University of Groningen. Glaucoma care optimised in an ageing population Wesselink, Christiaan

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1 University of Groningen Glaucoma care optimised in an ageing population Wesselink, Christiaan IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2017 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Wesselink, C. (2017). Glaucoma care optimised in an ageing population. [Groningen]: Rijksuniversiteit Groningen. Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/UMCG research database (Pure): For technical reasons the number of authors shown on this cover page is limited to 10 maximum. Download date:

2 CHAPTER 4 Rates of Progression and Longitudinal Signal-to-Noise Ratios for SAP, FDT, and Scanning Laser Polarimetry in the Groningen Longitudinal Glaucoma Study Ophthalmic Physiol Opt. 2017;37: Christiaan Wesselink 1 Nomdo M. Jansonius 1,2 1. Department of Ophthalmology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands 2. Department of Epidemiology and Biostatistics, Erasmus Medical Center, Rotterdam, the Netherlands

3 Abstract Purpose To determine the usefulness of frequency doubling perimetry (FDT) and scanning laser polarimetry (GDx) for progression detection in glaucoma, compared to standard automated perimetry (SAP). Methods Data were used from 154 eyes of 154 glaucoma patients participating in the Groningen Longitudinal Glaucoma Study. Using linear regression, progression velocities were calculated for SAP (Humphrey Field Analyzer) mean deviation (MD), FDT MD and the number of abnormal test locations in the total deviation probability plot (TD), and GDx parameters ellipse modulation (EMod) and the Number. Progression velocity tertiles were determined and eyes were classified as slowly, intermediately, or fast progressing for all techniques. Comparison between SAP and FDT/GDx classifications were made using a Mantel Haenszel chi-square test. Longitudinal signal-to-noise ratios (LSNRs) were calculated, per patient and per technique, defined as progression velocity divided by the standard deviation of the residuals. Results Mean (SD) follow-up was 6.4 (1.7) years; median (interquartile range) baseline SAP MD -6.6 (-14.1 to -3.6) db. Mantel Haenszel chi squares of SAP MD versus FDT MD and SAP MD versus FDT TD were 12.5 (p<0.001), and 15.8 (p<0.001), respectively. Mantel Haenszel chi squares of SAP MD versus GDx EMod and SAP MD versus GDx the Number were 1.1 (p=0.30), and 2.0 (p=0.16), respectively. LSNRs for SAP MD were better than those for FDT MD (p=0.010), not significantly different from those for GDx the Number (p=0.54) but better than those for GDx the Number in patients with moderate/advanced glaucoma (p=0.004). Conclusions FDT but not GDx progression velocity was associated with SAP progression velocity. LSNRs were better for SAP and GDx than for FDT. 58 Chapter 4

4 Introduction Perimetry has been the gold standard for glaucoma diagnosis and follow-up for decades. At the end of the last century, promising new techniques for the assessment of glaucoma patients arose. These new techniques were rapidly adopted in clinical practice - despite limited cross-sectional and lacking longitudinal validation. Of these new techniques, we introduced frequency doubling perimetry (FDT) and scanning laser polarimetry (GDx) in 2000 in our clinic and performed these techniques alongside standard automated perimetry (SAP) in our regular glaucoma care in all glaucoma patients, aiming for a safe transition without diagnostic discontinuity. A decade later, it was clear that these techniques were useful for screening and glaucoma suspect care. 1-9 Quantitative imaging of the optic nerve head, retinal nerve fiber layer (RNFL), and inner macular layers may have found their way to the guidelines of the European Glaucoma Society (EGS; and to the Preferred Practice Pattern for Primary Open-Angle Glaucoma (POAG) of the American Academy of Ophthalmology (AAO; as modalities for glaucoma follow-up, but these guidelines also state that, since agreement between structural and functional progression is relatively poor, SAP remains integral to patient care in established glaucoma. The latter is also acknowledged in the guideline of the National Institute for Health and Clinical Excellence (NICE; Visual field testing with SAP is hampered by variability, which is caused by many factors, including fatigue related to the long test duration Despite efforts that aimed to improve SAP, this variability can influence the test results to a point where SAP becomes impossible or valueless. For some glaucoma patients in which SAP cannot be used, FDT or GDx still yield apparently useful test results. Data on the usefulness of these test results for the monitoring of established glaucoma are relatively scarce, both for FDT and for GDx In this study, we explored this monitoring for FDT and GDx simultaneously in the same group of patients, the cohort of the Groningen Longitudinal Glaucoma Study. The aims of the present study were to determine the suitability of FDT and GDx for the detection of disease progression in glaucoma as an alternative to SAP. For this purpose, we compared (1) progression velocity classifications (slowly, Rates of Progression and Signal-to-Noise Ratios for SAP, FDT, and GDx 59

