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Glaucoma  |   August 2006
Identifying Glaucomatous Vision Loss with Visual-Function–Specific Perimetry in the Diagnostic Innovations in Glaucoma Study
Author Affiliations
  • Pamela A. Sample
    From the Visual Function Laboratory and Hamilton Glaucoma Center, Department of Ophthalmology, University of California at San Diego, La Jolla, California.
  • Felipe A. Medeiros
    From the Visual Function Laboratory and Hamilton Glaucoma Center, Department of Ophthalmology, University of California at San Diego, La Jolla, California.
  • Lyne Racette
    From the Visual Function Laboratory and Hamilton Glaucoma Center, Department of Ophthalmology, University of California at San Diego, La Jolla, California.
  • John P. Pascual
    From the Visual Function Laboratory and Hamilton Glaucoma Center, Department of Ophthalmology, University of California at San Diego, La Jolla, California.
  • Catherine Boden
    From the Visual Function Laboratory and Hamilton Glaucoma Center, Department of Ophthalmology, University of California at San Diego, La Jolla, California.
  • Linda M. Zangwill
    From the Visual Function Laboratory and Hamilton Glaucoma Center, Department of Ophthalmology, University of California at San Diego, La Jolla, California.
  • Christopher Bowd
    From the Visual Function Laboratory and Hamilton Glaucoma Center, Department of Ophthalmology, University of California at San Diego, La Jolla, California.
  • Robert N. Weinreb
    From the Visual Function Laboratory and Hamilton Glaucoma Center, Department of Ophthalmology, University of California at San Diego, La Jolla, California.
Investigative Ophthalmology & Visual Science August 2006, Vol.47, 3381-3389. doi:10.1167/iovs.05-1546
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      Pamela A. Sample, Felipe A. Medeiros, Lyne Racette, John P. Pascual, Catherine Boden, Linda M. Zangwill, Christopher Bowd, Robert N. Weinreb; Identifying Glaucomatous Vision Loss with Visual-Function–Specific Perimetry in the Diagnostic Innovations in Glaucoma Study. Invest. Ophthalmol. Vis. Sci. 2006;47(8):3381-3389. doi: 10.1167/iovs.05-1546.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

purpose. To compare the diagnostic results of four perimetric tests and to identify useful parameters from each for determining abnormality.

methods. One hundred eleven eyes with glaucomatous optic neuropathy (GON), 31 with progressive optic neuropathy (PGON) 53 with ocular hypertension, and 51 with no disease were included (N = 246). Visual field results were not used to classify the eyes. Short-wavelength automated perimetry (SWAP), frequency-doubling technology perimetry (FDT), high-pass resolution perimetry (HPRP), and standard automated perimetry (SAP) were performed. Receiver operating characteristic (ROC) curves were used to compute the areas under the curves (AUC) and sensitivity levels at given specificities for a variety of abnormality criteria. The agreement among tests for abnormality, location, and extent of visual field deficit were assessed.

results. AUC analysis: When the normal group was compared with the GON group, the FDT pattern SD (PSD) area was larger than the HPRP PSD (P = 0.020), and the FDT area of total deviation (TD) <5% was larger than the HPRP mean deviation (MD; P = 0.004). When the normal group was compared with the PGON group, the FDT area of pattern deviation (PD) <5% was larger than the SWAP PSD (P = 0.020). A difference from previous work was that AUCs for PSD or the best SAP were not significantly poorer than those in the function-specific tests. At set specificities, FDT yielded higher sensitivities than all other tests for all parameters. The agreement among tests for abnormality was fair to moderate (κ = 247–0.563). When loss was present on more than one test, the quadrant of the visual field affected was the same in 95% (79/83) of eyes. The number of eyes identified and number of abnormal quadrants increased across groups with increasing certainty of glaucoma.

conclusions. At equal specificity, no single perimetric test was always affected, whereas others remained normal. Several parameters at suggested criterion values provided good sensitivity and specificity. FDT showed the highest sensitivity overall, with SAP performing better than in prior reports. Of note, the same area of the retina was identified as damaged in all tests.

