Int Ophthalmol (2015) 35:81–87 DOI 10.1007/s10792-014-0022-x
ORIGINAL PAPER
Correlation between the ganglion cell complex and functional measures in glaucoma patients and suspects Ivan C. Teixeira • Erica Bresciani-Battilana • Diego T. Q. Barbosa • Cristiano Caixeta-Umbelino Maurı´cio D. Paolera • Niro Kasahara
•
Received: 1 September 2014 / Accepted: 20 November 2014 / Published online: 26 November 2014 Ó Springer Science+Business Media Dordrecht 2014
Abstract The purpose of the study was to correlate the Fourier-domain OCT ganglion cell complex (GCC) parameters with automated perimetry (AP) functional measures of the optic nerve. This retrospective study included patients who had previously undergone examination with the RTVue-100 OCT and AP, SITA strategy. The parameters of GCC (average, superior, inferior, S–I [superior inferior difference], S–I SD [standard deviation of S–I], GLV [global loss volume] and FLV [focal loss volume]) were correlated with the mean deviation (MD) and pattern standard deviation (PSD) values of AP using linear and logarithmic regression analysis. All correlations between GCC and automated perimetry parameters were strong (r [ 0.60), except that for SI and MD (r = -0.05); SI and PSD (r = 0.09); SI-DS and MD (r = -0.06); and SI-SD and PSD (r = 0.08). In summary, GCC derived structural measures showed good correlation with functional parameters from AP.
Presented at the 10th European Glaucoma Society Congress, Copenhagen on June 17–22, 2012. I. C. Teixeira E. Bresciani-Battilana D. T. Q. Barbosa C. Caixeta-Umbelino M. D. Paolera N. Kasahara (&) Department of Ophthalmology, Irmandade da Santa Casa de Misericordia de Sao Paulo, Santa Casa de Sao Paulo School of Medical Sciences, Rua Sao Mauro, 292, Sa˜o Paulo 02526-050, Brazil e-mail: [email protected]
Keywords Glaucoma Visual fields Ganglion cell complex Perimetry
Introduction Primary open angle glaucoma (POAG) is a multifactorial optic neuropathy characterized by a progressive loss of retinal ganglion cells and corresponding visual field defects. The structural assessment of the disease is usually done by evaluation of the optic nerve head (ONH) and the retinal nerve fiber layer (RNFL). Reports have suggested that macular thickness assessment could be a valuable measure of glaucomatous structural change, because glaucoma damage affects retinal ganglion cells, which are densely present in the macular region [1–4]. The ganglion cell complex (GCC) is a measure of the retinal ganglion cells in the macular region assessed by the Fourier-Domain optical coherence tomography (OCT). The GCC encompass three layers in the retina, the RNFL made of the ganglion cell axons, the ganglion cell layer made of the ganglion cell bodies, and the innerplexiform layer made of the ganglion cell dendrites. It has good diagnostic value to detect glaucoma and to differentiate between normal and glaucoma subjects [5–17]. We have previously demonstrated a strong correlation between the GCC parameters with structural measures of the ONH and the RNFL as evaluated by Fourier-domain OCT [18]. The correlation between
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82 Table 1 Demographic characteristic of the sample
Int Ophthalmol (2015) 35:81–87
Variable
Glaucoma
Suspects
Ocular hypertensives
Age
62.5 ± 9.2
51.8 ± 10.2
55.4 ± 8.7
M:F
5:10
4:8
5:7
Ethnicity
M male, F female, IOP intraocular pressure, CD cup-to disk ratio, MD mean deviation, PSD pattern standard deviation, GCC ganglion cell complex, FLV focal loss volume, GLV global loss volume
8
3
7
Black
3
4
1
0.670
4
5
4
Mean IOP
18.3 ± 2.1
14.5 ± 3.2
24.6 ± 2.8
0.219
Median CD Mean MD
0.8 -7.8 ± 2.7
0.6 -1.7 ± 0.5
0.3 0.4 ± 1.2
0.000 0.020
Mean PSD
5.3 ± 2.2
2.1 ± 0.4
1.4 ± 0.2
0.449
GCC average
75.6 ± 11.0
89.7 ± 2.2
98.5 ± 3.7
\0.000
GCC superior
75.6 ± 10.5
90.3 ± 2.6
99.9 ± 7.2
\0.000
GCC inferior
75.6 ± 12.0
89.6 ± 3.1
97.5 ± 5.3
\0.000
FLV %
4.9 ± 3.6
0.8 ± 0.2
0.2 ± 0.1
\0.000
GLV %
18.2 ± 12.1
6.4 ± 4.3
4.6 ± 3.6
\0.000
the GCC and functional measures of glaucoma has not been extensively studied. The purpose of this study was to access the correlation of the Fourier-domain OCT GCC parameters with automated perimetry functional measures of the optic nerve.