5 intermediately, and fast progressing) and (2) longitudinal signal to noise ratios (LSNRs) 25,26 between the three techniques. Methods Patients: The Groningen Longitudinal Glaucoma Study Details of the Groningen Longitudinal Glaucoma Study (GLGS) have been described elsewhere. 27 The GLGS fundamentally is an ongoing collection of regular glaucoma care data. The anonymous collection and analysis of these data was approved by the ethics board of the University Medical Center Groningen. All glaucoma patients and glaucoma suspects who visited the outpatient glaucoma service of the University Medical Center Groningen between July 1, 2000 and June 30, 2001, and who provided informed consent, were included. FDT and GDx (see below) were added to the diagnostic armamentarium of the outpatient glaucoma service in the same year. The original GLGS research question was if it was possible to replace, in glaucoma patients and/or glaucoma suspects, the lengthy and cumbersome SAP by FDT and/or GDx. In the initial years, SAP was performed alongside FDT and GDx (see Introduction section), and the glaucoma patients' test results from this period were analysed in the current study. After these initial years, we continued performing SAP in glaucoma patients and moved to FDT/GDx in glaucoma suspects in our regular care. The study followed the tenets of the Declaration of Helsinki. Of the 875 patients included in the original GLGS cohort, 452 were classified as glaucoma patients (see below). 27 Of these 452 glaucoma patients, 372 were classified with SAP (Humphrey Field Analyser; HFA; Carl Zeiss Meditec Inc., Dublin, CA). Goldmann perimetry (Haag Streit AG, Bern, Switzerland) was used in 80 cases; these cases were excluded. Of the 372 patients classified with SAP, all patients who underwent a GDx or FDT at baseline in at least one eye were enrolled in the baseline analysis. All patients who had performed at least four reliable (for criteria see below) SAP tests - after an initial learning test that was excluded - were included in the follow-up analysis if they had performed also at least four FDT tests and/or four GDx tests. Follow-up had to be at least three years, and the first and the last tests of each longitudinal comparison had to be 60 Chapter 4

6 performed during the same visit. One eye per patient was included. If a patient met the inclusion criteria with both eyes, a randomly chosen eye was included. SAP, FDT, and GDx SAP was performed using the HFA 30-2 SITA Fast strategy. An abnormal test result was defined as either (1) a Glaucoma Hemifield Test outside normal limits, (2) a Pattern Standard Deviation p<0.05, or (3) three adjacent non-edge test locations p<0.05 in the pattern deviation probability plot of which at least one location was p<0.01, with all locations being on the same side of the horizontal meridian. 27 A test result was considered unreliable if false positives exceeded 10% or if both false negatives and fixation losses exceeded 10% and 20%, respectively. For glaucoma at baseline, two consecutive, reliable tests had to be abnormal in at least one eye. Defects had to be in the same hemifield and at least one depressed test-point of these defects had to have exactly the same location on both fields. Moreover, defects had to be compatible with glaucoma and without any other explanation. The first test was discarded because of a learning effect. Therefore, at least three tests had to be performed at baseline before glaucoma could be diagnosed. FDT testing was performed using the C-20 full-threshold mode, which uses a grid with 17 test locations (version 2.60, Carl Zeiss Meditec Inc., Dublin, CA). We did not exclude tests based on reliability indices, since no reasonable cutoff points are available to do this. Moreover, it was demonstrated that unreliable tests contain useful information when used for screening purposes. 28 Furthermore, the learning effect of FDT tests has shown to be small. 29 GDx testing was performed using the nerve fiber analyzer (GDx; Carl Zeiss Meditec Inc., Dublin, CA,). The peripapillary RNFL thickness was recorded along a circle centered on the optic disc. Peripapillary atrophy (PPA) crossing the circle was disallowed. In case of PPA, the diameter of the circle was enlarged until the measurement did not include PPA. High image quality required a well centered optic nerve head, an in-focus image, equal illumination in all quadrants, and an absence of motion artifacts. During follow-up, SAP was performed with a frequency of one test per year. In case of suspected progression or unreliable tests, clinicians were allowed to increase the frequency of testing. This was a subjective decision; no formal tools or Rates of Progression and Signal-to-Noise Ratios for SAP, FDT, and GDx 61

