October 2011
Volume 52, Issue 11
Free
Glaucoma  |   October 2011
Macular and Retinal Nerve Fiber Layer Thickness: Which Is More Helpful in the Diagnosis of Glaucoma?
Author Affiliations & Notes
  • Jung Hwa Na
    From the Departments of Ophthalmology and
  • Kyung Rim Sung
    From the Departments of Ophthalmology and
  • Seunghee Baek
    Clinical Epidemiology and Biostatistics, College of Medicine, University of Ulsan, Asan Medical Center, Seoul, Korea.
  • Jae Hong Sun
    From the Departments of Ophthalmology and
  • Youngrok Lee
    From the Departments of Ophthalmology and
  • Corresponding author: Kyung Rim Sung, Department of Ophthalmology, University of Ulsan, College of Medicine, Asan Medical Center, 388-1 Pungnap-2-dong, Songpa-gu, Seoul, Korea 138-736; sungeye@gmail.com
Investigative Ophthalmology & Visual Science October 2011, Vol.52, 8094-8101. doi:10.1167/iovs.11-7833
  • Views
  • PDF
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Jung Hwa Na, Kyung Rim Sung, Seunghee Baek, Jae Hong Sun, Youngrok Lee; Macular and Retinal Nerve Fiber Layer Thickness: Which Is More Helpful in the Diagnosis of Glaucoma?. Invest. Ophthalmol. Vis. Sci. 2011;52(11):8094-8101. doi: 10.1167/iovs.11-7833.

      Download citation file:


      © 2015 Association for Research in Vision and Ophthalmology.

      ×
  • Supplements
Abstract

Purpose.: To explore factors affecting the glaucoma diagnostic capability of macular thickness and circumpapillary retinal nerve fiber layer (cRNFL) thickness as determined by spectral domain–optical coherence tomography.

Methods.: Area under the curve (AUC) of the receiver operating characteristics (ROC) discriminating healthy from glaucomatous eyes was determined using 10 macular thickness and 16 cRNFL thickness parameters. Glaucomatous eyes were categorized into two groups using four criteria according to whether cRNFL thickness or macular thickness measurement was better for glaucoma detection (cRNFL better group [RBG], macula better group [MBG], respectively). The clinical characteristics were compared between RBG and MBG. ROC regression analysis was conducted to identify variables affecting diagnostic capability using either macular thickness or cRNFL thickness measurements.

Results.: Four hundred twenty-four glaucomatous patients and 297 healthy subjects were analyzed. Of all cRNFL parameters, average thickness showed the largest AUC (0.958). Of macular parameters, the inferior outer sector showed the largest AUC (0.880). More eyes were placed into the RBG than the MBG, making use of all four criteria (90 vs. 24, 143 vs. 46, 76 vs. 18, and 103 vs. 36, respectively). RBG patients had a smaller optic disc area than did MBG patients in 3 of the 4 criteria. Signal strength affected the diagnostic performance of cRNFL thickness measurement (P = 0.043), whereas that of macular thickness was not affected by any covariate analyzed.

Conclusions.: Overall, cRNFL thickness measurements were generally superior to those of macular thickness when used to diagnose glaucoma. Macular thickness parameters were of greater value in eyes with larger optic discs.