Materials and methods Study population This retrospective study included both patients with POAG and suspects older than 40 years of age of both genders, with 20/20 best corrected visual acuity, and any ethnicity. Subjects with cataracts or any other ocular disease and previous incision or laser surgery for glaucoma were not included in the study. Institution Board Review approved the study and the procedures followed adhered to the principles for medical research involving human subjects of the Declaration of Helsinki in 1964 (amended by the 59th WMA General Assembly, Seoul, Korea, October 2008). The charts of all eligible subjects were reviewed in order to verify if they fulfilled the inclusion criteria and all data for the study extracted by one of us. In order to be included in the study, POAG patients had to have typical optic disk damage (diffuse or localized rim thinning, enlarged cupping, disk hemorrhage, asymmetry in cup-to disk ratio 0.2 or greater between eyes) with corresponding visual
123
0.016 0.865
White Mixed
P value
field loss on a reliable perimetry exam (at least 3 adjacent points in an expected location of the central 24° field that have P \ 5 % on the pattern deviation plot, one of which with P \ 1 %; glaucoma hemifield test ‘‘outside normal limits’’; PSD with a P value \ 5 %), intraocular pressure (IOP) higher than 21 mmHg (at the time of diagnosis on no medication) and open angles on gonioscopy. A reliable perimetry was an exam with less than 20 % fixation loss, and less than 33 % of both false negative and false positive. Glaucoma suspects comprised subjects with suspicious looking optic disks and normal visual fields as well as ocular hypertensive patients (IOP greater than 21 mmHg without hypotensive medication). Thirty-nine white subjects providing a sample of 73 eyes enrolled the study. Twenty-five patients were female (64.1 %) and 14 male (35.9 %). Mean age was 57.5 ± 10.7 years. Eighteen patients were white, 8 were African Brazilian, and 13 were mulatto. Table 1 depicts the demographic characteristics of the sample stratified by group. Proceedings The GCC images were retrieved from the RTVue-100 OCT hard drive (software version A4, 0, 5, 46; Optovue Inc, Freemont, CA). The OCT image is acquired on a very fast rate (26,000 A-scan/second), with a frame rate of 256–4096 A-scan/frame, and
Int Ophthalmol (2015) 35:81–87
provides high tissue resolution (depth resolution of 5.0 lm and transverse resolution of 15 lm). The acquisition of images was done previously by a technician following a standard procedure. After pharmacologic dilation, retinal ganglion cells in the macular region were assessed using the GCC scan protocol (MM7). The GCC scan covered a 7 9 7 mm scan area centered on the fovea. Images with signal strength index (SSI) less than 40 or with overt misalignment of the surface detection algorithm on at least 10 % of consecutive A-scans or 15 % of cumulative A-scans or with overt decentration of the measurement circle location were excluded from further analysis. Visual fields were assessed using the central 24–2 program (SITA standard strategy) with the Humphrey Field Analyzer II, model 750 (Zeiss Humphrey Systems, Dublin, CA) with the appropriate correction of refractive error prior. A trained technician previously did all visual field testing. Only the charts of patients with reliable tests were included. The time frame between OCT image acquisition and the visual field testing was between 3 days and three weeks. Statistical analysis Differences among the three groups were compared with the Fisher Exact test for categorical variables and the one-way analysis of variance (ANOVA) test for continuous variables. The relationship between structure and function was evaluated using both linear (y = a ? bx) and logarithm regression analysis (log(y) = a ? bx), as dB values are a logarithmic transformation of differential light sensitivity. Automated perimetry mean deviation (MD) and pattern standard deviation (PSD) recorded in dB were treated as the dependent variables and GCC parameters as the independent variables. Pearson’s product moment correlation coefficients with 95 % confidence intervals (95 % CI) were calculated to measure the degree of association between the seven GCC parameters (average, superior, inferior, S–I [superior inferior difference], S–I SD [standard deviation of S–I], GLV [global loss volume] and FLV [focal loss volume]) and two automated perimetry measures (MD and PSD). Statistical significance was set at P \ 0.05 and all analyses were done with MedCalcÒ software, version 9.3.7.0 (MedCalc Software bvba, Belgium).
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Results All linear correlations between GCC parameters and functional parameters were strong and achieved statistical significance, except for S–I and MD (r = -0.05, P = 0.66), S–I and PSD (r = 0.09, P = 0.44), S–I SD and MD (r = -0.06, P = 0.61), and S–I SD and PSD (r = 0.08, P = 0.47). Detailed data are depicted on the scaterplot matrix (Fig. 1). Logarithmic correlations between GCC parameters and functional parameters failed to reach statistical significance between S–I SD and MD (r = -0.135, P = 0.267) and between S–I SD and PSD (r = 0.144, P = .238). Logarithmic transformation of S–I was not possible because some values were negative. Figure 2 depicts the scaterplot matrix of all logarithmic regressions.