7 rules were given. FDT and GDx testing was performed every other year as a part of a regular glaucoma care visit that also comprised SAP testing. Baseline analysis All baseline FDT and GDx parameters that were a priori considered potentially useful for glaucoma progression detection, that is, parameters presumed to be monotonically related to disease severity, were (scatter)plotted against the baseline SAP mean deviation (MD) values. For FDT, these were MD and the number of test locations with a sensitivity p<0.01 on the total deviation probability plot (TD). 27 For GDx, these were superior maximum in µm (SupM), inferior maximum in µm (InfM), ellipse modulation (EMod), ellipse average in µm (EAvg), and the Number. Spearman s rank correlation coefficients were calculated to analyse the strength of the monotonic relationship between SAP MD and the various FDT/GDx parameters. Follow-up analysis The SAP progression velocity was calculated for each patient by applying linear regression to the MD and the tertiles of the resulting progression velocity distribution were determined. In this way, patients were classified as slow, intermediate, and fast SAP progressors. This was also done for both FDT parameters and for two GDx parameters that had the highest correlation with SAP MD in the baseline analysis. For the FDT parameter TD, patients with more than 15 (of 17) abnormal test locations at baseline were excluded in order to avoid ceiling effects. Subsequently, four three-by-three contingency tables were made, plotting the SAP progression velocity tertiles against the progression velocity tertiles of the two FDT and two GDx parameters. A Mantel-Haenszel chi-square test was applied to the contingency tables, as the progression velocity tertiles fall into a natural order. Longitudinal signal-to-noise ratios For SAP MD, FDT MD, and GDx the Number, longitudinal signal-to-noise ratios (LSNRs) were calculated. 25,26 This was done per patient and per technique/parameter by dividing the progression velocity by the standard deviation (SD) of the residuals. For comparing SAP LSNRs to FDT and GDx LSNRs, the Wilcoxon signed-rank test was used. Furthermore, a summary quantification of the LSNR was calculated using the method described by Gardiner et al. 25 In this method, signal is defined as the 10th percentile of the progression velocity 62 Chapter 4

8 distribution and noise as the SD of the residuals pooled over all eyes in the dataset. Statistical analyses were performed with IBM SPSS (version 22.0; IBM Corp., Armonk, NY), except for the Mantel-Haenszel chi-square test, which was performed using ETCETERA version 2.89 in WinPepi version A p value of 0.05 or less was considered statistically significant. Results For the baseline FDT and GDx analyses, 367 and 363 patients could be enrolled, respectively. For the follow-up analyses, 154 patients could be included. Of these 154 patients, 150 were available for FDT, of which 125 had 15 or less abnormal test locations at baseline, and 136 were available for GDx. Table 4.1 shows the baseline characteristics of the 154 patients included in the follow-up analyses, stratified per analysis. Table 4.2 presents the corresponding follow-up data. For the 154 patients included in the follow-up analysis, the mean (SD) follow-up duration was 6.4 (1.7) years and the mean number of reliable SAP test results was 8.1. Median (interquartile range [IQR]) baseline SAP MD was -6.6 (-14.1 to -3.6) db; median (IQR) SAP MD slope was 0.16 ( 0.46 to +0.02) db/year. Figure 4.1 displays the baseline relationships between SAP MD and the two FDT parameters. Both FDT parameters showed a strong correlation with SAP MD (FDT MD: 0.83, p<0.001; FDT TD: -0.82, p<0.001). Figure 4.2 gives the baseline relationships between SAP MD and the two GDx parameters that showed the higest correlation with SAP MD, EMod and the Number. These correlations with SAP MD were 0.53 (p<0.001) and (p<0.001), respectively. Table 4.3 presents the progression classification relationships between SAP MD and FDT MD (A) and between SAP MD and FDT TD (B). The relationships were highly significant (p<0.001) for both FDT parameters. In other words, if a patient was Rates of Progression and Signal-to-Noise Ratios for SAP, FDT, and GDx 63