Optical coherence tomography (OCT) is recognized as an important diagnostic tool in the structural diagnosis of glaucoma. The clinical usefulness of OCT has been demonstrated in numerous studies. 1 14 An increasing number of clinics use OCT for glaucoma workup. OCT technology has evolved from time-domain (TD) to spectral-domain (SD); the latter offers higher axial resolution and scan speed. The Cirrus SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec, Inc., Dublin, CA) is one such commercially available platform. The current version of Cirrus OCT yields circumpapillary retinal nerve fiber layer (cRNFL) thickness when operated in the optic disc cube mode and macular thickness when the macular cube mode is used. 
cRNFL and macular thickness differ in character. First, cRNFL thickness is usually determined by processing data from a 3.4-mm circumpapillary scan. A mild RNFL defect that is not located on the 3.4-mm scan circle may be missed. Moreover, the nasal and temporal side of the optic disc (where early glaucomatous change is rare) is included in the analysis, which may reduce the sensitivity of glaucoma detection if average RNFL thickness is considered. 
In the meantime, macular thickness measurement usually makes use of posterior pole data, centered on the fovea. Thus, part of the superior and inferior and all the temporal sides of the optic disc are included, but the nasal side is not. However, macular thickness estimates the total retinal thickness, including cell layers that are not affected in glaucoma, which may affect the sensitivity of the test. Another disadvantage of the macula analysis is that only ∼50% of the retinal ganglion cells are sampled in the scanned area compared with the full sample of the ganglion cell axons by the cRNFL scan, which might also reduce sensitivity. 
In contrast, measuring the thickness of the cRNFL can be more specific for the diagnosis of glaucoma because it contains only retinal ganglion cell axons. Given these differences between cRNFL and macular thickness measurements, we hypothesized that one assessment might be superior to the other when diagnosing glaucoma in specific patients under certain circumstances. From a clinical viewpoint, it is important to know which measurement is better under which conditions. In addition, it is important for clinicians to know the factors that affect the diagnostic capacities of the two measurements. To resolve these issues, we classified a relatively large group of glaucomatous eyes into two groups using various criteria. Specifically, the groups were termed the cRNFL-better group (RBG), in whom cRNFL measurements were superior for glaucoma diagnosis, and the macula-better group (MBG). The clinical characteristics of the two groups were compared. Additionally, we evaluated the effects of various clinical factors on the glaucoma diagnostic capability of each macular and RNFL thickness parameter. 
Subjects and Methods
Subjects
All study subjects were recruited prospectively, in a consecutive manner, between April 2009 and September 2010 at the Asan Medical Center, Seoul, Korea. At initial evaluation, each patient underwent a complete ophthalmologic examination, including the taking of medical, ocular, and family histories; best-corrected visual acuity (BCVA) assessment; slit-lamp biomicroscopy; Goldmann applanation tonometry (GAT); gonioscopy; dilated funduscopic examination using a 90- or 78-diopter (D) lens; stereoscopic optic disc photography; a visual field (VF) test using a Humphrey field analyzer running the Swedish Interactive Threshold Algorithm 24-2 (Carl Zeiss Meditec, Dublin, CA); axial length measurement by IOL master (Carl Zeiss Meditec); central corneal thickness (CCT) measurement by ultrasound pachymetry (DGH-550; DGH Technology Inc., Exton, PA), and cRNFL and macular thickness measurement by Cirrus HD-OCT (Carl Zeiss Meditec, Inc.). To minimize any learning effect, the first VF test results were excluded from analysis. 
For inclusion, each participant had to meet the following criteria: age older than 19 years; BCVA of 20/40 or better, with a spherical refraction ranging from −6 diopters (D) to +3 D and cylinder correction within +3 D; presence of a normal anterior chamber and open angle on slit-lamp and gonioscopic examinations; and reliable VF test results, with a false-positive error rate <15%, a false-negative error rate <15%, and a fixation loss rate <20%. Patients with evidence of intracranial or otolaryngeal lesions, a history of massive hemorrhage or hemodynamic crisis, any other ophthalmic disease that could result in VF defects, or a history of diabetes mellitus or eye surgery/laser treatment were excluded. One eye was randomly selected if both eyes were found to be eligible. Glaucoma was defined by the presence of glaucomatous VF defects regardless of optic disc or RNFL appearance. Glaucomatous VF defects had to meet at least two of the following criteria: (1) a cluster of three points with a probability of <5% on a pattern deviation map in at least one hemifield, including at least one point with a probability of <1% or a cluster of two points with a probability of <1%; (2) glaucoma hemifield test results outside normal limits; (3) a pattern SD (PSD) <5%. If an eye showed such VF defects in the central 10°, it was diagnosed as having central scotoma. The presence of peripapillary atrophy was also determined and agreed on by two glaucoma experts (KRS, JHN) when assessing optic disc photography. Healthy eyes of hospital staff, their families, and patients' spouses formed the control group. No control subject had any history of ocular symptoms or disease and had not been treated with intraocular incisional or laser surgery. All control eyes had IOP values lower than 22 mm Hg, with no history of IOP elevation, and were normal by both optic disc and VF examinations. 
All procedures conformed to the Declaration of Helsinki, and the study was approved by the Institutional Review Board of the Asan Medical Center at the University of Ulsan, Seoul, Korea. 
Optical Coherence Tomography
cRNFL thickness measurements were obtained by Cirrus HD-OCT optic disc cube mode, which provides cRNFL thickness measurements for average, quadrants (superior, inferior, nasal, and temporal), and 12 clock hours. Macula thickness was determined by macula cube mode. Thickness measurements for nine sectors (superior outer, inferior outer, nasal outer, temporal outer, superior inner, inferior inner, nasal inner, temporal inner, and fovea), central subfield thickness, cube volume, and cube average thickness were provided. Pharmacologic dilation was performed on all eyes. All images were acquired by a single well-trained technician during the same patient visit. Only images with signal strength (SS) ≥7 were included in the analysis. In addition, we excluded images with horizontal eye motion within the measurement circle in the en face image of the optic disc scan. Macular scans with eye motion that appeared as discontinuous blood vessels in the en face image were also excluded. 
Statistical Analysis
Comparisons between the RBG and MBG.
To separate patients into the RBG and MBG, glaucomatous eyes were classified using four criteria. First, area under the curve (AUC) of the receiver operating characteristics (ROC) discriminating between healthy and glaucomatous eyes was determined using the average, quadrant, and clock-hour thicknesses for cRNFL and the average and sectoral macular thicknesses. The cutoff values of average and sectoral thickness that showed the highest combination of specificity and sensitivity among each macular and cRNFL parameter were also calculated from ROC curves. 
Once cutoff values for average thickness were defined, glaucomatous eyes with cRNFL thickness parameters equal to or lower than the cutoff value and macular thickness parameters higher than the cutoff value were classified as the cRNFL better group (aRBG). Glaucomatous eyes with macular thickness parameters equal to or lower than the cutoff value and cRNFL thickness parameters higher than the cutoff value were classified as the macular better group (aMBG). Using cutoff values for best sectoral thickness (highest AUC), glaucomatous eyes were classified as either abRBG or abMBG. 
In addition, using normative classification, glaucomatous eyes that showed worse classification in either the cRNFL or the macular thickness parameter of average and best performing sector were classified as nRBG and nbRBG or as nMBG and nbMBG. For instance, abnormal classification in average RNFL thickness and borderline classification in macular thickness was classified as nRBG. If an eye showed the same classification in cRNFL and macular thickness parameters, that eye was not classified as either RBG or MBG. In other words, if both were abnormal or borderline or normal, they were not classified as either RBG or MBG. 
Clinical characteristics, including VF data, were compared between each subclass of RBG and MBG (e.g., aRBG vs. aMBG). Normally distributed data were compared using the independent t-test, whereas nonparametric data were analyzed using the Mann-Whitney U test. To compare categorical data, the χ2 test was used. 
ROC Regression Analysis.
To evaluate the effects of various clinical factors on the glaucoma diagnostic capability of each macular and RNFL thickness parameter, multivariate ROC regression analysis was performed as proposed by Alonzo and Pepe. 15 The ROC regression model (ROC generalized linear model) 15 with probit link can be written as   where α1 and α2 are the intercept and slope of the ROC curve, respectively. When q is 1-specificity of the test, ROC(q) represents sensitivity. The coefficient β for the covariate X greater than zero, which represents the discrimination between those with disease and those without disease, increases with increasing values of X. Discrimination increases with the increasing value of a certain covariate, X, when the coefficient, β, is greater than zero. Interaction terms between the variables and Φ−1(q) were included to allow the effects of the covariates to differ by varying amounts, depending on the FPRq (or specificity1 − q)—that is, to influence the shape of the curve. However, we found no covariate had a significant effect on the interaction term. Thus, the interaction terms were omitted in the final models. Age and sex were adjusted using a linear model, with empirically estimated error distribution. 16,17 To obtain confidence intervals for regression parameters, a bootstrap resampling procedure was used (n = 1000 resamples). Univariate analysis was performed using each variable of VF_mean deviation (MD), VF_PSD, visual field index (VFI), spherical equivalent (SE), disc_area determined by Cirrus HD-OCT optic disc cube mode, VA, SS, and CCT. Among those, a set of variables that met a significance level <0.2 was entered in a multivariate regression analysis. P < 0.05 was considered significant. A final model with no significant factors for each parameter is not presented. 
All statistical analyses were performed using commercially available statistical software (SPSS version 15.0; SPSS Inc., Chicago, IL; and MedCalc version 11.3.6.0; MedCalc, Mariakerke, Belgium), and R version 2.12.0 (free software that can be downloaded from http://www.r-project.org). 
Results
Four hundred twenty-four glaucomatous eyes and 297 healthy eyes that met the initial inclusion criteria were enrolled in the present study. The baseline characteristics of the glaucomatous and healthy eyes are summarized in Table 1
Table 1.
 
Baseline Characteristics of Glaucomatous and Healthy Eyes
Table 1.
 