Over the past several years, psychophysical tests of specific visual functions have been used to measure visual performance and to understand the underlying glaucomatous changes in retinal ganglion cell function. Testing vision with standard automated perimetry (SAP) is not selective for a particular ganglion cell type. Any of the primary ganglion cell subtypes can respond to an achromatic incremental stimulus presented on an achromatic background. In contrast, each visual-function–specific perimetric test attempts to isolate a subpopulation of ganglion cells by evaluating a specific visual function characteristically processed by that cell subtype. As an example, short-wavelength automated perimetry (SWAP) elicits detection by the short-wavelength cones. The stimulus information is then processed through the blue–yellow ganglion cells. The amount of isolation is unknown for each of these function-specific tests, with the exception of SWAP, which provides approximately 15 dB of isolation. This means the blue–yellow ganglion cell system would have to lose 15 dB of sensitivity before another cell type could assist in responding to the SWAP stimulus. 1  
Initially, it was hypothesized that the blue–yellow ganglion cells tested by SWAP were parvocellular. 2 3 It was later learned from Dacey 4 and others 5 6 that the blue–yellow cells are small bistratified ganglion cells that project their axons to the koniocellular (interlaminar) layers of the lateral geniculate nucleus (LGN) of the thalamus, rather than to the parvocellular layers. 7 Frequency-doubling technology perimetry (FDT) 8 9 and various forms of motion perimetry 10 11 12 13 14 attempt to target the magnocellular (also known as parasol) ganglion cells that project to the magnocellular layers of LGN, and high-pass resolution perimetry (HPRP) 15 16 is thought to assess the parvocellular (also known as midget) ganglion cells that project to the parvocellular layers of LGN. Recent reviews detail the evidence supporting anatomic and functional segregation of three primary pathways (parvocellular, magnocellular, and koniocellular) through the LGN. 17 18 Anatomic projections to cortex and functional preferences within cortical layers are much less segregated. 18 19 In addition, the relationship of visual function to underlying visual pathways is based primarily on electrophysiology in healthy primates and on lesion studies, 17 and so some caveats apply in the application of visual-function–specific perimetry to ganglion cell assessment (see the Discussion section). 
Several studies comparing one visual-function–specific test to SAP have shown that function-specific tests are superior to SAP for early detection of vision loss associated with glaucoma. 20 21 22 23 There are some problems with these studies (see the Discussion section). Very few have compared more than one visual-function–specific test in the same patient population. 24 25 26 We first made such a comparison 6 years ago and evaluated SAP, SWAP, motion automated perimetry (MAP), and FDT in 71 eyes with glaucomatous optic neuropathy, 37 ocular hypertensive eyes, and 28 age-matched normal control eyes. 24  
For detection of functional loss, it was found that (1) standard visual field testing was not optimal, missing 54% of eyes with glaucomatous optic neuropathy (GON); (2) a combination of two or more tests improved detection of functional loss; (3) in an individual, the same retinal location was damaged, regardless of the visual function tested; and (4) SWAP, MAP, and FDT showed promise as early indicators of function loss in glaucoma. 
One limitation of this previous study was that the normal eyes had normal SAP visual fields and we had to adjust specificities based on other published reports. This limitation does not exist in the present study. A second limitation was the testing of only the koniocellular and magnocellular pathways and the lack of a function-specific test preferred by the parvocellular pathway. We have since obtained sufficient data with HPRP, and in this current study, we to compared it to SWAP, FDT, and SAP in the same individuals. 
Methods
All participants were selected from the ongoing longitudinal Diagnostic Innovations in Glaucoma Study (DIGS), conducted at the Hamilton Glaucoma Center at the University of California at San Diego (UCSD). This study is prospectively designed to assess structure and function in glaucoma. DIGS patients are followed annually. Informed consent was obtained from all participants after the nature and procedures of the study were explained. The Institutional Review Board of the University of California at San Diego approved the study, which adheres to the tenets of the Declaration of Helsinki. 
Inclusion Criteria for DIGS.
Participants underwent complete ophthalmic examinations including slit lamp biomicroscopy, intraocular pressure (IOP) measurement, and dilated stereoscopic fundus examination. Simultaneous stereoscopic photographs were obtained for all participants and had to be of adequate quality for the subject to be included. All participants had open angles, a best corrected acuity of 20/40 or better, a spherical refraction within and inclusive of ±5.0 D (transposition allowed), and cylinder correction within ±3.0 D. A family history of glaucoma was allowed. 
Exclusion Criteria for DIGS.
Normal and ocular hypertensive participants were excluded if they had a history of intraocular surgery (except for uncomplicated cataract surgery). We also excluded all participants with nonglaucomatous secondary causes of elevated IOP (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), with medications known to affect visual field sensitivity, or with problems other than glaucoma affecting color vision (as assessed by the Farnsworth D-15 color vision test). 
DIGS participants are not chosen based on any criteria other than those specified herein. Most of the patient participants came to the study through the Glaucoma Service at the UCSD Department of Ophthalmology. Normal subjects were recruited from the general population through advertisement as well as from the staff and employees of the University of California San Diego. 
The Current Study.
All eligible DIGS participants with reliable visual field results on all four tests defined as ≤25% false positives, false negatives, and fixation losses were included. All tests were performed in randomized order and completed within a 3-month period. All possible orders of the four tests were determined, and participants were assigned to an order sequentially on arrival for study. One eye was selected randomly from each subject, except in cases in which only one eye met study criteria, and then that eye was included. 
Participants.
Two hundred forty-six eyes from 246 participants were evaluated on all three visual-function–specific perimetry tests as well as on SAP. Criteria for classification are detailed later. 
Diagnostic categories were based on simultaneous stereoscopic color photographs (TRC-SS camera; Topcon America Corp., Paramus, NJ), obtained after maximal pupil dilation. All photograph evaluations were taken using a stereoscopic viewer (Stereo Viewer II; Asahi Pentax, Golden, CO) illuminated with color-corrected fluorescent lighting. Two trained and masked graders from the UCSD Optic Disc Reading Center assessed each photograph independently. Inconsistencies between the two graders’ evaluations were resolved by consensus or through adjudication by a third evaluator. GON was identified by evidence of any of the following: excavation, neuroretinal rim thinning or notching, nerve fiber layer defects, or asymmetry of the vertical cup-to-disc ratio ≥0.2 between the two eyes. 
To identify progressive glaucomatous optic neuropathy, two trained graders independently compared the first and last photographs in each participant’s series. Graders were masked to all other participant information, photograph date and test results. Photographs were graded A equal to B, A worse than B, or A better than B. Inconsistencies between the two graders’ evaluations were resolved through adjudication by a third evaluator for each pair of photographs. After consensus was reached, the temporal sequence of the photographs was unmasked. Progression was defined based on evidence of increasing excavation, rim thinning or enlarging of notches or nerve fiber layer defects in the later photograph. Changes in rim color and the presence of disc hemorrhage or progressive parapapillary atrophy were not sufficient for characterization of progression. The time frame for progression on photographs ranged from 2 to 12 years. 
Classification of Study Groups
The 246 participants were placed in one of four diagnostic categories based on the consensus appearance of the optic disc on stereophotographs, the ocular examination results, and their IOP history. Visual fields were not used to classify the participants. This classification resulted in 111 eyes with GON, 31 with progressive GON (PGON), 53 with ocular hypertension (OHT), and 51 with normal eyes, serving as the control. We included PGON as it provides strong evidence of glaucoma, when a nonfunctional measure is used. 27  
Normal Control Subjects.
Normal eyes had IOP <23 mm Hg with no history of increased IOP, normal findings in an ocular examination, and normal optic discs according to the criteria. 
Ocular Hypertension.
Ocular hypertensive eyes had normal optic discs and highest IOPs ≥23 mm Hg on at least two separate occasions. 
Glaucomatous Optic Neuropathy.
These participants had evidence of glaucomatous-appearing optic discs on stereophotographs with or without elevated IOP. 
Progressive Glaucomatous Optic Neuropathy.
These participants showed progressive GON on evaluation of serial photographs sometime during their follow-up in DIGS before the visual field dates used in this analysis. 
Psychophysical Tests of Function
Four perimetric procedures were used to test visual function. All procedures were tested within the central 30° of visual field and necessitated fixation by the patient. Proper refraction was provided for each device. All required a 3-mm or larger pupil. Dilation was used if necessary. Lids of eyes with potential ptosis were taped to reduce artifacts. 
Standard Achromatic Automated Perimetry.
In SAP, a small (0.47°) 200-ms flash of white light is presented as the target on a dim background (10 cd/m2 or 31.5 asb). The target was randomly presented to 54 locations within the central 24° of visual field using the Humphrey Visual Field Analyzer II (Carl Zeiss Meditec, Dublin, CA), program 24-2, software version 3.4.7, and the SITA testing algorithm. The two locations just above and below the blind spot were not included in the analysis. Figure 1shows all target locations in all four tests. Also shown are the abnormal locations for each test in the same example eye with GON. 
Short-Wavelength Automated Perimetry.
