in visual field areas that are of interest or are at risk, to provide better tracking of progression in a given individual (Paetzold J, et al.
IOVS 2005;46:ARVO E-Abstract 636).
In conclusion, progressing visual field locations are most likely adjacent to and within a nerve fiber bundle area associated with the visual field defects present in initial tests. Quantifying the spatial relationships of these test locations may be useful in enhancing existing progression algorithms, creating new algorithms, and developing more sensitive, spatially relevant functional tests for follow-up.
Supported by National Eye Institute Grants EY08208 (PAS) and EY 11008 (LMZ). Participant retention incentive grants in the form of glaucoma medication at no cost were provided by Alcon Laboratories, Inc., Allergan, Pfizer, Inc., and Santen, Inc.
Submitted for publication August 14, 2006; revised December 8, 2006; accepted February 16, 2007.
Disclosure:
J.P. Pascual, None;
U. Schiefer, Haag-Streit (F,P,R);
J. Paetzold, Haag-Streit (F);
L.M. Zangwill, Carl-Zeiss Meditec (F,R);
I.M. Tavares, None;
R.N. Weinreb, Carl-Zeiss Meditec (F,R);
P.A. Sample, Carl-Zeiss Meditec (F), Haag-Streit (F), Welch Allyn (F)
The publication costs of this article were defrayed in part by page charge payment. This article must therefore be marked “
advertisement” in accordance with 18 U.S.C. §1734 solely to indicate this fact.
Corresponding author: Pamela A. Sample, Department of Ophthalmology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0946;
psample@glaucoma.ucsd.edu.
Table 1. Characteristics of the Baseline Visual Field Examinations
Table 1. Characteristics of the Baseline Visual Field Examinations
| Mean Deviation (First Baseline)* | Pattern Standard Deviation (First Baseline)* | Proportion of Patients with PD P < 5% across Locations (Mean ± SD) | Average Number of Locations with PD P < 5% across Patients* |
Overall | −1.87 (−5.08, −0.60) | 2.47 (1.93, 5.99) | 0.23 ± 0.07, † | 9 (4–19), † |
Superior | — | — | 0.26 ± 0.06, ‡ | 4 (1–10), ‡ |
Inferior | — | — | 0.20 ± 0.06, ‡ | 4 (1–8), ‡ |
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