Baseline Characteristics of Glaucomatous and Healthy Eyes
Glaucomatous Healthy P
Eyes, n 424 297
Age, y 57.9 ± 12.1 50.7 ± 11.1 <0.001*
Male/female, n 233/191 137/160 0.020†
Spherical equivalent, D −0.72 ± 1.94 −0.30 ± 1.33 0.001*
Intraocular pressure, mm Hg 14.9 ± 3.7 14.97 ± 3.1 0.772*
Central corneal thickness, μm 536.1 ± 34.5 543.4 ± 31.7 0.020*
Axial length, mm 23.8 ± 0.9 23.9 ± 0.8 0.462*
VFMD, dB −5.02 (−10.01, −2.49) −0.12 (−0.88, −0.83) <0.001‡
VP PSD, dB 6.22 (3.17, 10.55) 1.50 (1.34, 1.65) <0.001‡
Of all the cRNFL parameters, average thickness showed the largest AUC (0.958). Of the sectoral values, the inferior quadrant thickness had the greatest AUC (0.956). Of macular parameters, the inferior outer sector showed the largest AUC (0.880). The cutoff AUC values for average and inferior quadrant RNFL thickness were 87 μm and 110 μm, whereas the cutoffs for average and inferior outer sector macular thickness were 269 μm and 252 μm, respectively (Table 2). 
Table 2.
 
AUCs of cRNFL and Macular Thickness Parameters
Table 2.
 
AUCs of cRNFL and Macular Thickness Parameters
Parameter AUC (95% CI) Cutoff Value Sensitivity Specificity
cRNFL Parameters
Average, μm 0.958 (0.942–0.970) 87 87.10 94.45
Sector, μm
Quadrant
    Temporal 0.823 (0.793–0.851) 63 73.56 76.52
    Superior 0.914 (0.894–0.933) 108 80.60 90.47
    Nasal 0.765 (0.732–0.788) 66 78.31 59.36
    Inferior 0.956 (0.939–0.969) 110 86.59 94.43
12-Clock hours
    9 0.695 (0.661–0.729) 50 54.15 77.57
    10 0.810 (0.781–0.840) 71 62.59 86.83
    11 0.880 (0.884–0.902) 115 76.03 87.53
    12 0.831 (0.802–0.856) 100 66.80 88.48
    1 0.839 (0.811–0.864) 96 73.17 83.21
    2 0.779 (0.751–0.807) 74 74.97 66.90
    3 0.608 (0.572–0.641) 54 49.71 66.83
    4 0.708 (0.674–0.738) 61 67.32 64.76
    5 0.872 (0.847–0.894) 85 72.33 88.54
    6 0.918 (0.896–0.936) 107 78.47 94.76
    7 0.923 (0.903–0.941) 120 81.50 92.75
    8 0.797 (0.769–0.830) 58 62.59 86.47
Macular Parameters
Cube average, μm 0.866 (0.840–0.891) 269 72.01 89.00
Cube volume, mm3 0.869 (0.843–0.893) 9.7 72.30 88.67
Sector, μm
    Temporal outer 0.826 (0.797–0.852) 254 71.17 82.26
    Superior outer 0.816 (0.786–0.842) 275 75.75 76.38
    Nasal outer 0.806 (0.776–0.833) 287 65.92 86.54
    Inferior outer 0.880 (0.854–0.902) 252 71.61 94.42
    Temporal inner 0.767 (0.738–0.797) 297 61.51 86.52
    Superior inner 0.716 (0.683–0.749) 309 52.19 87.20
    Nasal inner 0.667 (0.633–0.701) 307 41.53 89.81
    Inferior inner 0.773 (0.741–0.802) 305 65.49 83.27
    Fovea (central) 0.509 (0.473–0.545) 239 36.24 70.80
By average thickness assessment, 90 eyes were classified into the aRBG and 24 into the aMBG. When the best performing sectoral parameter was conducted, 76 eyes were classified into the abRBG and 18 into the abMBG. By average thickness normative classification, 143 eyes were classified into the nRBG and 46 into the nMBG. By best performing sectoral thickness normative classification, 103 eyes were classified into the nbRBG and 36 into the nbMBG. Therefore, more eyes were classified into the RBG than the MBG using all four criteria. 
Eyes in the aRBG showed significantly lower SE, smaller disc areas, and lower RNFL SS than eyes in the aMBG (P = 0.002, P = 0.016, and P = 0.043, respectively) (Table 3). Eyes in the abRBG had lower VF MD, higher PSD, and smaller disc areas than abMBG eyes (P = 0.047, P = 0.011, and P = 0.017, respectively) (Table 4). 
Table 3.
 
Comparison of aMBG and aRBG Patients (Grouped in Terms of the Cutoff Values of Average Thickness)
Table 3.
 
Comparison of aMBG and aRBG Patients (Grouped in Terms of the Cutoff Values of Average Thickness)
aMBG aRBG P
Eyes, n 24 90
Age, y 64 (54, 71) 57 (49, 65) 0.019*
Male/female, n 10/14 50/40 0.256†
Eyes, OD/OS 10/14 49/41 0.358†
SE, D 0.31 ± 1.51 −0.87 ± 1.83 0.002‡
IOP, mmn Hg 13.58 ± 2.52 14.56 ± 3.99 0.260‡
CCT, μm 530 (523, 557) 530 (511, 554) 0.563*
Axial length, mm 23.75 ± 0.37 23.57 ± 0.95 0.755‡
Disc area, mm2 2.31 ± 0.44 2.04 ± 0.48 0.016‡
Presence of PPA, n (%) 11 (45.8) 35 (38.9) 0.349†
VFMD, dB −3.31 (−8.01, −1.34) −3.23 (−6.46, −1.89) 0.792*
VFPSD, dB 4.11 (2.68, 7.03) 4.83 (2.92, 7.53) 0.284*
Presence of central scotoma, n (%) 11 (45.8) 49 (54.4) 0.496†
cRNFL SS 8.06 ± 0.78 7.72 ± 0.80 0.043‡
MAC SS 8.29 ± 1.04 8.02 ± 0.98 0.262‡
Table 4.
 
Comparison between abMBG and abMBG Patients (Grouped in Terms of the Cutoff Value of the Sector with the Best AUC)
Table 4.
 