9 classified as a fast progressor on SAP, this patient was very likely to be classified as a fast progressor on FDT as well (and unlikely as a slow progressor), and vice versa. Table 4.4 presents the progression classification relationships between SAP MD and GDx EMod (A) and between SAP MD and GDx the Number (B). No association was found between SAP and GDx progression classification. The SAP versus GDx analysis performed in subgroups of patients with early (SAP MD -6 db) and moderate/severe (SAP MD < -6 db) glaucoma did not reveal associations either (data not shown). Table 4.1. Baseline characteristics of patients included in the follow-up analyses (mean with standard deviation between brackets unless stated otherwise) Patients included in FDT follow-up Patients included in GDx follow-up with 15 or less all abnormal FDT test locations at baseline Number of patients Age (year) 66.0 (11.2) 65.7 (11.6) 65.7 (11.1) Sex (% male) IOP (mmhg) 15.7 (4.6) 15.8 (4.6) 15.8 (4.5) SAP MD (db) -9.5 (7.6) -7.9 (6.5) -9.7 (7.7) SAP MD median (IQR; db) -6.6 (-14.2 to -3.6) -5.5 (-12.8 to -2.7) -7.0 (-14.6 to -3.8) FDT MD (db) -6.9 (5.5) -5.2 (4.4) FDT TD 7.8 (5.7) 6.0 (4.5) GDx SupM (µm) 72.9 (7.7) GDx InfM (µm) 75.8 (17.8) GDx EMod (55.2) GDx EAvg (µm) 59.7 (13.2) GDx the Number 52.9 (24.4) IOP = intraocular pressure; MD = mean deviation; TD = number of test locations with a sensitivity p<0.01 on the total deviation probability plot; SupM = superior maximum; InfM = inferior maximum; EMod = ellipse modulation; EAvg = ellipse average. 64 Chapter 4

10 Table 4.2. Follow-up characteristics (mean with standard deviation between brackets unless stated otherwise) Patients included in FDT follow-up Patients included in GDx follow-up With 15 or less All abnormal FDT test locations at baseline Number of patients Follow-up duration (year) 6.4 (1.7) 6.4 (1.8) 6.6 (1.7) Mean IOP (mmhg) 14.7 (2.7) 14.7 (2.7) 14.7 (2.7) Number of reliable SAP tests 8.2 (2.2) 8.1 (2.2) 8.5 (2.2) SAP MD slope (db/year) (0.53) (0.55) (0.55) SAP MD SD of residuals (db) 1.15 (0.65) 1.13 (0.65) 1.23 (0.75) SAP MD slope median (IQR; (-0.47 to (-0.46 to +0.02) (-0.47 to +0.02) db/year) +0.01) SAP MD slope tertile cut-off points (db/year) / / / Number of FDT tests 4.5 (0.8) 4.5 (0.8) FDT MD slope (db/year) (0.45) (0.43) FDT MD slope median (IQR; db/year) (-0.39 to +0.17) (-0.44 to +0.07) FDT MD slope tertile cut-off points (db/year) / / FDT TD slope (test locations per year) (0.50) (0.48) FDT TD slope tertile cut-off points (test locations per year) / / Number of GDx tests 4.4 (0.7) GDx EMod slope (7.34) GDx EMod slope tertile cut-off points / GDx the Number slope (2.62) GDx the Number slope tertile cut-off points / IOP = intraocular pressure; MD = mean deviation; TD = number of test locations with a sensitivity p<0.01 on the total deviation probability plot; IQR inter quartile range; EMod = ellipse modulation. Rates of Progression and Signal-to-Noise Ratios for SAP, FDT, and GDx 65

11 A. B. plots showing baseline Standard Automated Perimetry (SAP) test results and Frequency Figure 4.1. Scatter-plots Doubling Perimetry (FDT) test results for 367 glaucoma patients. SAP parameter was Mean Deviation; FDT parameters were Mean Deviation (A) and the number nu of test locations with a sensitivity p<0.01 on the total deviation probability plot (B). Correlation coefficients in right upper corner. 66 Chapter 4

12 A. B. plots showing baseline Standard Automated Perimetry (SAP) test results and scanning Figure 4.2. Scatter-plots laser polarimetry (GDx) test results for 363 glaucoma patients. SAP parameter was Mean Deviation; GDx parameters were Ellipse Modulation (A) and the Number (B). Correlation coefficients in right upper corner. Rates of Progression and Signal-to-Noise Signal Ratios for SAP, FDT, and GDx 67