Discussion The results of this study revealed a strong correlation between most of GCC measurements (average, superior, inferior, GLV, and FLV) and AP parameters (MD and PSD), regardless of the type of regression. Particularly distinctive is the correlation between GLV and MD and that between FLV and PSD. GLV measures the average amount of GCC loss over the entire GCC map whereas FLV measures the average amount of focal loss over the entire GCC map. FLV detects focal loss using a pattern deviation map to correct for overall absolute changes and will best detect local ganglion cell loss. On the other hand, GLV will best detect diffuse ganglion cell loss. Similarly, the MD is a weighted average of the total deviation values in a visual field test; the lower its value, the more advanced the damage in visual function is. The PSD value is the standard deviation of the difference between the threshold value at each test location and the expected value and, as an indicator of localized defects, it reflects the roughness of the visual field. In other words, the GLV is similar to MD in AP and FLV is similar to PSD. We found no correlation between S–I and S–I SD with AP parameters. S–I is the difference between the superior and inferior hemiretina thickness and S–I SD the standard deviation of the difference. All GCC parameters were found to be sensitive for perimetric glaucoma diagnosis, except S–I [19]. Since S–I SD
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84
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MD r (95% CI)
PSD r (95% CI)
0.673 (0.525 to 0.782)
-0.629 (-0.750 to -0.466)
0.613 (0.446 to 0.739)
-0.559 (-0.699 to -0.378)
0.677 (0.530 to 0.785)
-0.643 (-0.760 to -0.484)
-0.052 (-0.279 to 0.179)
0.090 (-0.142 to 0.314)
-0.061 (-0.294 to 0.177)
0.090 (-0.152 to 0.317)
0.640 (0.481 to 0.758)
0.640 (0.481 to 0.758)
-0.701 (-0.803 to -0.559)
0.669 (0.516 to 0.780)
Average
Superior
Inferior
S-I
S-I SD
FLV
GLV
Fig. 1 Scatterplot matrix displaying the pairwise scatter diagrams of the linear correlation between ganglion cell complex and the functional parameters (S–I superior inferior
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difference, S–I SD standard deviation of S–I, GLV global loss volume, FLV focal loss volume, 95 % CI 95 % confidence interval)
Int Ophthalmol (2015) 35:81–87
85
MD r (95% CI)
PSD r (95% CI)
0.689 (0.546 to 0.793)
-0.651 (-0.766 to -0.495)
-0.598 (-0.728 to -0.427)
-0.559 (-0.699 to -0.378)
-0.669 (-0.729 to -0.518)
-0.643 (-0.760 to -0.484)
0.144 (-0.096 to 0.368)
0.090 (-0.152 to 0.317)
-0.464 (- 0.628 to - 0.261)
0.394 (0.178 to 0.573)
0.391 (0.173 to 0.572)
0.669 (0.516 to 0.780)
Average
Superior
Inferior
S-I SD
FLV
GLV
Fig. 2 Scatterplot matrix displaying the pairwise scatter diagrams of the logarithmic correlation between ganglion cell complex and the functional parameters (S–I SD standard
deviation of S–I, GLV: global loss volume, FLV focal loss volume, 95 % CI 95 % confidence interval)
revealed no correlation with the MD and PSD, it might be similar to S–I for glaucoma diagnosis as well. The GCC scan makes a 6 mm map, which is corresponding to about 20 degrees on the visual field
map. Specially, it is 10° for superior and inferior direction, 7° to nasal direction and 13° to temporal direction. Overlapping this map on a central 24–2 test, which we have used in the study, only the 12 central
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points would be covered. A central 10–2 visual field test, which tests 64 points on the central 10°, would overlap more with the GCC scan and may probably correlate more strongly [20]. However, even using the 24–2 program, we were able to detect a strong correlation between CCG and automated perimetry. Previous authors have evaluated the association between GCC and visual fields. Kim et al. assessed the relationship between GCC thickness with MD and visual field index by regression analysis and concluded that a curvilinear function best described the relationship between structure and function [21]. In our study, we have not used nonlinear (second-order and third-order polynomial) regression analysis to assess the relationships between mean GCC thickness and MD/PSD. Cho et al. evaluated the strength and pattern of the relationship between visual field mean sensitivity (MS) and GCC thickness in a group of 97 patients with glaucoma. On linear regression analysis, the association between MS and GCC was similar to that between MS and RNFL thickness [22]. In our study, we have used MD and PSD values instead of MS and the sample included POAG patients and suspects. Na et al. studied the relationship between the macular visual field MS and the GCC thickness, and macular peripapillary RNFL thickness (mpRNFLT) assessed by spectral domain OCT in 217 POAG patients. The authors noted that GCC thickness had a statistically significant structure–function association with macular visual field, and the strength of the association was greater than that of the mpRNFL with macular MS in the superior central visual field area [23]. In our study, GCC parameters were taken with the Fourier-domain OCT and visual field measurements used were those from the central 24-2 SITA program. Aggarwal et al. used Fourier-domain OCT to characterize the loss of nerve fiber layer (NFL) and GCC in non-arteritic ischemic optic neuropathy. Patients were stratified into superior field loss, inferior field loss, and bihemispheric field loss groups based on the SITA 30-2 achromatic visual field tests. Six months after presentation, they were scanned by FD-OCT to map peripapillary NFL and macular GCC thicknesses. Using Pearson’s correlation coefficient to assess the correlation between visual fields and OCT measurements, the authors noted that the GCC maps demonstrated clear hemispheric loss pattern in agreement with visual fields in both magnitude and
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location [24]. In our study, we have evaluated only patients with glaucomatous optic neuropathy and suspects. The use of non-logarithmic scale for MD and PSD to compare structural and functional measurements is more appropriate than the standard dB scale. However, the dB scale is more useful clinically, because the span of average thickness is smaller when GCC thickness data are used, and the strength of any association may thus appear to be less obvious when expressed in the non-logarithmic scale [22]. The use of a curvilinear model in association with the dB scale may lead to underestimation of the rate of change when thickness measurements are near normal, or patients with early stage of visual field loss, thus giving the false impression that a functional reserve is present and overestimates the rate of change in GCC thickness in patients with more advanced visual field loss [22]. In this study, we have used a logarithmic transformation of the structural parameters of GCC instead of transforming the MD and PSD values into a non-logarithmic scale, because the software used cannot calculate negative values. Nevertheless, the correlations using both linear and logarithmic regression analysis were equally robust. This study has some shortcomings. The retrospective nature of the study with use of non standardized secondary data poses a clear limitation. The sample size was relatively small to have a subanalysis per group (glaucoma patients, suspects, and ocular hypertensive). It would have been more interesting to evaluate how the structure–function correlation would behave in each particular group. In summary, the results of this study concur with previous literature and substantiates that GCC parameters have strong correlation with automated perimetry measurements. Financial support submission.
No financial support was received for this
Conflicts of interest None of the authors has conflict of interest with the submission.
References 1. Zeimer R, Asrani S, Zou S et al (1998) Quantitative detection of glaucomatous damage at the posterior pole by retinal thickness mapping: a pilot study. Ophthalmology 105:224–231
Int Ophthalmol (2015) 35:81–87 2. Guedes V, Schuman JS, Hertsmark E et al (2003) Optical coherence tomography: measurement of macular and nerve fiber layer thickness in normal and glaucomatous human eyes. Ophthalmology 110:177–189 3. Medeiros FA, Zangwill LM, Bowd C et al (2005) Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography. Am J Ophthalmol 139:44–55 4. Ishikawa H, Stein DM, Wollstein G et al (2005) Macular segmentation with optical coherence tomography. Investig Ophthalmol Vis Sci 46:2012–2017 5. Mori S, Hangai M, Sakamoto A, Yoshimura N (2010) Spectral-domain optical coherence tomography measurement of macular volume for diagnosing glaucoma. J Glaucoma 19:528–534 6. Garas A, Vargha P, Hollo G (2011) Diagnostic accuracy of nerve fiber layer, macular thickness and optic disc measurements made with the RTVue-100 optical coherence tomography to detect glaucoma. Eye 25:57–65 7. Takagi ST, Kita Y, Yagi F, Tomita G (2011) Macular retinal ganglion cell complex damage in the apparently normal visual field of glaucomatous eyes with hemifield defects. J Glaucoma 21:318–325 8. Kita Y, Kita R, Nitta A et al (2011) Glaucomatous eye macular ganglion cell complex thickness and its relation to temporal circumpapillary retinal nerve fiber layer thickness. Jpn J Ophthalmol 55:228–234 9. Kim NR, Hong S, Kim JH et al (2013) Comparison of macular ganglion cell complex thickness by Fourier-domain