Comparison between abMBG and abMBG Patients (Grouped in Terms of the Cutoff Value of the Sector with the Best AUC)
abMEG abRBG P
Eyes,n 18 76
Age, y 62.5 (51.0, 71.3) 59.0 (51.0, 65.0) 0.256*
Male/female, n 7/11 44/32 0.190†
Eyes, OD/OS 8/10 43/33 0.433†
SE, D 0.13 ± 2.00 −0.69 ± 1.78 0.133‡
IOP, mm Hg 14.5 (13.0, 17.25) 14(12.0, 16.0) 0.293*
CCT, μm 533 (521, 602) 543 (511, 560) 0.659*
Axial length, mm 23.67 ± 0.01 23.83 ± 0.67 0.200‡
Disc area, mm2 2.31 ± 0.35 2.05 ± 0.48 0.017‡
Presence of PPA, n (%) 7 (38.9) 25 (32.9) 0.783
VF MD, dB −2.26 (−5.04, −1.08) −3.68 (−7.73, −1.91) 0.047*
VF PSD, dB 2.95 (2.41, 5.41) 5.27 (3.11, 7.80) 0.011*
Presence of central scotoma, n (%) 10 (55.6) 25 (32.9) 0.104†
cRNFL SS 7.94 ± 0.74 7.68 ± 0.82 0.199‡
MAC SS 8.28 ± 0.96 8.00 ± 0.88 0.272‡
Eyes in the nRBG were younger, had smaller disc areas, and had less frequent central scotomas than nMBG eyes (P = 0.028, P = 0.003, and P = 0.045, respectively) (Table 5). Eyes in the nbRBG were younger age and had lower VF MD and higher VF PSD than nMBG eyes (P = 0.002, 0.023, and 0.014, respectively) (Table 6). 
Table 5.
 
Comparison between nMBG and nRBG Patients (Grouped by Normative Average Thickness Data)
Table 5.
 
Comparison between nMBG and nRBG Patients (Grouped by Normative Average Thickness Data)
nMBG nRBG P
Eyes, n 46 143
Age, y 60.54 ± 13.15 56.07 ± 11.45 0.028*
Male/female, n 19/27 93/50 0.006†
Eyes, OD/OS 22/24 73/70 0.737†
SE, D −0.55 ± 2.15 −0.97 ± 1.93 0.210*
IOP, mm Hg 14.54 ± 2.63 14.73 ± 3.56 0.697*
CCT, μm 548.55 ± 35.45 531.60 ± 36.30 0.103*
Axial length, mm 24.30 ± 0.81 23.72 ± 1.05 0.216*
Disc area, mm2 2.20 ± 0.41 1.98 ± 0.44 0.003*
Presence of PPA, n (%) 24 (52.2) 70 (49.0) 0.737†
VF MD, dB −5.46 (−8.29, −2.72) −4.83 (−10.50, −2.50) 0.747†
VF PSD, dB 6.01 (3.26, 8.85) 7.49 (3.34, 10.84) 0.225†
Presence of central scotoma, n (%) 37 (80.4) 92 (64.3) 0.045†
cRNFL SS 7.97 ± 0.89 7.85 ± 0.86 0.466*
MAC SS 8.07 ± 0.95 8.09 ± 1.03 0.881*
Table 6.
 
Comparison of nbMBG and nbRBG Patients (Grouped According to Normative Data from the Sector with the Best AUC)
Table 6.
 
Comparison of nbMBG and nbRBG Patients (Grouped According to Normative Data from the Sector with the Best AUC)
nbMEG nbRBG P
Eyes,n 36 103
Age, y 63.56 ± 12.55 55.92 ± 12.07 0.002*
Male/female, n 22/14 69/34 0.546†
Eyes, OD/OS 15/21 53/50 0.339†
SE, D −0.26 ± 1.97 −0.83 ± 1.85 0.138*
IOP, mmHg 14.53 ± 3.64 14.51 ± 3.31 0.984*
CCT, μm 548.26 ± 31.80 532.52 ± 31.67 0.147*
Axial length, mm 23.33 ± 0.83 23.76 ± 0.98 0.256*
Disc area, mm2 2.23 ± 0.39 2.09 ± 0.46 0.102*
Presence of PPA, n (%) 14 (38.9) 54 (52.4) 0.180†
VF MD, dB −3.52 (−7.50, −2.05) −5.71 (−10.97, −2.99) 0.023‡
VF PSD, dB 4.15 (2.87, 7.71) 7.26 (3.42, 10.76) 0.014‡
Presence of central scotoma, n (%) 24 (66.7) 66 (64.1) 0.841†
cRNFLSS 7.91 ± 0.81 7.52 ± 0.87 0.101*
MAC SS 8.08 ± 1.05 8.13 ± 1.01 0.828*
By univariate ROC regression analyses, inferior outer thickness of the macular parameter was affected by disc area, but this was not significant in multivariate analyses (Table 7). Among cRNFL parameters, only RNFL SS influenced the ROC in inferior quadrant thickness measurement (Table 8). 
Table 7.
 
Results of the ROC Regression Model on the Diagnostic Performance of theInferior Outer Sectoral Macular Thickness Parameter
Table 7.
 
Results of the ROC Regression Model on the Diagnostic Performance of theInferior Outer Sectoral Macular Thickness Parameter
Parameter Estimate STE 95% CI (lower limit) 95% CI (upper limit) P
Univariate
VF MD, dB −0.063 0.111 −0.291 0.127 0.285
VF PSD, dB −0.337 0.480 −1.360 0.546 0.132
VFVFI, % 0.081 0.197 −0.362 0.421 0.340
SE, D −0.050 0.070 −0.187 0.088 0.240
Age, y −0.015 0.011 −0.037 0.006 0.083
Sex 0.027 0.260 −0.482 0.536 0.459
Disc area, mm2 0.343 0.200 −0.050 0.736 0.043
VA 0.169 0.476 −0.764 1.102 0.361
MACSS 0.027 0.079 −0.129 0.182 0.369
RNFL SS 0.054 0.095 −0.131 0.240 0.283
CCT, μm 0.003 0.003 −0.004 0.009 0.206
Multivariate
α1 1.8 0.51 0.889 2.94 0.185
α2 1.86 0.218 1.54 2.35 0.031
Disc area 0.343 0.200 −0.092 0.734 0.199
Table 8.
 
Results of the ROC Regression Model on the Diagnostic Performance of the Inferior Quadrant of cRNFL Thickness Parameter
Table 8.
 