13 Table 4.3. Progression velocity tertiles for SAP MD versus FDT MD (A) and SAP MD versus FDT TD (B). MD = mean deviation; (TD = number of test locations with a sensitivity p<0.01 on the total deviation probability plot) A. FDT MD SAP MD Slow Intermediate Fast Slow Intermediate Fast Mantel-Haenszel chi-square: 12.5 (p<0.001) B. FDT TD Slow Intermediate Fast Slow SAP MD Intermediate Fast Mantel-Haenszel chi-square: 15.8 (p<0.001) Table 4.4. Progression velocity tertiles for SAP MD versus GDx EMod (A) and SAP MD versus GDx The Number (B). (MD = mean deviation; EMod = ellipse modulation) A. GDx EMod SAP MD Slow Intermediate Fast Slow Intermediate Fast Mantel-Haenszel chi-square: 1.1 (p=0.30) B. GDx The Number SAP MD Slow Intermediate Fast Slow Intermediate Fast Mantel-Haenszel chi-square: 2.0 (p=0.16) 68 Chapter 4

14 Table 4.5 presents the LSNR and the summary quantification of the LSNR for SAP MD, FDT MD, and GDx the Number. The LSNR was better for SAP than for FDT (p=0.010), but equal between SAP and GDx (p=0.54). For patients with moderate/severe glaucoma (SAP MD < -6 db), the SAP LSNR was better than both the FDT LSNR (p=0.017) and the GDx LSNR (p=0.004). Summary quantifications of LSNR were yr -1 for SAP MD, yr -1 for FDT MD and yr -1 for GDx the Number (the underlying 10th percentile of the progression velocity was db/yr for SAP MD, db/yr for FDT MD, and +5.1 per yr for GDx the Number; the corresponding SDs of the pooled residuals were 1.17 db, 1.31 db, and 7.6). Table 4.5. Longitudinal signal-to-noise ratios for SAP MD, FDT MD, and GDx the Number Summary LSNR (yr -1 ) LSNR (yr -1 ) Median Compared to SAP^ All SAP FDT P=0.010 GDx P=0.54 Early glaucoma (SAP MD -6 db) SAP FDT P=0.026 GDx P=0.052 Moderate/severe (SAP MD < -6 db) SAP FDT P=0.017 GDx P=0.004 ^Wilcoxon Signed-Rank Test. LSNR = longitudinal signal-to-noise ratio; MD = mean deviation; Discussion FDT but not GDx progression velocity was associated with SAP progression velocity in this longitudinal glaucoma study. Longitudinal signal-to-noise ratios were better for SAP than for FDT, equal for SAP and GDx in early glaucoma, but better for SAP than for GDx in moderate/advanced glaucoma. Rates of Progression and Signal-to-Noise Ratios for SAP, FDT, and GDx 69

15 Studies comparing SAP progression detection with FDT progression detecting are scarce. 18,23,24 Haymes et al compared FDT with SAP in 65 glaucoma patients, using various criteria for progression. 18 Their median follow-up was 3.5 years with 9 tests. Little agreement was found between both techniques. Xin et al compared Matrix FDT with SAP in 33 glaucoma patients. 24 Their follow-up was 21 months with 5 tests. Agreement between tests was poor. Redmond et al compared Matrix FDT with SAP in 64 glaucoma patients. 23 Their median follow-up was 5.4 years with 12 tests. They found a moderate agreement between both techniques for total deviation analysis but not for pattern deviation analysis. The former is in agreement with our results; the latter can be explained by the fact that pattern deviation analysis is not monotonically related to disease severity over the entire range. For that reason we had excluded this analysis beforehand. The SAP progression velocity in the report by Redmond et al (median db/year; IQR to db/year) is in very good agreement with our findings (-0.16 [-0.46 to +0.02]). For FDT, our velocities were smaller (-0.05 [-0.39 to +0.17]) than those of Redmond et al (-0.19 [-0.44 to +0.00]). This may be explained by the early disease stage of their sample (mean baseline SAP MD in their study was -2.6 db, to be compared to -9.5 db in our study [Table 4.1]). After excluding those with 16 or 17 abnormal test locations on FDT at baseline, our median FDT progression velocity increased to db/year and the IQR moved to to (Table 4.2), that is, in good agreement with Redmond et al (-0.44 to db/year). Liu et al compared Matrix FDT with SAP in 148 glaucoma patients, using pointwise linear regression analysis. 19 Their median follow-up was 3.3 years with 11 tests. Agreement between FDT and SAP was poor to fair. Their mean progression velocities for SAP (-0.20 db/year) and FDT (-0.26 db/year) are in good/moderate agreement with our results (-0.26 and db/year; Table 4.2). Event detection algorithms usually require two baseline tests and three follow-up tests. This implies that events are difficult to detect reliably with a limited number of tests. For trend analysis, the length of the follow-up is much more important than the number of tests; a follow-up of at least five years is required to make a reasonable estimate of the progression velocity in glaucoma. 31 With these limitations in mind, it is not a surprise that some of the abovementioned studies found little agreement between SAP and FDT progression detection, since limited follow-up implies noisy data and sample sizes were not large enough to compensate for that. The fact that the studies with the longest follow-up (Redmond et al: 5.4 years; this 70 Chapter 4