SWAP was measured with the same HFA perimeter as SAP (software version 3.4.7, and the 24-2 program), but with the full threshold (FT) strategy. 1 In SWAP, a 440-nm, narrow-band, 1.8° target is presented at 200-ms duration on a bright 100 cd/m2 yellow background and selectively tests the short-wavelength–sensitive cones and their connections. 
Frequency-Doubling Technology Perimetry.
FDT was measured with the frequency-doubling visual field instrument (Carl Zeiss Meditec, Dublin, CA) using Welch-Allyn technology (Skaneateles Falls, NY) and the N-30 program, software version 3.00.1. The targets consist of a 0.25-cyc/deg sinusoidal grating that undergoes a 25-Hz counterphase flicker. The test involves a modified binary search staircase threshold procedure with stimuli presented for a maximum of 720 ms. FDT measures the contrast needed for detection of the stimulus. Each grating target is a square subtending approximately 10° in diameter. Targets are presented in one of 18 test areas located within the central 20° radius of the visual field temporally and 30° nasally. 
High-Pass Resolution Perimetry.
In HPRP, ring-shaped vanishing optotypes which vary in size are used to assess resolution ability in the central 30° of the visual field. 15 The optotypes used in HPRP are high-spatial-frequency filtered targets where the inner and outer portions of the rings are darker (15 cd/m2), whereas the center portion of the rings is brighter (25 cd/m2). The space-averaged luminance of the entire ring is equal to the luminance of the background (20 cd/m2). Therefore, when the edges of the ring cannot be resolved, the rings blend into the background, that is, the targets are either resolved (seen) or they are invisible. The target consists of rings of different sizes, presented at 50 locations within the central 30°. No stimuli are presented within the central 5° of the visual field (Fig. 1) . The subject responds when the target is large enough to resolve. We measured HPRP with the Ophthimus High-Pass Resolution Perimeter, version 2.0, software version 2.51 (HighTech Vision, Malmö, Sweden). 
Statistical Analyses
Continuous variables were compared by using ANOVA overall, with between-test comparisons conducted with the Tukey honestly significantly different (HSD) test with α set at 0.05. The χ2 analysis was used to assess agreement of categorical variables. 
Visual field parameter data were derived by comparison to the manufacturer’s internal normative database for SAP, FDT, and HPRP and to our laboratory’s normative database for SWAP (n = 345, one eye per subject), which was developed in collaboration with Chris Johnson for the SWAP ancillary arm of the Ocular Hypertension Treatment Study after it was suspected that the manufacturer’s internal normative database for SWAP-full threshold testing was inaccurate. Soliman et al. 28 have confirmed the inaccuracy. The inclusion–exclusion criteria were the same as for our normal control group, but none of our control subjects were included in the SWAP normative database. 
Abnormality.
Receiver operator characteristic (ROC) curves were generated for the visual field parameters shown in Table 1 , by comparing normal and patient eyes with GON and then again comparing normal and patient eyes with PGON. The area under these curves (AUCs) were compared statistically for the best-performing parameter from each test and for the pattern SD (PSD), which performed well in all tests, using the method of DeLong et al. 29 Theirs is a nonparametric method used to compare correlated AUC (that is, AUC for different tests obtained in the same group of individuals). It is based on a Mann-Whitney statistic and has been widely used for this purpose in the medical literature. 27 30  
The ROC results were then used to select criterion values for various parameters to provide specificities of 80% and 90% in the normal control group, to equate somewhat the tests for diagnosing abnormality, because each test presents different stimuli and targets different test locations to assess different visual functions. Sensitivity for diagnosis of glaucoma was then computed based on these criterion values. The percentage of OHT eyes identified as abnormal by each test according to the specified criterion values for the most sensitive parameters was also determined. 
Agreement among Tests.
We evaluated the agreement among the tests to determine the percentage of eyes that were classified the same, by the various combinations of tests, based on the pattern deviation (PD) <1% (or deep dent for HPRP) criterion number of points that yielded 80% specificity. The developer of HPRP, Lars Frisen supplied the following explanation of dents (personal communication, 2005). “Dents for HPRP were identified by sets of linear regressions. One set ran over test locations that could be viewed as members of one and the same field radius. The other set ran over test points that normally share much the same value, i.e., they normally belong to one and the same isopter. Normally, test results should rise monotonically along radii, and should stay constant along isopters. The regressions helped identify test locations that deviated by 1-l.9 dB from expected (= shallow dent), or by 2 dB or more (= deep dent). Hence, the analysis essentially aimed to highlight deviations from the normal shape of the threshold surface.” 
A χ2 analysis was used to determine the statistical significance of this agreement in pairs of tests. Kappa statistics were used and significance assessed by using the method of Landis and Koch. 31  
Location of Field Defect.
The stimuli and test locations differ among the four tests. For this reason, we assessed the agreement in the location of the defect by quadrant. We first determined whether the field was abnormal based on the criterion number of PD points at 1% determined from the ROC analysis for 80% specificity (shaded criteria in Table 1 ). Once it was determined that a field had a sufficient number of PD locations at ≤1% to be called abnormal, a quadrant was called abnormal if it contained any one of these PD points (deep dents for HPRP). HPRP presents stimuli centered on the vertical and horizontal meridians. When one of these points (which overlap two quadrants) was abnormal, we attributed it to the most defective quadrant. 
Extent of Defect.
The number of quadrants affected on each perimetric test gave the extent of the defect. Overall significant differences among all tests were further assessed with paired comparisons using the Tukey HSD post hoc test. 
Results
The major purpose of this study was to evaluate each of the visual field procedures against the others. We want to emphasize again that SAP fields were not used for the classification of any study group, to prevent bias. However, for the reader’s information, Table 2gives the descriptive results for the participants in each of the four groups. The means, standard deviation, and range of mean defect (MD) and pattern SD (PSD) are given for SAP to provide an idea of the range of disease for study participants. None of the PGON and only 7% of the GON were in advanced stages based on a modification of the criteria for visual field severity of Hodapp et al. 32 The GON and PGON groups showed comparable early and moderate levels of severity. The normal subjects were significantly younger than the patients with GON. For this reason, only the age-corrected parameters from Statpac (Carl Zeiss Meditec, Inc.) were evaluated. All four tests were completed within a median time of 0.25 months (range, 0–6.33). 
Abnormality.
Table 1gives the AUCs comparing normal subjects with patients having GON and with those having PGON. The more stringent criteria for glaucoma, PGON, resulted in larger AUCs, but the relationships among the four tests were comparable for the PGON and GON groups. To avoid numerous multiple comparisons, statistical comparisons of AUC shown in Table 1were made (1) comparing the AUC for PSD from each test and (2) comparing the best performing parameter from each of the four tests for both the GON (bold) and PGON (italic) definitions of glaucoma. The results of these comparisons are shown in Table 3 . FDT consistently showed the largest AUC regardless of the visual field parameter. For PSD in the GON group, the FDT AUC was 0.770, followed by SWAP (0.733), SAP (0.713), and HPRP (0.661). In the PGON group, FDT AUC was 0.875, followed by HPRP (0.780), SWAP (0.775), and SAP (0.762). The FDT AUC was significantly larger than only the HPRP AUC (P = 0.020) and only in the GON group. 
Using the best parameter from each test, FDT TD at 5% AUC (0.795), SWAP PSD (0.733), SAP PSD (0.713), and HPRP MD (0.670), showed that the FDT AUC was significantly larger than was the HPRP MD (P = 0.004) in the GON group and than the SWAP PSD (P = 0.020) in the PGON group. No other comparisons reached significance, including comparisons between FDT and SAP or SWAP and SAP. 
In addition to the AUC, the shape of the ROC curve is important. There is a tradeoff between sensitivity and specificity. For this reason, we chose two different specificities for analysis. The ROC curve results were used to determine criterion values for various parameters at specificities near 80% and 90%. Using this step-wise approach proceeding from the ROC analysis to determining the desired specificity can assist in developing comparable criteria for abnormality for each of the four tests. The PGON results are shown in Table 1 . Sensitivities were slightly lower in the GON group, but again the relationship among the test results was comparable to that in the PGON group. FDT showed the highest sensitivity in all cases. With this approach, a possible advantage to FDT testing for detection of vision loss in participants with PGON or GON emerges at both specificities and for all parameters. Table 4shows the percentage of OHT eyes classified as abnormal when using these same criterion values. 
Table 5shows how the tests compared in separating participant groups when specificity was set to 80% for the parameters PSD (top) or number of PD plot locations triggered at P < 1% (bottom). It is important to note that the percentage of eyes classified as abnormal by only one test was high in the normal (39%) and OHT groups (36%) using the PD plot criterion (top), but quite reasonable when two or more tests are required, 4% and 8%, respectively. The percentage of eyes determined to be normal by all tests decreased in the expected direction from 53% in the normal subjects through 49% in OHT, 27% in GON to only 16% in the PGON group. These relationships were similar for the criteria PSD (Table 5 ; bottom). 
Agreement among Tests.
Figures 2(PSD) and 3(PD) show the agreement among the four tests in identifying abnormality in eyes with GON and PGON combined (n = 142), using the 80% specificity criterion values. Table 6gives the κ statistics, proportion agreement, and strength of agreement for these relationships. 31 Agreement is fair to moderate with better agreement found for some pairs of tests (SWAP and FDT; SWAP and HPRP) when using the PD plot criteria compared with the PSD. 