Results of the ROC Regression Model on the Diagnostic Performance of the Inferior Quadrant of cRNFL Thickness Parameter
Parameter Estimate STE 95% CI (lower limit) 95% CI (upper limit) P
Univariate
VF MD, dB −0.021 0.057 −0.134 0.091 0.356
VFPSD, dB 0.171 0.220 −0.261 0.603 0.219
VF VFI, % −0.056 0.098 −0.248 0.136 0.284
SE, D −0.136 0.113 −0.402 0.045 0.114
Age, y −0.007 0.015 −0.042 0.017 0.320
Sex 0.120 0.383 −0.596 0.984 0.377
Disc area, mm2 0.431 0.412 −0.191 1.440 0.148
VA 0.936 0.733 −0.441 2.390 0.101
MAC SS 0.030 0.125 −0.200 0.292 0.405
RNFL SS −0.096 0.085 −0.263 0.070 0.129
CCT, μm −0.002 0.003 −0.008 0.004 0.225
Multivariate
α1 1.420 0.213 1.1 1.94 0.113
α2 1.49 0.884 0.936 4.78 0.044
RNFL SS 0.343 0.200 0.003 0.634 0.043
Discussion
Conventionally, cRNFL thickness has been used as a structural assessment strategy in the diagnosis of glaucoma. However, it has been suggested that posterior pole macular thickness measurement might serve as an alternative. 14,18 24 In clinical practice, cRNFL measurement affords good glaucoma detection sensitivity in some patients while macular thickness does not. However, in other patients, macular measurements are superior to cRNFL measurements for glaucoma detection. Knowing when one form of measurement outperforms the other would aid in determining whether cRNFL or macular thickness should be given more weight when evaluating glaucoma in individual patients. In the present study, we sought to define the factors affecting the diagnostic capability of cRNFL and macular thickness measurements. To this end, we simulated clinical circumstances under which one measurement afforded good glaucoma detection capability whereas the other did not. We used a relatively large group of glaucomatous eyes to determine when one measurement was superior to the other based on various criteria. Glaucomatous eyes in which both macular and cRNFL measurements showed similar performance levels in glaucoma detection were not included. Given that criteria for glaucoma detection tend to be arbitrary, we used several parameters. 
When OCT output is used in clinical diagnosis, we usually refer to normative classification, such as abnormal, borderline, or within normal limit, and numeral thickness value. Thus, we included both normative classification and thickness value criteria in our analysis. To select appropriate cutoffs for thickness assessments, we performed ROC analysis to determine how discrimination between glaucomatous and healthy eyes could be best achieved. Because average thickness measurements may not detect localized early glaucomatous changes, we also included the most sensitive sector in our analysis of glaucoma diagnosis utility. The inferior quadrant cRNFL thickness and the macular thickness of the inferior outer sector performed best for glaucoma detection, which is consistent with previous results. 24 26  
When we compared RBG and MBG subjects using our four criteria, MBG patients showed larger disc areas. Thus, using macular rather than cRNFL thickness measurement, glaucomatous eyes with larger optic discs were likely to be better distinguished from normal eyes. In other words, glaucomatous eyes with larger optic discs may be missed by cRNFL assessment. This is explained by the fact that the scan diameter of cRNFL measurement is fixed. The RNFL is generally thicker around the disc margin because the RNFL merges into the optic disc and becomes gradually thinner as the distance from the disc margin increases. 27,28 Therefore, if a fixed 3.4-mm-diameter scan is run, cRNFL thickness tends to be greater in eyes with larger optic discs. Previous studies 29,30 found a positive correlation between optic disc size and cRNFL thickness measurements obtained by OCT. In addition, a histologic report found that larger optic discs have a greater number of nerve fibers. 31 A study using Stratus OCT (Carl Zeiss Meditec) found that glaucoma detection capability was low in patients with large discs. 32 However, macular thickness is assessed at the posterior pole, which is further from the optic disc margin than is the point at which cRNFL is measured. Thus, macular thickness measurements are less influenced by optic disc size than cRNFL thickness in terms of glaucoma detection and can afford greater diagnostic capability than cRNFL thickness, especially in glaucomatous eyes with large optic disc. 
The SS of cRNFL measurements differed between patients in the RBG and MBG. Many studies have found that SS significantly affects cRNFL measurements obtained by Stratus OCT. 33 36 Sung et al. 36 found that higher SS tended to be associated with lower probability of glaucoma. Reduced cRNFL SS might have yielded a thinner RNFL measurement than afforded by an image of higher SS, resulting in the improved diagnostic capacity of cRNFL assessment because the RNFL appeared to be thinner than it is in reality. However, macular SS did not differ between the two groups, implying that macular measurements are less affected by SS. This may be explained by the fact that cRNFL measurement is conducted after the segmentation of total retinal thickness data. Because SS is calculated using image reflectance, such reflectance may significantly influence segmentation. Within the macula, the retinal thickness measurements run from the retinal surface (the anterior boundary) to the inner/outer segment junction of the retinal pigment epithelial layer (the posterior boundary), and these measurements do not have to be segmented from assessment of total retinal thickness. Therefore, compared with cRNFL thickness assessments, macular thickness measurement may be less affected by SS. 
Age was significantly different between RBG and MBG when the comparison with the normative data set was used both globally and by sectors. Because there were no significant differences between RBG and MBG when compared by thickness value criteria, this observation might reflect a limitation of the age adjustment in the normative data set. VF MD and PSD were also significantly different in sectoral thickness assessment, which meant that RBG group had generally more advanced disease. We could not find a possible explanation regarding this observation. 
Therefore, we sought to investigate how such parameters, including VF severity, affect the diagnostic capability of each cRNFL and macular thickness measurement on glaucoma diagnosis. Because some factors are mutually correlated, we performed univariate and multivariate ROC regression analysis of each cRNFL and macular measurement to evaluate factors affecting glaucoma diagnostic capability, after adjusting for correlations among analyzed factors. No factor affected macular diagnostic capability; however, the SS affected cRNFL diagnostic capability when inferior quadrant data were used. Thus, macular measurements were relatively independent of other factors when used for glaucoma detection, but image quality affected RNFL diagnostic capability. 
By our four criteria, there were consistently fewer MBG than RBG patients (24/90; 46/143; 18/76; and 36/103), indicating that macular thickness parameters were generally of lower diagnostic capacity than were cRNFL thickness measurements. This was confirmed after comparing the AUCs of average RNFL and macular thicknesses (0.958 vs. 0.866; P < 0.001). The best performing macular thickness sector (the inferior outer) also had a significantly smaller AUC than did inferior cRNFL thickness (0.880 vs. 0.956; P < 0.001). However, macular thickness parameters were of greater use than cRNFL thickness measurement in approximately one-third of all eyes. Therefore, we recommend performing both macular and cRNFL scanning in all patients to maximize the diagnostic ability to detect glaucoma. 
Because of the large number of P values that were computed in this study, a type 1 (or false-positive) error might have occurred. Therefore, our result should be interpreted with caution in this regard. 