OCT in normal tension glaucoma and primary open-angle glaucoma. J Glaucoma 22:133–139 10. Sevim MS, Buttanri B, Acar BT et al (2013) Ability of Fourier-domain optical coherence tomography to detect retinal ganglion cell complex atrophy in glaucoma patients. J Glaucoma 22:542–549 11. Schulze A, Lamparter J, Pfeiffer N et al (2011) Diagnostic ability of retinal ganglion cell complex, retinal nerve fiber layer, and optic nerve head measurements by Fourierdomain optical coherence tomography. Graefes Arch Clin Exp Ophthalmol 249:1039–1045 12. Chen J, Huang H, Wang M et al (2012) Fourier domain OCT measurement of macular, macular ganglion cell complex, and peripapillary RNFL thickness in glaucomatous Chinese eyes. Eur J Ophthalmol 22:972–979 13. Arintawati P, Sone T, Akita T et al (2013) The applicability of ganglion cell complex parameters determined from SDOCT images to detect glaucomatous eyes. J Glaucoma. doi:10.1097/IJG.0b013e318259b2e1
87 14. Kita Y, Kita R, Takeyama A et al (2013) Ability of optical coherence tomography-determined ganglion cell complex thickness to total retinal thickness ratio to diagnose glaucoma. J Glaucoma 22:757–762 15. Morooka S, Hangai M, Nukada M et al (2012) Wide 3-dimensional macular ganglion cell complex imaging with spectral-domain optical coherence tomography in glaucoma. Investig Ophthalmol Vis Sci 53:4805–4812 16. Firat PG, Doganay S, Demirel EE, Colak C (2013) Comparison of ganglion cell and retinal nerve fiber layer thickness in primary open-angle glaucoma and normal tension glaucoma with spectral-domain OCT. Graefes Arch Clin Exp Ophthalmol 251:831–838 17. Sung MS, Kang BW, Kim HG et al (2013) Clinical validity of macular ganglion cell complex by spectral domainoptical coherence tomography in advanced glaucoma. J Glaucoma. doi:10.1097/IJG.0b013e318279c932 18. Bresciani-Battilana E, Teixeira IC, Barbosa DTQ et al (2014) Correlation between the ganglion cell complex and structural measures of the optic disc and retinal nerve fiber layer in glaucoma. Int J Ophthalmol. doi:10.1007/s10792014-9988-7 (in press) 19. Tan O, Chopra V, Lu A et al (2009) Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography. Ophthalmology 116:2305–2314 20. Huang D, Tan O (2013) Introduction to RTVue for glaucoma diagnosis. In: RTVue Primer Series: vol. III glaucoma: Fourier-domain optical coherence tomography. Accessed Mar 2013 Available from: http://www.opto.com. br/imgs/downloads/1281618551_glaucomaprimerreva.pdf 21. Kim NR, Lee ES, Seong GJ et al (2010) Structure-function relationship and diagnostic value of macular ganglion cell complex measurement using Fourier-domain OCT in glaucoma. Investig Ophthalmol Vis Sci 51:4646–4651 22. Cho JW, Sung KR, Lee S et al (2010) Relationship between visual field sensitivity and macular ganglion cell complex thickness as measured by spectral-domain optical coherence tomography. Investig Opthalmol Vis Sci 51:6401–6407 23. Na JH, Kook MS, Lee Y, Baek S (2012) Structure-function relationship of the macular visual field sensitivity and the ganglion cell complex thickness in glaucoma. Investig Ophthalmol Vis Sci 53:5044–5051 24. Aggarwal D, Tan O, Huang D, Sadun AA (2012) Patterns of ganglion cell complex and nerve fiber layer loss in nonarteritic ischemic optic neuropathy by Fourier-domain optical coherence tomography. Investig Ophthalmol Vis Sci 53:4539–4545
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ORIGINAL PAPER
Correlation between the ganglion cell complex and functional measures in glaucoma patients and suspects Ivan C. Teixeira • Erica Bresciani-Battilana • Diego T. Q. Barbosa • Cristiano Caixeta-Umbelino Maurı´cio D. Paolera • Niro Kasahara
•
Received: 1 September 2014 / Accepted: 20 November 2014 / Published online: 26 November 2014 Ó Springer Science+Business Media Dordrecht 2014
Abstract The purpose of the study was to correlate the Fourier-domain OCT ganglion cell complex (GCC) parameters with automated perimetry (AP) functional measures of the optic nerve. This retrospective study included patients who had previously undergone examination with the RTVue-100 OCT and AP, SITA strategy. The parameters of GCC (average, superior, inferior, S–I [superior inferior difference], S–I SD [standard deviation of S–I], GLV [global loss volume] and FLV [focal loss volume]) were correlated with the mean deviation (MD) and pattern standard deviation (PSD) values of AP using linear and logarithmic regression analysis. All correlations between GCC and automated perimetry parameters were strong (r [ 0.60), except that for SI and MD (r = -0.05); SI and PSD (r = 0.09); SI-DS and MD (r = -0.06); and SI-SD and PSD (r = 0.08). In summary, GCC derived structural measures showed good correlation with functional parameters from AP.