Location of Field Defect.
Eighty-three participants were considered abnormal on two or more tests based on the PD criteria at 80% specificity: 4 (8%) of 51 normal, 8 (15%) of 53 OHT, 54 (49%) of 111 GON, and 17 (55%) of 31 PGON subjects. At least one quadrant was found to be commonly defective in 79 (95%) of the 83 eyes, regardless of whether two, three, or four tests had abnormal quadrants. 
Extent of Defect.
Table 7shows the number of abnormal quadrants for each participant group and each test based on the PD criteria at 80% specificity. Within diagnostic groups, a significant difference in the normal group was attributed to FDT compared with each of the other three tests. Within a perimetric test the differences were significant (P < 0.0001) and were attributable to differences between normal subjects and the GON and PGON groups for all tests and between the OHT and the GON and PGON groups for all tests. No differences were found between normal and OHT or between GON and PGON. 
Discussion
In this study, we found that no one test type always resulted in abnormal findings, whereas others remained normal in eyes with glaucoma. We also found that although FDT consistently showed the largest AUC, the areas were not significantly different from the other tests, except that the FDT area was larger than that in HPRP (PSD comparison) in the GON group, FDT TD at 5% area was larger than HPRP MD (best parameter from each) in the GON group and FDT PD5% was larger than SWAP PSD (best parameter from each) in PGON group. FDT showed higher sensitivities at set specificities for all parameters. An example can be seen in Table 1looking at the TD values at 5% and specificities near 80%. In this case, FDT has a sensitivity of 84% compared with SWAP at 42%, SAP at 68%, and HPRP small dent at 23%, consistent with the results from our earlier study. 24  
There were several important improvements in this study compared with those in our original study. 24 First, visual fields were not used to classify any of the study participants. This avoids bias in two ways. For example, if SAP is used as a gold standard along with GON to define normal and glaucoma, no other test could ever exceed it in sensitivity and specificity by definition. In our original study, normal participants were required to have normal SAP examinations causing difficulties in setting all tests for equal specificities. We had to use other published data to set criterion values. Not requiring SAP defects to classify participants in the present study allowed SAP deficits to precede those of the function specific tests, or vice versa, without constraint. 
The second improvement in this study was the inclusion of HPRP, a possible parvocellular cell test. Although magnocellular and parvocellular cell functions overlap greatly, 33 there is a range for both cell types where one is significantly more likely to mediate detection than the other if the parameters of the visual stimulus are properly designed. 18 The high-resolution nature of HPRP is most likely handled by the parvocellular cells, especially in the central visual field. The amount of parvocellular cell damage needed before magnocellular cells can take over detection of the HPRP stimulus (the amount of parvocellular cell isolation), however, is unknown. 
The third improvement was the inclusion of a group of eyes with progressive GON. Glaucoma is defined as a progressive optic neuropathy. By design we did not use visual field loss to help verify the presence of glaucoma in eyes with recently identified or long-term stable GON. Therefore, evidence of PGON provided a subgroup that most surely had glaucoma. 27 The results of this group, even though few in number and therefore lower in power, were consistent with those in the larger GON group, thereby strengthening the conclusions drawn. 
When new tests are developed, it is problematic to determine what criteria are best for separating healthy from diseased eyes. A variety of methods have been used. For example, the modified Statpac criteria (Carl Zeiss Meditec, Inc.) for SWAP are based on those developed for SAP. 34 Often, different criteria are developed and used by different investigators. An example of this is the number of different criteria for evaluating abnormality on FDT (reviewed in Muskens et al. 35 ). A strength of the present study was the use of ROC analysis to set criteria for each perimetric procedure and to equate the tests for specificity. In clinical practice, each instrument uses its own internal normative database. It seemed most clinically relevant to use these databases and to assess specificities for comparisons among tests using an independent group of healthy eyes. 
A limitation to our study was that we did not have confirming visual field results for all test types, and so this criterion was not included. If a test is overcalling abnormality, it suggests a higher sensitivity for that test than is the actual case. In this real false-positive situation, a repeat would most likely not confirm the abnormal result. Therefore, requiring two abnormal test results in a row should improve specificity and give a truer measure of the test’s sensitivity to glaucomatous loss. In our study, this could influence the results if one test is more likely to overcall abnormality than the others. For example, the percentage of OHT eyes classified as abnormal was higher for FDT (45%) at 80% specificity than for the other tests. This percentage is higher than the number that would be expected to convert to glaucoma, a result similar to our earlier finding. 24 At 90% specificity, the result (26%) is more reasonable. There has been a case report showing that FDT results fluctuate with changes in IOP, 36 but none of our participants had the high IOP noted in that study. In FDT’s favor, Spry et al. 26 found little difference in confirmation of field results for FDT (47%) compared with SWAP (42%), and the small difference seen was in the direction opposite that expected if FDT was simply overcalling abnormality in these OHT eyes. To address partially the lack of confirmation on a specific test type, results from two different test types can be used to verify the presence of glaucomatous vision loss. In this situation we found that only a small number of normal eyes were identified as abnormal when at least two tests were required (Table 5)
Our study also does not address progressive visual field loss. Additional study is needed to determine the relative ability of each test type to follow the disease once visual field loss is already present. 
The results of the present study point out a difference from earlier findings, 24 where we found that SWAP-FT and FDT-N30 outperformed SAP-FT. In the present study, this difference was not found. It may be that the introduction of the SITA thresholding algorithm for SAP, with its tighter confidence limits, has improved SAP’s ability to detect abnormality relative to the longer and more variable SWAP full threshold. It remains to be seen if the newly released SITA version of SWAP will improve its performance in a similar manner. This result also highlights one of the ongoing problems in clinical research. The technologies are changing so rapidly that it is difficult to obtain data and report results on a device before it has moved on to the next generation with improvements. For glaucoma, this problem exists both with measures of visual function and with assessment of the optic nerve and retinal nerve fiber layer. Ongoing re-evaluation with patients transitioning from one version or test to another is critical to giving a complete picture of how these new instruments or procedures will best alter clinical practice. 
An additional reason for the improvement in SAP performance relative to SWAP may be that none of the participants in this study were selected based on their SAP visual field results. As we have mentioned, our previous study included only normal control subjects with normal SAP fields, and specificity criterion values were derived from the literature. SAP was not used for this in the present study and the same normal subjects were tested on all four tests and used to set the specificity criterion values, removing any possible bias due to different normal subjects used for the specificity of each test. 
This study found that patients could show deficits on any one of the four perimetric procedures, while remaining normal on some or all the other three. In addition, the results were similar to those in earlier work in that agreement on abnormality among tests was only fair to moderate. These two findings together are consistent with the conclusion that no one ganglion cell subtype is always affected first in glaucoma. 24 26 37 All optic nerve fibers are damaged, but tests that favor detection of a stimulus by one visual pathway or processing subsystem reduce the ability of the visual system to use other pathways to compensate for the damaged retinal ganglion cell type under test. 2 3 These findings are consistent with those from an elegant study in primate animal models of glaucoma. Harwerth et al. 38 combined psychophysics, electrophysiology, anatomy, and histochemistry to show that glaucomatous atrophy causes a nonselective reduction of metabolism of magnocellular and parvocellular neurons in the afferent visual pathway. Yucel et al. 39 also find no evidence for selective cell loss in glaucoma within the magnocellular, parvocellular, or koniocellular layers of the LGN. 39 However, not all eyes with primary open-angle glaucoma or those at risk of the disease are affected in the same way. Perhaps blue–yellow ganglion cell function is reduced first in one individual, parvocellular in another individual, and magnocellular ganglion cell function in another. 24 40 The inclusion of HPRP did not alter this conclusion. The study also replicated our earlier finding that when two or more test results are abnormal, the same quadrant of the visual field is affected on all. This finding is very important for using two different tests to verify the presence of abnormality and for targeting follow-up by careful monitoring of specific areas of the visual field. 
In summary, no one test type was always affected in patients with GON or PGON, whereas the other test types remained normal. Several parameters were identified that provided good sensitivity at set specificities. The FDT N-30 test showed the highest sensitivity for all parameters. SAP performance was equal to or slightly better than SWAP and not significantly different from FDT, a finding that differs from those in previous studies. Consistent with previous findings, the same quadrant of the retina is damaged for all affected tests. A combination of test types may be most efficient in identifying early loss and confirming the area of the retina affected by glaucoma. 
 