In summary, cRNFL thickness measurements were generally superior to macular thickness measurements in the diagnosis of glaucoma. Approximately threefold more eyes were more accurately evaluated when cRNFL measurement rather than macular measurement was used. In a small subgroup of glaucomatous eyes, macular thickness assessment was superior to cRNFL measurement; these eyes had larger optic disc areas and higher RNFL SS. Although macular measurements were generally not superior to cRNFL measurements for glaucoma detection, macular parameters may nonetheless be useful, especially in eyes of larger optic disc area. The observation that posterior pole macular thickness measurement is not affected by other factors may be of particular importance. We believe that our work may serve as a guide to which SD-OCT parameters may be optimal, in certain patients under particular conditions, in the clinical diagnosis of glaucoma. 
Footnotes
 Supported by Asan Medical Center Clinical Research Grant 2010-0723.
Footnotes
 Disclosure: J.H. Na, None; K.R. Sung, None; S. Baek, None; J.H. Sun, None; Y. Lee, None
References
Burgansky-Eliash Z Wollstein G Chu T . Optical coherence tomography machine learning classifiers for glaucoma detection: a preliminary study. Invest Ophthalmol Vis Sci. 2005;46:4147–4152. [CrossRef] [PubMed]
Huang ML Chen HY . Development and comparison of automated classifiers for glaucoma diagnosis using Stratus optical coherence tomography. Invest Ophthalmol Vis Sci. 2005;46:4121–4129. [CrossRef] [PubMed]
Kanamori A Nagai-Kusuhara A Escano MF . Comparison of confocal scanning laser ophthalmoscopy, scanning laser polarimetry and optical coherence tomography to discriminate ocular hypertension and glaucoma at an early stage. Graefes Arch Clin Exp Ophthalmol. 2006;244:58–68. [CrossRef] [PubMed]
Lalezary M Medeiros FA Weinreb RN . Baseline optical coherence tomography predicts the development of glaucomatous change in glaucoma suspects. Am J Ophthalmol. 2006;142:576–582. [CrossRef] [PubMed]
Leung CK Chan WM Yung WH . Comparison of macular and peripapillary measurements for the detection of glaucoma: an optical coherence tomography study. Ophthalmology. 2005;112:391–400. [CrossRef] [PubMed]
Manassakorn A Nouri-Mahdavi K Caprioli J . Comparison of retinal nerve fiber layer thickness and optic disk algorithms with optical coherence tomography to detect glaucoma. Am J Ophthalmol. 2006;141:105–115. [CrossRef] [PubMed]
Mayoral F Polo V Ferreras A . Diagnostic ability of Stratus optical coherence tomography (OCT) in pre-perimetric glaucoma diagnosis [in Spanish]. Arch Soc Esp Oftalmol. 2006;81:537–544. [CrossRef] [PubMed]
Medeiros FA Zangwill LM Bowd C . Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography. Am J Ophthalmol. 2005;139:44–55. [CrossRef] [PubMed]
Naithani P Sihota R Sony P . Evaluation of optical coherence tomography and heidelberg retinal tomography parameters in detecting early and moderate glaucoma. Invest Ophthalmol Vis Sci. 2007;48:3138–3145. [CrossRef] [PubMed]
Nouri-Mahdavi K Nikkhou K Hoffman DC . Detection of early glaucoma with optical coherence tomography (Stratus OCT). J Glaucoma. 2008;17:183–188. [CrossRef] [PubMed]
Parikh RS Parikh S Sekhar GC . Diagnostic capability of optical coherence tomography (Stratus OCT 3) in early glaucoma. Ophthalmology. 2007;114:2238–2243. [CrossRef] [PubMed]
Sihota R Sony P Gupta V . Comparing glaucomatous optic neuropathy in primary open angle and chronic primary angle closure glaucoma eyes by optical coherence tomography. Ophthalmic Physiol Opt. 2005;25:408–415. [CrossRef] [PubMed]
Jaffe GJ Caprioli J . Optical coherence tomography to detect and manage retinal disease and glaucoma. Am J Ophthalmol. 2004;137:156–169. [CrossRef] [PubMed]
Wollstein G Ishikawa H Wang J . Comparison of three optical coherence tomography scanning areas for detection of glaucomatous damage. Am J Ophthalmol. 2005;139:39–43. [CrossRef] [PubMed]
Alonzo TA Pepe MS . Distribution-free ROC analysis using binary regression techniques. Biostatistics. 2002;3:421–432. [CrossRef] [PubMed]
Pepe MS Longton G Janes H . Estimation and comparison of receiver operating characteristic curves. Stata J. 2009;9:1–16. [PubMed]
Janes H Longton G Pepe MS . Accommodating covariates in receiver operating characteristic analysis. Stata J. 2009;9:17–39. [PubMed]
Sung KR Kim JS Wollstein G . Imaging of the retinal nerve fibre layer with spectral domain optical coherence tomography for glaucoma diagnosis. Br J Ophthalmol. 2011;95:909–914. [CrossRef] [PubMed]
Cho JW Sung KR Lee S . Relationship between visual field sensitivity and macular ganglion cell complex thickness as measured by spectral-domain optical coherence tomography. Invest Ophthalmol Vis Sci. 2010;51:6401–6407. [CrossRef] [PubMed]
Nakatani Y Higashide T Ohkubo S . Evaluation of macular thickness and peripapillary retinal nerve fiber layer thickness for detection of early glaucoma using spectral domain optical coherence tomography. J Glaucoma. 2011;20:252–259. [CrossRef] [PubMed]
Medeiros FA Zangwill LM Alencar LM . Detection of glaucoma progression with stratus OCT retinal nerve fiber layer, optic nerve head, and macular thickness measurements. Invest Ophthalmol Vis Sci. 2009;50:5741–5748. [CrossRef] [PubMed]
Tan O Chopra V Lu AT . Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography. Ophthalmology. 2009;116:2305–2314. [CrossRef] [PubMed]
Seong M Sung KR Choi EH . Macular and peripapillary retinal nerve fiber layer measurements by spectral domain optical coherence tomography in normal-tension glaucoma. Invest Ophthalmol Vis Sci. 2010;51:1446–1452. [CrossRef] [PubMed]
Parikh RS Parikh SR Thomas R . Diagnostic capability of macular parameters of Stratus OCT 3 in detection of early glaucoma. Br J Ophthalmol. 2010;94:197–201. [CrossRef] [PubMed]
Park SB Sung KR Kang SY . Comparison of glaucoma diagnostic capabilities of Cirrus HD and Stratus optical coherence tomography. Arch Ophthlamol. 2009;127:1603–1609. [CrossRef]
Jeoung JW Park KH . Comparison of Cirrus OCT and Stratus OCT on the ability to detect localized retinal nerve fiber layer defects in preperimetric glaucoma. Invest Ophthalmol Vis Sci. 2010;51:938–945. [CrossRef] [PubMed]
Ogden TE . Nerve fiber layer of the primate retina: morphometric analysis. Invest Ophthalmol Vis Sci. 1984;25:19–29. [PubMed]
Gabriele ML Ishikawa H Wollstein G . Optical coherence tomography scan circle location and mean retinal nerve fiber layer measurement variability. Invest Ophthalmol Vis Sci. 2008;49:2315–2321. [CrossRef] [PubMed]
Bowd C Zangwill LM Blumenthal EZ . Imaging of the optic disc and retinal nerve fiber layer: the effects of age, optic disc area, refractive error, and gender. J Opt Soc Am A Opt Image Sci Vis. 2002;19:197–207. [CrossRef] [PubMed]
Savini G Zanini M Carelli V . Correlation between retinal nerve fibre layer thickness and optic nerve head size: an optical coherence tomography study. Br J Ophthalmol. 2005;89:489–492. [CrossRef] [PubMed]
Jonas JB Schmidt AM Muller-Bergh JA . Human optic nerve fiber count and optic disc size. Invest Ophthalmol Vis Sci. 1992;33:2012–2018. [PubMed]
Medeiros FA Zangwill LM Bowd C . Influence of disease severity and optic disc size on the diagnostic performance of imaging instruments in glaucoma. Invest Ophthalmol Vis Sci. 2006;47:1008–1015. [CrossRef] [PubMed]
Cheung CY Leung CK Lin D . Relationship between retinal nerve fiber layer measurement and signal strength in optical coherence tomography. Ophthalmology. 2008;115:1347–1351. [CrossRef] [PubMed]
Wu Z Huang J Dustin L . Signal strength is an important determinant of accuracy of nerve fiber layer thickness measurement by optical coherence tomography. J Glaucoma. 2009;18:213–216. [CrossRef] [PubMed]
Stein DM Wollstein G Ishikawa H . Effect of corneal drying on optical coherence tomography. Ophthalmology. 2006;113:985–991. [CrossRef] [PubMed]
Sung KR Wollstein G Schuman JS . Scan quality effect on glaucoma discrimination by glaucoma imaging devices. Br J Ophthalmol. 2009;93:1580–1584. [CrossRef] [PubMed]
Table 1.
 