Presented at the 10th European Glaucoma Society Congress, Copenhagen on June 17–22, 2012. I. C. Teixeira E. Bresciani-Battilana D. T. Q. Barbosa C. Caixeta-Umbelino M. D. Paolera N. Kasahara (&) Department of Ophthalmology, Irmandade da Santa Casa de Misericordia de Sao Paulo, Santa Casa de Sao Paulo School of Medical Sciences, Rua Sao Mauro, 292, Sa˜o Paulo 02526-050, Brazil e-mail: [email protected]
Keywords Glaucoma Visual fields Ganglion cell complex Perimetry
Introduction Primary open angle glaucoma (POAG) is a multifactorial optic neuropathy characterized by a progressive loss of retinal ganglion cells and corresponding visual field defects. The structural assessment of the disease is usually done by evaluation of the optic nerve head (ONH) and the retinal nerve fiber layer (RNFL). Reports have suggested that macular thickness assessment could be a valuable measure of glaucomatous structural change, because glaucoma damage affects retinal ganglion cells, which are densely present in the macular region [1–4]. The ganglion cell complex (GCC) is a measure of the retinal ganglion cells in the macular region assessed by the Fourier-Domain optical coherence tomography (OCT). The GCC encompass three layers in the retina, the RNFL made of the ganglion cell axons, the ganglion cell layer made of the ganglion cell bodies, and the innerplexiform layer made of the ganglion cell dendrites. It has good diagnostic value to detect glaucoma and to differentiate between normal and glaucoma subjects [5–17]. We have previously demonstrated a strong correlation between the GCC parameters with structural measures of the ONH and the RNFL as evaluated by Fourier-domain OCT [18]. The correlation between
123
82 Table 1 Demographic characteristic of the sample
Int Ophthalmol (2015) 35:81–87
Variable
Glaucoma
Suspects
Ocular hypertensives
Age
62.5 ± 9.2
51.8 ± 10.2
55.4 ± 8.7
M:F
5:10
4:8
5:7
Ethnicity
M male, F female, IOP intraocular pressure, CD cup-to disk ratio, MD mean deviation, PSD pattern standard deviation, GCC ganglion cell complex, FLV focal loss volume, GLV global loss volume
8
3
7
Black
3
4
1
0.670
4
5
4
Mean IOP
18.3 ± 2.1
14.5 ± 3.2
24.6 ± 2.8
0.219
Median CD Mean MD
0.8 -7.8 ± 2.7
0.6 -1.7 ± 0.5
0.3 0.4 ± 1.2
0.000 0.020
Mean PSD
5.3 ± 2.2
2.1 ± 0.4
1.4 ± 0.2
0.449
GCC average
75.6 ± 11.0
89.7 ± 2.2
98.5 ± 3.7
\0.000
GCC superior
75.6 ± 10.5
90.3 ± 2.6
99.9 ± 7.2
\0.000
GCC inferior
75.6 ± 12.0
89.6 ± 3.1
97.5 ± 5.3
\0.000
FLV %
4.9 ± 3.6
0.8 ± 0.2
0.2 ± 0.1
\0.000
GLV %
18.2 ± 12.1
6.4 ± 4.3
4.6 ± 3.6
\0.000
the GCC and functional measures of glaucoma has not been extensively studied. The purpose of this study was to access the correlation of the Fourier-domain OCT GCC parameters with automated perimetry functional measures of the optic nerve.
Materials and methods Study population This retrospective study included both patients with POAG and suspects older than 40 years of age of both genders, with 20/20 best corrected visual acuity, and any ethnicity. Subjects with cataracts or any other ocular disease and previous incision or laser surgery for glaucoma were not included in the study. Institution Board Review approved the study and the procedures followed adhered to the principles for medical research involving human subjects of the Declaration of Helsinki in 1964 (amended by the 59th WMA General Assembly, Seoul, Korea, October 2008). The charts of all eligible subjects were reviewed in order to verify if they fulfilled the inclusion criteria and all data for the study extracted by one of us. In order to be included in the study, POAG patients had to have typical optic disk damage (diffuse or localized rim thinning, enlarged cupping, disk hemorrhage, asymmetry in cup-to disk ratio 0.2 or greater between eyes) with corresponding visual
123
0.016 0.865
White Mixed
P value
field loss on a reliable perimetry exam (at least 3 adjacent points in an expected location of the central 24° field that have P \ 5 % on the pattern deviation plot, one of which with P \ 1 %; glaucoma hemifield test ‘‘outside normal limits’’; PSD with a P value \ 5 %), intraocular pressure (IOP) higher than 21 mmHg (at the time of diagnosis on no medication) and open angles on gonioscopy. A reliable perimetry was an exam with less than 20 % fixation loss, and less than 33 % of both false negative and false positive. Glaucoma suspects comprised subjects with suspicious looking optic disks and normal visual fields as well as ocular hypertensive patients (IOP greater than 21 mmHg without hypotensive medication). Thirty-nine white subjects providing a sample of 73 eyes enrolled the study. Twenty-five patients were female (64.1 %) and 14 male (35.9 %). Mean age was 57.5 ± 10.7 years. Eighteen patients were white, 8 were African Brazilian, and 13 were mulatto. Table 1 depicts the demographic characteristics of the sample stratified by group. Proceedings The GCC images were retrieved from the RTVue-100 OCT hard drive (software version A4, 0, 5, 46; Optovue Inc, Freemont, CA). The OCT image is acquired on a very fast rate (26,000 A-scan/second), with a frame rate of 256–4096 A-scan/frame, and
Int Ophthalmol (2015) 35:81–87