Figure 1.
 
Examples of visual field pattern deviation display for SAP, SWAP, FDT, and the small and deep dent display for HPRP in a patient with GON. Each plot shows the location and number of stimulus test locations as designated by either a box or a dot. Dot: within normal limits. The shading in the boxes denotes the probability of abnormality relative to the internal normative database of each device. Probabilities are shown in the corresponding key.
Figure 1.
 
Examples of visual field pattern deviation display for SAP, SWAP, FDT, and the small and deep dent display for HPRP in a patient with GON. Each plot shows the location and number of stimulus test locations as designated by either a box or a dot. Dot: within normal limits. The shading in the boxes denotes the probability of abnormality relative to the internal normative database of each device. Probabilities are shown in the corresponding key.
Table 1.
 
AUC and Standard Error for Groups with GON and PGON
Table 1.
 
AUC and Standard Error for Groups with GON and PGON
Parameter GON PGON
AUC SE AUC SE Criterion Sens/Spec (%) Criterion Sens/Spec (%)
SAP PSD 0.713 0.041 0.762 0.056 2.31 48/90 1.93 dB 52/80
SWAP PSD 0.733 0.041 0.775 0.052 4.48 45/90 3.75 dB 48/80
FDT PSD 0.770 0.036 0.875 0.041 4.76 68/90 4.36 dB 71/80
HPRP PSD 0.661 0.043 0.780 0.054 0.87 52/90 0.8 dB 65/80
SAP TD 5% 0.708 0.041 0.758 0.060 17 45/90 9 68/80
SAP TD 1% 0.711 0.036 0.797 0.051 4 55/90 2 65/80
SWAP TD 5% 0.641 0.046 0.646 0.062 23 23/90 14 42/80
SWAP TD 1% 0.659 0.044 0.696 0.061 11 32/92 4 48/82
FDT TD 5% 0.795 0.033 0.880 0.044 4 71/90 3 84/78
FDT TD 1% 0.763 0.028 0.820 0.047 1 68/90 N/A
HPRP Small dent 0.583 0.047 0.604 0.063 8 16/94 7 23/88
HPRP Deep dent 0.652 0.041 0.759 0.054 4 32/92 3 42/86
SAP PD 5% 0.704 0.041 0.734 0.058 11 39/92 9 55/80
SAP PD 1% 0.669 0.039 0.733 0.058 5 39/92 3 39/86
SWAP PD 5% 0.710 0.044 0.727 0.057 11 32/90 7 42/84
SWAP PD 1% 0.684 0.041 0.731 0.059 5 36/92 3 48/84
FDT PD 5% 0.763 0.036 0.891 0.043 5 74/98 4 84/86
FDT PD 1% 0.741 0.031 0.818 0.048 2 58/96 1 71/84
SAP MD 0.601 0.045 0.731 0.069 −2.22 55/90 −1.73 dB 65/80
SWAP MD 0.601 0.045 0.587 0.069 −7.07 29/90 −5.42 dB 42/80
FDT MD 0.755 0.037 0.813 0.054 −2.37 61/90 −1.48 dB 74/80
HPRP MD 0.670 0.044 0.638 0.064 2.67 19/90 1.82 dB 39/80
Table 2.
 
Summary Data for Each of the Four Participant Groups
Table 2.
 
Summary Data for Each of the Four Participant Groups
Normal (n = 51) OHT (n = 53) GON (n = 111) PGON (n = 31)
Age (y)
 Mean ± SD 51.81 ± 13.70O,G,P 60.27 ± 11.61N,G,P 65.59 ± 11.42N,O 66.85 ± 10.57N,O
 Range 25.75 to 81.33 23.29 to 79.96 33.21 to 87.47 40.01 to 81.96
Highest IOP (mm Hg)
 Mean ± SD 16.08 ± 3.03O,G,P 29.87 ± 7.16N,G 25.44 ± 7.36N,O,P 30.26 ± 9.47N,G
 Range 10 to 23 13 to 59 14 to 54 16 to 56
SAP MD (dB)
 Mean ± SD −0.72 ± 1.17G,P −0.36 ± 1.38G,P −2.89 ± 3.97N,O −3.19 ± 3.36N,O
 Range −4.05 to 0.95 −4.08 to 3.13 −20.14 to 2.04 −12.41 to 1.52
SAP PSD (dB)
 Mean ± SD 1.72 ± 0.68G,P 1.61 ± 0.69G,P 3.62 ± 3.58N,O 4.16 ± 3.97N,O
 Range 1.05 to 4.46 0.90 to 4.2 1.02 to 16.11 1.33 to 14.19
Table 3.
 
Statistical Comparison of ROC Area for PSD and for the Best Parameter from Each Test
Table 3.
 
Statistical Comparison of ROC Area for PSD and for the Best Parameter from Each Test
A. PSD
GON GON P PGON P
SAP vs. HPRP 0.291 0.762
SAP vs. FDT 0.197 0.076
SAP vs. SWAP 0.633 0.813
HPRP vs. FDT 0.020 0.135
HPRP vs. SWAP 0.193 0.939
FDT vs. SWAP 0.432 0.067
B. Best parameter from each test
GON P PGON P
SAP PSD vs. HPRP MD 0.434 SAP TD 1% vs. HPRP PSD 0.795
SAP PSD vs. FDT TD 5% 0.069 SAP TD 1% vs. FDT PD 5% 0.090
SAP PSD vs. SWAP PSD 0.633 SAP TD 1% vs. SWAP PSD 0.677
HPRP MD vs. FDT TD 5% 0.004 HPRP PSD vs. FDT PD 5% 0.097
HPRP MD vs. SWAP PSD 0.240 HPRP PSD vs. SWAP PSD 0.939
FDT TD 5% vs. SWAP PSD 0.182 FDT PD 5% vs. SWAP PSD 0.020
Table 4.
 
Percentage of Ocular Hypertensives Called Abnormal by Each Test
Table 4.
 
Percentage of Ocular Hypertensives Called Abnormal by Each Test
Specificity 80% Specificity 90%
PSD PD < 1% PSD PD < 1%
SAP 18.9% 15.1% 9.4% 9.4%
HPRP 24.5% 20.8% 15.0% 11.3%
FDT 45.3% 30.2% 26.4% 15.1%
SWAP 18.9% 13.2% 15.1% 9.4%
Table 5.
 
The Number of Eyes and Percentage with Number of Visual Field Types Showing Abnormal Results
Table 5.
 