Baseline Characteristics of Glaucomatous and Healthy Eyes
Table 1.
 
Baseline Characteristics of Glaucomatous and Healthy Eyes
Glaucomatous Healthy P
Eyes, n 424 297
Age, y 57.9 ± 12.1 50.7 ± 11.1 <0.001*
Male/female, n 233/191 137/160 0.020†
Spherical equivalent, D −0.72 ± 1.94 −0.30 ± 1.33 0.001*
Intraocular pressure, mm Hg 14.9 ± 3.7 14.97 ± 3.1 0.772*
Central corneal thickness, μm 536.1 ± 34.5 543.4 ± 31.7 0.020*
Axial length, mm 23.8 ± 0.9 23.9 ± 0.8 0.462*
VFMD, dB −5.02 (−10.01, −2.49) −0.12 (−0.88, −0.83) <0.001‡
VP PSD, dB 6.22 (3.17, 10.55) 1.50 (1.34, 1.65) <0.001‡
Table 2.
 
AUCs of cRNFL and Macular Thickness Parameters
Table 2.
 
AUCs of cRNFL and Macular Thickness Parameters
Parameter AUC (95% CI) Cutoff Value Sensitivity Specificity
cRNFL Parameters
Average, μm 0.958 (0.942–0.970) 87 87.10 94.45
Sector, μm
Quadrant
    Temporal 0.823 (0.793–0.851) 63 73.56 76.52
    Superior 0.914 (0.894–0.933) 108 80.60 90.47
    Nasal 0.765 (0.732–0.788) 66 78.31 59.36
    Inferior 0.956 (0.939–0.969) 110 86.59 94.43
12-Clock hours
    9 0.695 (0.661–0.729) 50 54.15 77.57
    10 0.810 (0.781–0.840) 71 62.59 86.83
    11 0.880 (0.884–0.902) 115 76.03 87.53
    12 0.831 (0.802–0.856) 100 66.80 88.48
    1 0.839 (0.811–0.864) 96 73.17 83.21
    2 0.779 (0.751–0.807) 74 74.97 66.90
    3 0.608 (0.572–0.641) 54 49.71 66.83
    4 0.708 (0.674–0.738) 61 67.32 64.76
    5 0.872 (0.847–0.894) 85 72.33 88.54
    6 0.918 (0.896–0.936) 107 78.47 94.76
    7 0.923 (0.903–0.941) 120 81.50 92.75
    8 0.797 (0.769–0.830) 58 62.59 86.47
Macular Parameters
Cube average, μm 0.866 (0.840–0.891) 269 72.01 89.00
Cube volume, mm3 0.869 (0.843–0.893) 9.7 72.30 88.67
Sector, μm
    Temporal outer 0.826 (0.797–0.852) 254 71.17 82.26
    Superior outer 0.816 (0.786–0.842) 275 75.75 76.38
    Nasal outer 0.806 (0.776–0.833) 287 65.92 86.54
    Inferior outer 0.880 (0.854–0.902) 252 71.61 94.42
    Temporal inner 0.767 (0.738–0.797) 297 61.51 86.52
    Superior inner 0.716 (0.683–0.749) 309 52.19 87.20
    Nasal inner 0.667 (0.633–0.701) 307 41.53 89.81
    Inferior inner 0.773 (0.741–0.802) 305 65.49 83.27
    Fovea (central) 0.509 (0.473–0.545) 239 36.24 70.80
Table 3.
 
Comparison of aMBG and aRBG Patients (Grouped in Terms of the Cutoff Values of Average Thickness)
Table 3.
 
Comparison of aMBG and aRBG Patients (Grouped in Terms of the Cutoff Values of Average Thickness)
aMBG aRBG P
Eyes, n 24 90
Age, y 64 (54, 71) 57 (49, 65) 0.019*
Male/female, n 10/14 50/40 0.256†
Eyes, OD/OS 10/14 49/41 0.358†
SE, D 0.31 ± 1.51 −0.87 ± 1.83 0.002‡
IOP, mmn Hg 13.58 ± 2.52 14.56 ± 3.99 0.260‡
CCT, μm 530 (523, 557) 530 (511, 554) 0.563*
Axial length, mm 23.75 ± 0.37 23.57 ± 0.95 0.755‡
Disc area, mm2 2.31 ± 0.44 2.04 ± 0.48 0.016‡
Presence of PPA, n (%) 11 (45.8) 35 (38.9) 0.349†
VFMD, dB −3.31 (−8.01, −1.34) −3.23 (−6.46, −1.89) 0.792*
VFPSD, dB 4.11 (2.68, 7.03) 4.83 (2.92, 7.53) 0.284*
Presence of central scotoma, n (%) 11 (45.8) 49 (54.4) 0.496†
cRNFL SS 8.06 ± 0.78 7.72 ± 0.80 0.043‡
MAC SS 8.29 ± 1.04 8.02 ± 0.98 0.262‡
Table 4.
 
Comparison between abMBG and abMBG Patients (Grouped in Terms of the Cutoff Value of the Sector with the Best AUC)
Table 4.
 