provides high tissue resolution (depth resolution of 5.0 lm and transverse resolution of 15 lm). The acquisition of images was done previously by a technician following a standard procedure. After pharmacologic dilation, retinal ganglion cells in the macular region were assessed using the GCC scan protocol (MM7). The GCC scan covered a 7 9 7 mm scan area centered on the fovea. Images with signal strength index (SSI) less than 40 or with overt misalignment of the surface detection algorithm on at least 10 % of consecutive A-scans or 15 % of cumulative A-scans or with overt decentration of the measurement circle location were excluded from further analysis. Visual fields were assessed using the central 24–2 program (SITA standard strategy) with the Humphrey Field Analyzer II, model 750 (Zeiss Humphrey Systems, Dublin, CA) with the appropriate correction of refractive error prior. A trained technician previously did all visual field testing. Only the charts of patients with reliable tests were included. The time frame between OCT image acquisition and the visual field testing was between 3 days and three weeks. Statistical analysis Differences among the three groups were compared with the Fisher Exact test for categorical variables and the one-way analysis of variance (ANOVA) test for continuous variables. The relationship between structure and function was evaluated using both linear (y = a ? bx) and logarithm regression analysis (log(y) = a ? bx), as dB values are a logarithmic transformation of differential light sensitivity. Automated perimetry mean deviation (MD) and pattern standard deviation (PSD) recorded in dB were treated as the dependent variables and GCC parameters as the independent variables. Pearson’s product moment correlation coefficients with 95 % confidence intervals (95 % CI) were calculated to measure the degree of association between the seven GCC parameters (average, superior, inferior, S–I [superior inferior difference], S–I SD [standard deviation of S–I], GLV [global loss volume] and FLV [focal loss volume]) and two automated perimetry measures (MD and PSD). Statistical significance was set at P \ 0.05 and all analyses were done with MedCalcÒ software, version 9.3.7.0 (MedCalc Software bvba, Belgium).
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Results All linear correlations between GCC parameters and functional parameters were strong and achieved statistical significance, except for S–I and MD (r = -0.05, P = 0.66), S–I and PSD (r = 0.09, P = 0.44), S–I SD and MD (r = -0.06, P = 0.61), and S–I SD and PSD (r = 0.08, P = 0.47). Detailed data are depicted on the scaterplot matrix (Fig. 1). Logarithmic correlations between GCC parameters and functional parameters failed to reach statistical significance between S–I SD and MD (r = -0.135, P = 0.267) and between S–I SD and PSD (r = 0.144, P = .238). Logarithmic transformation of S–I was not possible because some values were negative. Figure 2 depicts the scaterplot matrix of all logarithmic regressions.
Discussion The results of this study revealed a strong correlation between most of GCC measurements (average, superior, inferior, GLV, and FLV) and AP parameters (MD and PSD), regardless of the type of regression. Particularly distinctive is the correlation between GLV and MD and that between FLV and PSD. GLV measures the average amount of GCC loss over the entire GCC map whereas FLV measures the average amount of focal loss over the entire GCC map. FLV detects focal loss using a pattern deviation map to correct for overall absolute changes and will best detect local ganglion cell loss. On the other hand, GLV will best detect diffuse ganglion cell loss. Similarly, the MD is a weighted average of the total deviation values in a visual field test; the lower its value, the more advanced the damage in visual function is. The PSD value is the standard deviation of the difference between the threshold value at each test location and the expected value and, as an indicator of localized defects, it reflects the roughness of the visual field. In other words, the GLV is similar to MD in AP and FLV is similar to PSD. We found no correlation between S–I and S–I SD with AP parameters. S–I is the difference between the superior and inferior hemiretina thickness and S–I SD the standard deviation of the difference. All GCC parameters were found to be sensitive for perimetric glaucoma diagnosis, except S–I [19]. Since S–I SD
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MD r (95% CI)
PSD r (95% CI)
0.673 (0.525 to 0.782)
-0.629 (-0.750 to -0.466)
0.613 (0.446 to 0.739)
-0.559 (-0.699 to -0.378)
0.677 (0.530 to 0.785)
-0.643 (-0.760 to -0.484)
-0.052 (-0.279 to 0.179)
0.090 (-0.142 to 0.314)
-0.061 (-0.294 to 0.177)
0.090 (-0.152 to 0.317)
0.640 (0.481 to 0.758)
0.640 (0.481 to 0.758)
-0.701 (-0.803 to -0.559)
0.669 (0.516 to 0.780)
Average
Superior
Inferior
S-I
S-I SD
FLV
GLV
Fig. 1 Scatterplot matrix displaying the pairwise scatter diagrams of the linear correlation between ganglion cell complex and the functional parameters (S–I superior inferior
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difference, S–I SD standard deviation of S–I, GLV global loss volume, FLV focal loss volume, 95 % CI 95 % confidence interval)
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85
MD r (95% CI)
PSD r (95% CI)
0.689 (0.546 to 0.793)
-0.651 (-0.766 to -0.495)
-0.598 (-0.728 to -0.427)
-0.559 (-0.699 to -0.378)
-0.669 (-0.729 to -0.518)
-0.643 (-0.760 to -0.484)
0.144 (-0.096 to 0.368)
0.090 (-0.152 to 0.317)