The Number of Eyes and Percentage with Number of Visual Field Types Showing Abnormal Results
Number of Abnormal Results PGON (n = 31) GON (n = 111) OHT (n = 53) Normal (n = 51)
PD Plot
 All 4 8 (26) 24 (22) 3 (6) 0 (0)
 3 of 4 3 (10) 15 (14) 1 (2) 2 (4)
 2 of 4 6 (19) 15 (14) 4 (8) 2 (4)
 1 of 4 9 (29) 27 (24) 19 (36) 20 (39)
 SAP only 1 7 1 4
 SWAP only 1 3 3 6
 FDT only 6 14 10 6
 HPRP only 1 3 5 4
 None 5 (16) 30 (27) 26 (49) 27 (53)
PSD
 All 4 10 (32) 31 (28) 4 (7) 1 (2)
 3 of 4 3 (10) 17 (15) 2 (4) 2 (4)
 2 of 4 8 (26) 16 (15) 7 (13) 6 (12)
 1 of 4 8 (26) 30 (27) 21 (40) 18 (35)
 SAP only 0 5 0 3
 SWAP only 1 6 3 4
 FDT only 4 12 13 5
 HPRP only 3 7 5 6
 None 2 (6) 17 (15) 19 (36) 24 (47)
Figure 2.
 
A Venn diagram showing the overall agreement among SAP, SWAP, FDT, and HPRP in the GON and PGON groups combined (n = 142). Abnormality was based on the PSD and a specificity of 80%.
Figure 2.
 
A Venn diagram showing the overall agreement among SAP, SWAP, FDT, and HPRP in the GON and PGON groups combined (n = 142). Abnormality was based on the PSD and a specificity of 80%.
Figure 3.
 
A Venn diagram showing the overall agreement among SAP, SWAP, FDT, and HPRP in the GON and PGON groups combined (n = 142). Abnormality is based on the PD plot results at the 1% level and a specificity of 80%.
Figure 3.
 
A Venn diagram showing the overall agreement among SAP, SWAP, FDT, and HPRP in the GON and PGON groups combined (n = 142). Abnormality is based on the PD plot results at the 1% level and a specificity of 80%.
Table 6.
 
The κ Statistics and Levels of Agreement for All Combinations of Test Pairings in the GON and PGON Groups Combined
Table 6.
 
The κ Statistics and Levels of Agreement for All Combinations of Test Pairings in the GON and PGON Groups Combined
κ SE Proportion Agreement Strength of Agreement
PD Plot
 SAP and SWAP 0.586 0.07 0.79 Moderate
 SAP and FDT 0.393 0.07 0.69 Fair
 SAP and HPRP 0.375 0.08 0.69 Fair
 SWAP and FDT 0.411 0.07 0.69 Moderate
 SWAP and HPRP 0.470 0.07 0.74 Moderate
 FDT and HPRP 0.326 0.07 0.64 Fair
PSD
 SAP and SWAP 0.563 0.07 0.78 Moderate
 SAP and FDT 0.349 0.07 0.67 Fair
 SAP and HPRP 0.394 0.08 0.69 Fair
 SWAP and FDT 0.366 0.07 0.68 Fair
 SWAP and HPRP 0.268 0.08 0.63 Fair
 FDT and HPRP 0.247 0.08 0.62 Fair
Table 7.
 
Number of Abnormal Quadrants for Each Group and Test
Table 7.
 