16 study: 6.5 years) showed the best agreement suggests that both techniques provide overlapping information. We found better LSNRs for progression detection with SAP than for progression detection with FDT. Our summary quantification of LSNR for SAP was -0.67/yr. This value agreed well with that of Gardiner et al, being -0.74/yr. 25 The poorer performance of FDT seems to contradict the findings of Artes and Chauhan. 32 Their SNR calculation methodology, however, was primarily developed to compare devices independently of their measurement scales and their signal in the SNR calculation was the presence of visual field defects rather than progression. Obviously, our LSNR findings should not be interpreted as a disqualification of FDT for screening. Most reports on the monitoring of glaucomatous progression with GDx and SAP include glaucoma patients as well as glaucoma suspects and/or healthy patients Higher rates of retinal RNFL change were observed in subjects who showed progressive change with SAP and/or stereophotography. 35 However, other studies showed that agreement between the techniques was poor, or moderate at best. 33,34 Longitudinal studies comparing the progression detection ability of GDx compared to SAP in established glaucoma are very limited in number. Makabe et al found a significant relationship between SAP progression using mean or pattern standard deviations and the GDx progression using the nerve fiber indicator (NFI) in 24 glaucomatous eyes, whereas Moon et al showed in a cohort of 152 glaucoma patients that agreement between SAP and GDx was poor with both trend and event analyses. 20,22 O Leary et al found no detectable increase in deterioration of RNFL thickness in glaucoma patients compared with control subjects, whereas SAP did show significant deterioration in the patients. 36 Our data showed no relationship between GDx and SAP concerning detecting glaucomatous progression in established glaucoma. This is in line with recent data from the United Kingdom Glaucoma Treatment Study. In this study, 516 newly detected glaucoma patients were randomized to latanoprost or placebo, of whom 252 were followed with both SAP and GDx. There was a significant difference in SAP progression but not in GDx progression between the groups (Nomoto H, et al. IOVS 2014;55:ARVO E- Abstract 4776). It seems that GDx test results can predict glaucomatous field loss in suspects 1,8,37,38, but there is apparently no useful information yield in glaucoma patients with an established visual field defect. Rates of Progression and Signal-to-Noise Ratios for SAP, FDT, and GDx 71

17 We found - on average - an equal LSNR for SAP and GDx. For patients with early glaucoma, there was a trend towards a better LSNR for GDx (p=0.052) whereas for patients with moderate/severe glaucoma we found a clearly worse LSNR for GDx compared to SAP (p=0.004). Gardiner et al found that RNFL thickness (as measured by SDOCT) had a better LSNR than SAP MD. 25 This may be explained by the very early disease stage of their sample (the mean (SD) baseline SAP MD in their study was -0.5 (2.6) db, to be compared to -9.7 (7.7) db) in our study [Table 4.1]). Due to nature of our data collection, the numbers of SAP, FDT, and GDx tests were different. For that reason, we analysed the data using linear regression - event-based comparisons would have be biased by the unequal numbers of tests. For comparisons based on linear regression, the only import issue is an equally long and coinciding follow-up. For that reason we required that the first and the last tests of each comparison were performed during the same visit. The remaining question is if the differences in numbers of tests might have influenced the LSNR comparisons. To explore this, we performed model calculations similar to those used in our earlier work. 31 For the range of number of test as used in the current study, the LSNR calculations were not noticeably influenced by the number of tests. FDT has some advantages over SAP. The low spatial frequency tested makes the test insensitive to optical blur and the low number of test locations (17 for the C-20 grid) implies a relatively short test duration (typically 1 minute per eye in the C-20 screening mode and less than 5 minutes in the full-threshold threshold mode; obviously, this could be improved further with strategies like SITA in SAP). The parameter in best agreement with SAP was the number of test locations with a sensitivity P<0.01 on the total deviation probability plot. When using this FDT parameter, the C-20 screening mode can be used instead of the longer fullthreshold mode. 39 In this way, the test is even more accessible for patients not capable of providing useful SAP test results. If in the screenings mode there is a confirmed increase in the number of abnormal test locations, the treatment can be intensified accordingly. We do not advocate an easy glaucoma follow-up with FDT for all glaucoma patients. SAP MD showed a better signal-to-noise ratio than FDT MD. Furthermore, unlike FDT, SAP has shown its value in glaucoma progression detection in several 72 Chapter 4