-0.464 (- 0.628 to - 0.261)
0.394 (0.178 to 0.573)
0.391 (0.173 to 0.572)
0.669 (0.516 to 0.780)
Average
Superior
Inferior
S-I SD
FLV
GLV
Fig. 2 Scatterplot matrix displaying the pairwise scatter diagrams of the logarithmic correlation between ganglion cell complex and the functional parameters (S–I SD standard
deviation of S–I, GLV: global loss volume, FLV focal loss volume, 95 % CI 95 % confidence interval)
revealed no correlation with the MD and PSD, it might be similar to S–I for glaucoma diagnosis as well. The GCC scan makes a 6 mm map, which is corresponding to about 20 degrees on the visual field
map. Specially, it is 10° for superior and inferior direction, 7° to nasal direction and 13° to temporal direction. Overlapping this map on a central 24–2 test, which we have used in the study, only the 12 central
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points would be covered. A central 10–2 visual field test, which tests 64 points on the central 10°, would overlap more with the GCC scan and may probably correlate more strongly [20]. However, even using the 24–2 program, we were able to detect a strong correlation between CCG and automated perimetry. Previous authors have evaluated the association between GCC and visual fields. Kim et al. assessed the relationship between GCC thickness with MD and visual field index by regression analysis and concluded that a curvilinear function best described the relationship between structure and function [21]. In our study, we have not used nonlinear (second-order and third-order polynomial) regression analysis to assess the relationships between mean GCC thickness and MD/PSD. Cho et al. evaluated the strength and pattern of the relationship between visual field mean sensitivity (MS) and GCC thickness in a group of 97 patients with glaucoma. On linear regression analysis, the association between MS and GCC was similar to that between MS and RNFL thickness [22]. In our study, we have used MD and PSD values instead of MS and the sample included POAG patients and suspects. Na et al. studied the relationship between the macular visual field MS and the GCC thickness, and macular peripapillary RNFL thickness (mpRNFLT) assessed by spectral domain OCT in 217 POAG patients. The authors noted that GCC thickness had a statistically significant structure–function association with macular visual field, and the strength of the association was greater than that of the mpRNFL with macular MS in the superior central visual field area [23]. In our study, GCC parameters were taken with the Fourier-domain OCT and visual field measurements used were those from the central 24-2 SITA program. Aggarwal et al. used Fourier-domain OCT to characterize the loss of nerve fiber layer (NFL) and GCC in non-arteritic ischemic optic neuropathy. Patients were stratified into superior field loss, inferior field loss, and bihemispheric field loss groups based on the SITA 30-2 achromatic visual field tests. Six months after presentation, they were scanned by FD-OCT to map peripapillary NFL and macular GCC thicknesses. Using Pearson’s correlation coefficient to assess the correlation between visual fields and OCT measurements, the authors noted that the GCC maps demonstrated clear hemispheric loss pattern in agreement with visual fields in both magnitude and
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location [24]. In our study, we have evaluated only patients with glaucomatous optic neuropathy and suspects. The use of non-logarithmic scale for MD and PSD to compare structural and functional measurements is more appropriate than the standard dB scale. However, the dB scale is more useful clinically, because the span of average thickness is smaller when GCC thickness data are used, and the strength of any association may thus appear to be less obvious when expressed in the non-logarithmic scale [22]. The use of a curvilinear model in association with the dB scale may lead to underestimation of the rate of change when thickness measurements are near normal, or patients with early stage of visual field loss, thus giving the false impression that a functional reserve is present and overestimates the rate of change in GCC thickness in patients with more advanced visual field loss [22]. In this study, we have used a logarithmic transformation of the structural parameters of GCC instead of transforming the MD and PSD values into a non-logarithmic scale, because the software used cannot calculate negative values. Nevertheless, the correlations using both linear and logarithmic regression analysis were equally robust. This study has some shortcomings. The retrospective nature of the study with use of non standardized secondary data poses a clear limitation. The sample size was relatively small to have a subanalysis per group (glaucoma patients, suspects, and ocular hypertensive). It would have been more interesting to evaluate how the structure–function correlation would behave in each particular group. In summary, the results of this study concur with previous literature and substantiates that GCC parameters have strong correlation with automated perimetry measurements. Financial support submission.
No financial support was received for this
Conflicts of interest None of the authors has conflict of interest with the submission.
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