Number of Abnormal Quadrants for Each Group and Test
Normal (n = 51) OHT (n = 53) GON (n = 111) PGON (n = 31) ANOVA P
SAP 0.76 ± 0.89 0.55 ± 0.89 1.38 ± 1.25 1.61 ± 1.20 <0.001
SWAP 0.69 ± 0.99 0.49 ± 0.78 1.21 ± 1.05 1.39 ± 1.15 <0.001
FDT 0.20 ± 0.49 0.42 ± 0.75 1.09 ± 1.13 1.32 ± 1.11 <0.001
HPRP 0.57 ± 0.88 0.66 ± 0.92 1.13 ± 1.14 1.42 ± 0.96 <0.001
P 0.004 0.4889 0.2445 0.7569
SamplePA, JohnsonCA, Haegerstrom-PortnoyG, AdamsAJ. Optimum parameters for short-wavelength automated perimetry. J Glaucoma. 1996;5:375–383. [PubMed]
JohnsonCA. Selective versus non-selective losses in glaucoma. J Glaucoma. 1994;3:S32–S44. [PubMed]
SamplePA, MadridM, WeinrebRN. Evidence for a variety of functional defects in glaucoma suspect eyes. J Glaucoma. 1994;3:S5–S18. [PubMed]
DaceyDM. Morphology of a small-field bistratified ganglion cell type in the macaque and human retina. Vis Neurosci. 1993;10:1081–1098. [CrossRef] [PubMed]
DaceyDM, LeeBB. The ‘blue-on’ opponent pathway in primate retina originates from a distinct bistratified ganglion cell type. Nature. 1994;367:731–735. [CrossRef] [PubMed]
DaceyDM, PackerOS. Colour coding in the primate retina: diverse cell types and cone-specific circuitry. Curr Opin Neurobiol. 2003;13:421–427. [CrossRef] [PubMed]
MartinPR, WhiteAJ, GoodchildAK, WilderHD, SeftonAE. Evidence that blue-on cells are part of the third geniculocortical pathway in primates. Proc Natl Acad Sci USA. 1997;27:5900–5905.
MaddessT, HenryGH. Performance of nonlinear visual units in ocular hypertension and glaucoma. Clin Vis Sci. 1992;7:371–383.
JohnsonCA, DemirelS. The role of spatial and temporal factors in frequency-doubling perimetry.WallM HeijlA eds. Perimetry Update 1996/97: Proceedings of the XIIth International Perimetric Society Meeting. 1997;13–19.Kugler Amsterdam.
FitzkeFW, PoinoosawmyD, NagasubramanianS, HitchingsRA. Peripheral displacement thresholds in glaucoma and ocular hypertension.HeijlA eds. Perimetry Update 1988/89. 1989;399–405.Kugler Amsterdam.
TrickGL, SteinmanBS, AmyotM. Motion perception deficits in glaucomatous optic neuropathy. Vision Res. 1995;25:2225–2233.
WallM, JennischCS, MundenPM. Motion perimetry identifies nerve fiber bundle-like defects in ocular hypertension. Arch Ophthalmol. 1997;115:26–33. [CrossRef] [PubMed]
BosworthCF, SamplePA, WeinrebRN. Perimetric motion thresholds are elevated in glaucoma suspects and glaucoma patients. Vision Res. 1997;37:1989–1997. [CrossRef] [PubMed]
SilvermanSE, TrickGL, HartWM, Jr. Motion perception is abnormal in primary open-angle glaucoma. Invest Ophthalmol Vis Sci. 1990;31:722–729. [PubMed]
FrisenL. High-pass resolution perimetry: evidence for parvocellular neural channel dependence. Neuro-ophthalmology. 1992;4:257–264.
FrisenL. High-pass resolution perimetry: central-field neuroretinal correlates. Vis Res. 1995;35:293–301. [CrossRef] [PubMed]
CallawayEM. Structure and function of parallel pathways in the primate early visual system. J Physiol. 2005;566:13–19. [CrossRef] [PubMed]
KaplanE. The M, P, K pathways of the primate visual system.ChalupaLM WernerJS eds. The Visual Neurosciences. 2004;481–493.The MIT Press Cambridge, MA.
DobkinsKR, AlbrightTD. Merging processing streams: color cues for motion detection and interpretation.ChalupaLM WernerJS eds. The Visual Neurosciences. 2004;1217–1228.The MIT Press Cambridge, MA.
AndersonAJ, JohnsonCA. Frequency-doubling technology perimetry. Ophthalmol Clin North Am. 2003;16:213–225. [CrossRef] [PubMed]
ChauhanBC. The value of high-pass resolution perimetry in glaucoma. Curr Opin Ophthalmol. 2000;11:85–89. [CrossRef] [PubMed]
RacetteL, SamplePA. Short-wavelength automated perimetry. Ophthalmol Clin North Am. 2003;16:227–236. [CrossRef] [PubMed]
SamplePA. Short-wavelength automated perimetry: its role in the clinic and for understanding ganglion cell function. Prog Retin Eye Res. 2000;19:369–383. [CrossRef] [PubMed]
SamplePA, BosworthCF, BlumenthalEZ, GirkinC, WeinrebRN. Visual function-specific perimetry for indirect comparison of different ganglion cell populations in glaucoma. Invest Ophthalmol Vis Sci. 2000;41:1783–1790. [PubMed]
SamplePA, ChauhanBC, AraieM, JohnsonCA. Comparison of functional methods.WeinrebRN GreveEL eds. Glaucoma Diagnosis: Structure and Function. 2004;119–126.Kugler Publications The Hague, The Netherlands.
SpryPG, JohnsonCA, MansbergerSL, CioffiGA. Psychophysical investigation of ganglion cell loss in early glaucoma. J Glaucoma. 2005;14:11–19. [CrossRef] [PubMed]
MedeirosFA, ZangwillLM, BowdC, SamplePA, WeinrebRN. Use of progressive glaucomatous optic disk change as the reference standard for evaluation of diagnostic tests in glaucoma. Am J Ophthalmol. 2005;139:1010–1018. [CrossRef] [PubMed]
SolimanMA, de JongLA, IsmaeilAA, van den BergTJ, de SmetMD. Standard achromatic perimetry, short wavelength automated perimetry, and frequency doubling technology for detection of glaucoma damage. Ophthalmology. 2002;109:444–454. [CrossRef] [PubMed]
DeLongER, DeLongDM, Clarke-PearsonDL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–845. [CrossRef] [PubMed]
GoldbaumMH, SamplePA, ChanK, et al. Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry. Invest Ophthalmol Vis Sci. 2002;43:162–169. [PubMed]
LandisJR, KochGG. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159–174. [CrossRef] [PubMed]
BudenzDL, RheeP, FeuerWJ, McSoleyJ, JohnsonCA, AndersonDR. Comparison of glaucomatous visual field defects using standard full threshold and Swedish interactive threshold algorithms. Arch Ophthalmol. 2002;120:1136–1141. [CrossRef] [PubMed]
AndersonRS. The psychophysics of glaucoma: improving the structure/function relationship. Prog Retin Eye Res. 2005;25:79–97. [PubMed]
JohnsonCA, SamplePA, CioffiGA, LiebmannJR, WeinrebRN. Structure and function evaluation (SAFE): I. Criteria for glaucomatous visual field loss using standard automated perimetry (SAP) and short wavelength automated perimetry (SWAP). Am J Ophthalmol. 2002;134:177–185. [CrossRef] [PubMed]
MuskensRP, HeegGP, JansoniusNM. An evaluation of algorithms designed to classify the results from frequency doubling perimetry. Ophthalmic Physiol Opt. 2004;24:498–503. [CrossRef] [PubMed]
FujimotoN, ZhangD, SatoE, MiyauchiO, MizotaA. Reversible changes in frequency-doubling perimetry with transiently elevated intraocular pressure. Arch Ophthalmol. 2004;122:1540–1543. [CrossRef] [PubMed]
MartinL, WangerP, VanceaL, GothlinB. Concordance of high-pass resolution perimetry and frequency-doubling technology perimetry results in glaucoma: no support for selective ganglion cell damage. J Glaucoma. 2003;12:40–44. [CrossRef] [PubMed]
HarwerthRS, CrawfordML, FrishmanLJ, ViswanathanS, SmithEL, Carter-DawsonL. Visual field defects and neural losses from experimental glaucoma. Prog Retin Eye Res. 2002;21:91–125. [CrossRef] [PubMed]
YucelYH, ZhangQ, WeinrebRN, KaufmanPL, GuptaN. Effects of retinal ganglion cell loss on magno-, parvo-, koniocellular pathways in the lateral geniculate nucleus and visual cortex in glaucoma. Prog Retin Eye Res. 2003;22:465–481. [CrossRef] [PubMed]
SamplePA, BosworthCF, WeinrebRN. Short-wavelength automated perimetry and motion automated perimetry in patients with glaucoma (see comments). Arch Ophthalmol. 1997;115:1129–1133. [CrossRef] [PubMed]
Figure 1.
 
Examples of visual field pattern deviation display for SAP, SWAP, FDT, and the small and deep dent display for HPRP in a patient with GON. Each plot shows the location and number of stimulus test locations as designated by either a box or a dot. Dot: within normal limits. The shading in the boxes denotes the probability of abnormality relative to the internal normative database of each device. Probabilities are shown in the corresponding key.
Figure 1.
 
Examples of visual field pattern deviation display for SAP, SWAP, FDT, and the small and deep dent display for HPRP in a patient with GON. Each plot shows the location and number of stimulus test locations as designated by either a box or a dot. Dot: within normal limits. The shading in the boxes denotes the probability of abnormality relative to the internal normative database of each device. Probabilities are shown in the corresponding key.
Figure 2.
 
A Venn diagram showing the overall agreement among SAP, SWAP, FDT, and HPRP in the GON and PGON groups combined (n = 142). Abnormality was based on the PSD and a specificity of 80%.
Figure 2.
 
A Venn diagram showing the overall agreement among SAP, SWAP, FDT, and HPRP in the GON and PGON groups combined (n = 142). Abnormality was based on the PSD and a specificity of 80%.
Figure 3.
 
A Venn diagram showing the overall agreement among SAP, SWAP, FDT, and HPRP in the GON and PGON groups combined (n = 142). Abnormality is based on the PD plot results at the 1% level and a specificity of 80%.
Figure 3.
 
A Venn diagram showing the overall agreement among SAP, SWAP, FDT, and HPRP in the GON and PGON groups combined (n = 142). Abnormality is based on the PD plot results at the 1% level and a specificity of 80%.
Table 1.
 
AUC and Standard Error for Groups with GON and PGON
Table 1.
 
AUC and Standard Error for Groups with GON and PGON
Parameter GON PGON
AUC SE AUC SE Criterion Sens/Spec (%) Criterion Sens/Spec (%)
SAP PSD 0.713 0.041 0.762 0.056 2.31 48/90 1.93 dB 52/80
SWAP PSD 0.733 0.041 0.775 0.052 4.48 45/90 3.75 dB 48/80
FDT PSD 0.770 0.036 0.875 0.041 4.76 68/90 4.36 dB 71/80
HPRP PSD 0.661 0.043 0.780 0.054 0.87 52/90 0.8 dB 65/80
SAP TD 5% 0.708 0.041 0.758 0.060 17 45/90 9 68/80
SAP TD 1% 0.711 0.036 0.797 0.051 4 55/90 2 65/80
SWAP TD 5% 0.641 0.046 0.646 0.062 23 23/90 14 42/80
SWAP TD 1% 0.659 0.044 0.696 0.061 11 32/92 4 48/82
FDT TD 5% 0.795 0.033 0.880 0.044 4 71/90 3 84/78
FDT TD 1% 0.763 0.028 0.820 0.047 1 68/90 N/A
HPRP Small dent 0.583 0.047 0.604 0.063 8 16/94 7 23/88
HPRP Deep dent 0.652 0.041 0.759 0.054 4 32/92 3 42/86
SAP PD 5% 0.704 0.041 0.734 0.058 11 39/92 9 55/80
SAP PD 1% 0.669 0.039 0.733 0.058 5 39/92 3 39/86
SWAP PD 5% 0.710 0.044 0.727 0.057 11 32/90 7 42/84
SWAP PD 1% 0.684 0.041 0.731 0.059 5 36/92 3 48/84
FDT PD 5% 0.763 0.036 0.891 0.043 5 74/98 4 84/86
FDT PD 1% 0.741 0.031 0.818 0.048 2 58/96 1 71/84
SAP MD 0.601 0.045 0.731 0.069 −2.22 55/90 −1.73 dB 65/80
SWAP MD 0.601 0.045 0.587 0.069 −7.07 29/90 −5.42 dB 42/80
FDT MD 0.755 0.037 0.813 0.054 −2.37 61/90 −1.48 dB 74/80
HPRP MD 0.670 0.044 0.638 0.064 2.67 19/90 1.82 dB 39/80
Table 2.
 