18 randomized trials and thus remains the modality of choice. However, if SAP appears to be too difficult to perform (see Introduction section), FDT can be tried as an alternative for glaucoma progression detection and switching to FDT might be a better option than refraining from monitoring at all. The applicability of FDT is limited to early and moderate glaucoma; in severe glaucoma, FDT cannot be used due to ceiling effects (Fig. 4.1B). Rates of Progression and Signal-to-Noise Ratios for SAP, FDT, and GDx 73

19 References 1. Heeg GP, Jansonius NM. The groningen longitudinal glaucoma study III. The predictive value of frequency-doubling perimetry and GDx nerve fibre analyser test results for the development of glaucomatous visual field loss. Eye (Lond) 2009;23: Jansonius NM, Heeg GP. The Groningen Longitudinal Glaucoma Study. II. A prospective comparison of frequency doubling perimetry, the GDx nerve fibre analyser and standard automated perimetry in glaucoma suspect patients. Acta Ophthalmol 2009;87: Leeprechanon N, Giangiacomo A, Fontana H, Hoffman D, Caprioli J. Frequency-doubling perimetry: comparison with standard automated perimetry to detect glaucoma. Am J Ophthalmol 2007;143: Liu S, Lam S, Weinreb RN, et al. Comparison of standard automated perimetry, frequency-doubling technology perimetry, and short-wavelength automated perimetry for detection of glaucoma. Invest Ophthalmol Vis Sci 2011;52: Medeiros FA, Sample PA, Weinreb RN. Frequency doubling technology perimetry abnormalities as predictors of glaucomatous visual field loss. Am J Ophthalmol 2004;137: Mowatt G, Burr JM, Cook JA, et al. Screening tests for detecting open-angle glaucoma: systematic review and meta-analysis. Invest Ophthalmol Vis Sci 2008;49: Mwanza JC, Warren JL, Hochberg JT, Budenz DL, Chang RT, Ramulu PY. Combining Frequency Doubling Technology Perimetry and Scanning Laser Polarimetry for Glaucoma Detection. J Glaucoma 2015;24: Shaikh A, Salmon JF. The role of scanning laser polarimetry using the GDx variable corneal compensator in the management of glaucoma suspects. Br J Ophthalmol 2006;90: Toth M, Kothy P, Vargha P, Hollo G. Accuracy of combined GDx-VCC and matrix FDT in a glaucoma screening trial. J Glaucoma 2007;16: Bengtsson B. Glaucoma case detection. Acta Ophthalmol (Copenh) 1991;69: De Jong DGMM, Greve EL, Bakker D. Psychological factors in computer assisted perimetry: automatic and semi-automatic perimetry. Doc Ophthalmol Proc Ser 1984;43: Gardiner SK, Demirel S. Assessment of patient opinions of different clinical tests used in the management of glaucoma. Ophthalmology 2008;115: Henson DB, Emuh T. Monitoring vigilance during perimetry by using pupillography. Invest Ophthalmol Vis Sci 2010;51: Hudson C, Wild JM, O'Neill EC. Fatigue effects during a single session of automated static threshold perimetry. Invest Ophthalmol Vis Sci 1994;35: Junoy Montolio FG, Wesselink C, Gordijn M, Jansonius NM. Factors that influence standard automated perimetry test results in glaucoma: test reliability, technician experience, time of day, and season. Invest Ophthalmol Vis Sci 2012;53: Chapter 4