Comparison between abMBG and abMBG Patients (Grouped in Terms of the Cutoff Value of the Sector with the Best AUC)
abMEG abRBG P
Eyes,n 18 76
Age, y 62.5 (51.0, 71.3) 59.0 (51.0, 65.0) 0.256*
Male/female, n 7/11 44/32 0.190†
Eyes, OD/OS 8/10 43/33 0.433†
SE, D 0.13 ± 2.00 −0.69 ± 1.78 0.133‡
IOP, mm Hg 14.5 (13.0, 17.25) 14(12.0, 16.0) 0.293*
CCT, μm 533 (521, 602) 543 (511, 560) 0.659*
Axial length, mm 23.67 ± 0.01 23.83 ± 0.67 0.200‡
Disc area, mm2 2.31 ± 0.35 2.05 ± 0.48 0.017‡
Presence of PPA, n (%) 7 (38.9) 25 (32.9) 0.783
VF MD, dB −2.26 (−5.04, −1.08) −3.68 (−7.73, −1.91) 0.047*
VF PSD, dB 2.95 (2.41, 5.41) 5.27 (3.11, 7.80) 0.011*
Presence of central scotoma, n (%) 10 (55.6) 25 (32.9) 0.104†
cRNFL SS 7.94 ± 0.74 7.68 ± 0.82 0.199‡
MAC SS 8.28 ± 0.96 8.00 ± 0.88 0.272‡
Table 5.
 
Comparison between nMBG and nRBG Patients (Grouped by Normative Average Thickness Data)
Table 5.
 
Comparison between nMBG and nRBG Patients (Grouped by Normative Average Thickness Data)
nMBG nRBG P
Eyes, n 46 143
Age, y 60.54 ± 13.15 56.07 ± 11.45 0.028*
Male/female, n 19/27 93/50 0.006†
Eyes, OD/OS 22/24 73/70 0.737†
SE, D −0.55 ± 2.15 −0.97 ± 1.93 0.210*
IOP, mm Hg 14.54 ± 2.63 14.73 ± 3.56 0.697*
CCT, μm 548.55 ± 35.45 531.60 ± 36.30 0.103*
Axial length, mm 24.30 ± 0.81 23.72 ± 1.05 0.216*
Disc area, mm2 2.20 ± 0.41 1.98 ± 0.44 0.003*
Presence of PPA, n (%) 24 (52.2) 70 (49.0) 0.737†
VF MD, dB −5.46 (−8.29, −2.72) −4.83 (−10.50, −2.50) 0.747†
VF PSD, dB 6.01 (3.26, 8.85) 7.49 (3.34, 10.84) 0.225†
Presence of central scotoma, n (%) 37 (80.4) 92 (64.3) 0.045†
cRNFL SS 7.97 ± 0.89 7.85 ± 0.86 0.466*
MAC SS 8.07 ± 0.95 8.09 ± 1.03 0.881*
Table 6.
 
Comparison of nbMBG and nbRBG Patients (Grouped According to Normative Data from the Sector with the Best AUC)
Table 6.
 
Comparison of nbMBG and nbRBG Patients (Grouped According to Normative Data from the Sector with the Best AUC)
nbMEG nbRBG P
Eyes,n 36 103
Age, y 63.56 ± 12.55 55.92 ± 12.07 0.002*
Male/female, n 22/14 69/34 0.546†
Eyes, OD/OS 15/21 53/50 0.339†
SE, D −0.26 ± 1.97 −0.83 ± 1.85 0.138*
IOP, mmHg 14.53 ± 3.64 14.51 ± 3.31 0.984*
CCT, μm 548.26 ± 31.80 532.52 ± 31.67 0.147*
Axial length, mm 23.33 ± 0.83 23.76 ± 0.98 0.256*
Disc area, mm2 2.23 ± 0.39 2.09 ± 0.46 0.102*
Presence of PPA, n (%) 14 (38.9) 54 (52.4) 0.180†
VF MD, dB −3.52 (−7.50, −2.05) −5.71 (−10.97, −2.99) 0.023‡
VF PSD, dB 4.15 (2.87, 7.71) 7.26 (3.42, 10.76) 0.014‡
Presence of central scotoma, n (%) 24 (66.7) 66 (64.1) 0.841†
cRNFLSS 7.91 ± 0.81 7.52 ± 0.87 0.101*
MAC SS 8.08 ± 1.05 8.13 ± 1.01 0.828*
Table 7.
 
Results of the ROC Regression Model on the Diagnostic Performance of theInferior Outer Sectoral Macular Thickness Parameter
Table 7.
 
Results of the ROC Regression Model on the Diagnostic Performance of theInferior Outer Sectoral Macular Thickness Parameter
Parameter Estimate STE 95% CI (lower limit) 95% CI (upper limit) P
Univariate
VF MD, dB −0.063 0.111 −0.291 0.127 0.285
VF PSD, dB −0.337 0.480 −1.360 0.546 0.132
VFVFI, % 0.081 0.197 −0.362 0.421 0.340
SE, D −0.050 0.070 −0.187 0.088 0.240
Age, y −0.015 0.011 −0.037 0.006 0.083
Sex 0.027 0.260 −0.482 0.536 0.459
Disc area, mm2 0.343 0.200 −0.050 0.736 0.043
VA 0.169 0.476 −0.764 1.102 0.361
MACSS 0.027 0.079 −0.129 0.182 0.369
RNFL SS 0.054 0.095 −0.131 0.240 0.283
CCT, μm 0.003 0.003 −0.004 0.009 0.206
Multivariate
α1 1.8 0.51 0.889 2.94 0.185
α2 1.86 0.218 1.54 2.35 0.031
Disc area 0.343 0.200 −0.092 0.734 0.199
Table 8.
 
Results of the ROC Regression Model on the Diagnostic Performance of the Inferior Quadrant of cRNFL Thickness Parameter
Table 8.
 
Results of the ROC Regression Model on the Diagnostic Performance of the Inferior Quadrant of cRNFL Thickness Parameter
Parameter Estimate STE 95% CI (lower limit) 95% CI (upper limit) P
Univariate
VF MD, dB −0.021 0.057 −0.134 0.091 0.356
VFPSD, dB 0.171 0.220 −0.261 0.603 0.219
VF VFI, % −0.056 0.098 −0.248 0.136 0.284
SE, D −0.136 0.113 −0.402 0.045 0.114
Age, y −0.007 0.015 −0.042 0.017 0.320
Sex 0.120 0.383 −0.596 0.984 0.377
Disc area, mm2 0.431 0.412 −0.191 1.440 0.148
VA 0.936 0.733 −0.441 2.390 0.101
MAC SS 0.030 0.125 −0.200 0.292 0.405
RNFL SS −0.096 0.085 −0.263 0.070 0.129
CCT, μm −0.002 0.003 −0.008 0.004 0.225
Multivariate
α1 1.420 0.213 1.1 1.94 0.113
α2 1.49 0.884 0.936 4.78 0.044
RNFL SS 0.343 0.200 0.003 0.634 0.043
Copyright © Association for Research in Vision and Ophthalmology
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×