Summary Data for Each of the Four Participant Groups
Table 2.
 
Summary Data for Each of the Four Participant Groups
Normal (n = 51) OHT (n = 53) GON (n = 111) PGON (n = 31)
Age (y)
 Mean ± SD 51.81 ± 13.70O,G,P 60.27 ± 11.61N,G,P 65.59 ± 11.42N,O 66.85 ± 10.57N,O
 Range 25.75 to 81.33 23.29 to 79.96 33.21 to 87.47 40.01 to 81.96
Highest IOP (mm Hg)
 Mean ± SD 16.08 ± 3.03O,G,P 29.87 ± 7.16N,G 25.44 ± 7.36N,O,P 30.26 ± 9.47N,G
 Range 10 to 23 13 to 59 14 to 54 16 to 56
SAP MD (dB)
 Mean ± SD −0.72 ± 1.17G,P −0.36 ± 1.38G,P −2.89 ± 3.97N,O −3.19 ± 3.36N,O
 Range −4.05 to 0.95 −4.08 to 3.13 −20.14 to 2.04 −12.41 to 1.52
SAP PSD (dB)
 Mean ± SD 1.72 ± 0.68G,P 1.61 ± 0.69G,P 3.62 ± 3.58N,O 4.16 ± 3.97N,O
 Range 1.05 to 4.46 0.90 to 4.2 1.02 to 16.11 1.33 to 14.19
Table 3.
 
Statistical Comparison of ROC Area for PSD and for the Best Parameter from Each Test
Table 3.
 
Statistical Comparison of ROC Area for PSD and for the Best Parameter from Each Test
A. PSD
GON GON P PGON P
SAP vs. HPRP 0.291 0.762
SAP vs. FDT 0.197 0.076
SAP vs. SWAP 0.633 0.813
HPRP vs. FDT 0.020 0.135
HPRP vs. SWAP 0.193 0.939
FDT vs. SWAP 0.432 0.067
B. Best parameter from each test
GON P PGON P
SAP PSD vs. HPRP MD 0.434 SAP TD 1% vs. HPRP PSD 0.795
SAP PSD vs. FDT TD 5% 0.069 SAP TD 1% vs. FDT PD 5% 0.090
SAP PSD vs. SWAP PSD 0.633 SAP TD 1% vs. SWAP PSD 0.677
HPRP MD vs. FDT TD 5% 0.004 HPRP PSD vs. FDT PD 5% 0.097
HPRP MD vs. SWAP PSD 0.240 HPRP PSD vs. SWAP PSD 0.939
FDT TD 5% vs. SWAP PSD 0.182 FDT PD 5% vs. SWAP PSD 0.020
Table 4.
 
Percentage of Ocular Hypertensives Called Abnormal by Each Test
Table 4.
 
Percentage of Ocular Hypertensives Called Abnormal by Each Test
Specificity 80% Specificity 90%
PSD PD < 1% PSD PD < 1%
SAP 18.9% 15.1% 9.4% 9.4%
HPRP 24.5% 20.8% 15.0% 11.3%
FDT 45.3% 30.2% 26.4% 15.1%
SWAP 18.9% 13.2% 15.1% 9.4%
Table 5.
 
The Number of Eyes and Percentage with Number of Visual Field Types Showing Abnormal Results
Table 5.
 
The Number of Eyes and Percentage with Number of Visual Field Types Showing Abnormal Results
Number of Abnormal Results PGON (n = 31) GON (n = 111) OHT (n = 53) Normal (n = 51)
PD Plot
 All 4 8 (26) 24 (22) 3 (6) 0 (0)
 3 of 4 3 (10) 15 (14) 1 (2) 2 (4)
 2 of 4 6 (19) 15 (14) 4 (8) 2 (4)
 1 of 4 9 (29) 27 (24) 19 (36) 20 (39)
 SAP only 1 7 1 4
 SWAP only 1 3 3 6
 FDT only 6 14 10 6
 HPRP only 1 3 5 4
 None 5 (16) 30 (27) 26 (49) 27 (53)
PSD
 All 4 10 (32) 31 (28) 4 (7) 1 (2)
 3 of 4 3 (10) 17 (15) 2 (4) 2 (4)
 2 of 4 8 (26) 16 (15) 7 (13) 6 (12)
 1 of 4 8 (26) 30 (27) 21 (40) 18 (35)
 SAP only 0 5 0 3
 SWAP only 1 6 3 4
 FDT only 4 12 13 5
 HPRP only 3 7 5 6
 None 2 (6) 17 (15) 19 (36) 24 (47)
Table 6.
 
The κ Statistics and Levels of Agreement for All Combinations of Test Pairings in the GON and PGON Groups Combined
Table 6.
 
The κ Statistics and Levels of Agreement for All Combinations of Test Pairings in the GON and PGON Groups Combined
κ SE Proportion Agreement Strength of Agreement
PD Plot
 SAP and SWAP 0.586 0.07 0.79 Moderate
 SAP and FDT 0.393 0.07 0.69 Fair
 SAP and HPRP 0.375 0.08 0.69 Fair
 SWAP and FDT 0.411 0.07 0.69 Moderate
 SWAP and HPRP 0.470 0.07 0.74 Moderate
 FDT and HPRP 0.326 0.07 0.64 Fair
PSD
 SAP and SWAP 0.563 0.07 0.78 Moderate
 SAP and FDT 0.349 0.07 0.67 Fair
 SAP and HPRP 0.394 0.08 0.69 Fair
 SWAP and FDT 0.366 0.07 0.68 Fair
 SWAP and HPRP 0.268 0.08 0.63 Fair
 FDT and HPRP 0.247 0.08 0.62 Fair
Table 7.
 
Number of Abnormal Quadrants for Each Group and Test
Table 7.
 
Number of Abnormal Quadrants for Each Group and Test
Normal (n = 51) OHT (n = 53) GON (n = 111) PGON (n = 31) ANOVA P
SAP 0.76 ± 0.89 0.55 ± 0.89 1.38 ± 1.25 1.61 ± 1.20 <0.001
SWAP 0.69 ± 0.99 0.49 ± 0.78 1.21 ± 1.05 1.39 ± 1.15 <0.001
FDT 0.20 ± 0.49 0.42 ± 0.75 1.09 ± 1.13 1.32 ± 1.11 <0.001
HPRP 0.57 ± 0.88 0.66 ± 0.92 1.13 ± 1.14 1.42 ± 0.96 <0.001
P 0.004 0.4889 0.2445 0.7569
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