20 16. Junoy Montolio FG, Wesselink C, Jansonius NM. Persistence, spatial distribution and implications for progression detection of blind parts of the visual field in glaucoma: a clinical cohort study. PLoS One 2012;7:e Bayer AU, Erb C. Short wavelength automated perimetry, frequency doubling technology perimetry, and pattern electroretinography for prediction of progressive glaucomatous standard visual field defects. Ophthalmology 2002;109: Haymes SA, Hutchison DM, McCormick TA, et al. Glaucomatous visual field progression with frequency-doubling technology and standard automated perimetry in a longitudinal prospective study. Invest Ophthalmol Vis Sci 2005;46: Liu S, Yu M, Weinreb RN, Lai G, Lam DS, Leung CK. Frequency doubling technology perimetry for detection of visual field progression in glaucoma: a pointwise linear regression analysis. Invest Ophthalmol Vis Sci 2014;55: Makabe K, Takei K, Oshika T. Longitudinal relationship between retinal nerve fiber layer thickness parameters assessed by scanning laser polarimetry (GDxVCC) and visual field in glaucoma. Graefes Arch Clin Exp Ophthalmol 2012;250: Meira-Freitas D, Tatham AJ, Lisboa R, et al. Predicting progression of glaucoma from rates of frequency doubling technology perimetry change. Ophthalmology 2014;121: Moon BG, Sung KR, Cho JW, et al. Glaucoma progression detection by retinal nerve fiber layer measurement using scanning laser polarimetry: event and trend analysis. Korean J Ophthalmol 2012;26: Redmond T, O'Leary N, Hutchison DM, Nicolela MT, Artes PH, Chauhan BC. Visual field progression with frequency-doubling matrix perimetry and standard automated perimetry in patients with glaucoma and in healthy controls. JAMA Ophthalmol 2013;131: Xin D, Greenstein VC, Ritch R, Liebmann JM, De Moraes CG, Hood DC. A comparison of functional and structural measures for identifying progression of glaucoma. Invest Ophthalmol Vis Sci 2011;52: Gardiner SK, Fortune B, Demirel S. Signal-to-Noise Ratios for Structural and Functional Tests in Glaucoma. Transl Vis Sci Technol 2013;2: Gardiner SK, Boey PY, Yang H, Fortune B, Burgoyne CF, Demirel S. Structural Measurements for Monitoring Change in Glaucoma: Comparing Retinal Nerve Fiber Layer Thickness With Minimum Rim Width and Area. Invest Ophthalmol Vis Sci 2015;56: Heeg GP, Blanksma LJ, Hardus PL, Jansonius NM. The Groningen Longitudinal Glaucoma Study. I. Baseline sensitivity and specificity of the frequency doubling perimeter and the GDx nerve fibre analyser. Acta Ophthalmol Scand 2005;83: Heeg GP, Jansonius NM. Influence of test reliability on the screening performance of frequencydoubling perimetry. Am J Ophthalmol 2006;141: Heeg GP, Ponsioen TL, Jansonius NM. Learning effect, normal range, and test-retest variability of Frequency Doubling Perimetry as a function of age, perimetric experience, and the presence or absence of glaucoma. Ophthalmic Physiol Opt 2003;23: Rates of Progression and Signal-to-Noise Ratios for SAP, FDT, and GDx 75

21 30. Abramson JH. WINPEPI updated: computer programs for epidemiologists, and their teaching potential. Epidemiol Perspect Innov 2011;8: Jansonius NM. On the accuracy of measuring rates of visual field change in glaucoma. Br J Ophthalmol 2010;94: Artes PH, Chauhan BC. Signal/noise analysis to compare tests for measuring visual field loss and its progression. Invest Ophthalmol Vis Sci 2009;50: Alencar LM, Zangwill LM, Weinreb RN, et al. Agreement for detecting glaucoma progression with the GDx guided progression analysis, automated perimetry, and optic disc photography. Ophthalmology 2010;117: Folio LS, Wollstein G, Kotowski J, et al. Detection of glaucoma progression by population and individual derived variability criteria. Br J Ophthalmol 2013;97: Medeiros FA, Alencar LM, Zangwill LM, et al. Detection of progressive retinal nerve fiber layer loss in glaucoma using scanning laser polarimetry with variable corneal compensation. Invest Ophthalmol Vis Sci 2009;50: O'Leary N, Artes PH, Hutchison DM, Nicolela MT, Chauhan BC. Rates of retinal nerve fibre layer thickness change in glaucoma patients and control subjects. Eye (Lond) 2012;26: Essock EA, Gunvant P, Zheng Y, Garway-Heath DF, Kotecha A, Spratt A. Predicting visual field loss in ocular hypertensive patients using wavelet-fourier analysis of GDx scanning laser polarimetry. Optom Vis Sci 2007;84: Mohammadi K, Bowd C, Weinreb RN, Medeiros FA, Sample PA, Zangwill LM. Retinal nerve fiber layer thickness measurements with scanning laser polarimetry predict glaucomatous visual field loss. Am J Ophthalmol 2004;138: Stoutenbeek R, Heeg GP, Jansonius NM. Frequency doubling perimetry screening mode compared to the full-threshold mode. Ophthalmic Physiol Opt 2004;24: Chapter 4

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