Structure-Function Relationships With Spectral-Domain Optical Coherence Tomography Retinal Nerve Fiber Layer and Optic Nerve Head Measurements

Abstract

Purpose.: To evaluate the regional structure-function relationship between visual field sensitivity and retinal nerve fiber layer (RNFL) thickness and optic nerve head (ONH) measurements using spectral-domain optical coherence tomography (SD-OCT).

Methods.: Prospective cross-sectional study conducted on patients with glaucoma, suspected glaucoma, and healthy subjects. Eyes were tested on Cirrus OCT and standard achromatic perimetry. RNFL thickness of 12 peripapillary 30° sectors, neuroretinal rim thickness extracted from 36 neuroretinal rim scans, and Bruch membrane opening minimum rim width (BMO-MRW)—a recently defined parameter—extracted from 36 neuroretinal rim scans were obtained. Correlations between peripapillary RNFL thickness, neuroretinal rim thickness, all six sectors of BMO-MRW, and visual field sensitivity in the six corresponding areas were evaluated using logarithmic regression analysis. Receiver operating curve areas were calculated for each RNFL, ONH, and macular ganglion cell analysis parameter.

Results.: We included 142 eyes of 142 subjects. The correlations (r ²) between RNFL thickness, Cirrus-based neuroretinal rim thickness, BMO-MRW and visual field sensitivity ranged from 0.07 to 0.60, 0.15 to 0.49, and 0.24 to 0.66, respectively. The structure-function correlations were stronger with BMO-MRW than with Cirrus-based neuroretinal rim thickness. The largest areas under the receiver operating curve were seen for rim area (0.926 [95% confidence interval 0.875, 0.977]; P < 0.001) in eyes with glaucoma and for average RNFL (0.863 [0.769, 0.957]; P < 0.01) in eyes with suspected glaucoma.

Conclusions.: The structure-function relationship was significantly stronger with BMO-MRW than other ONH SD-OCT parameters. The best diagnostic capabilities were seen with rim area and average RNFL.

Introduction

The basic pathological change in glaucoma is the loss of retinal ganglion cells and their axons, resulting in local and/or diffuse thinning of the retinal nerve fiber layer (RNFL) and of the neuroretinal rim, eventually leading to loss of visual function. ¹ Glaucoma diagnosis and follow-up of progression is often based on a combination of structural and functional assessments. Evaluation of structural loss has long been limited to a clinical qualitative examination of the optic nerve head (ONH) and RNFL. ^2,3 Imaging technologies introduced in the last decade now provide objective and quantitative measurements of the RNFL. ^4–7 Several studies have reported a significant relationship between structural damage, observed with different imaging methods, and functional damage, determined by standard achromatic perimetry. ^8–21  

Optical coherence tomography (OCT) is a method that directly measures the RNFL and neuroretinal rim thickness. Previous studies have evaluated and compared the regional relationships between visual field sensitivity and RNFL thickness measured with OCT and with other imaging modalities such as scanning laser polarimetry or confocal scanning laser ophthalmoscopy. ^8–21 Since algorithms allowing analysis of the neuroretinal rim thickness from SD-OCT ONH scans were recently developed and added to the devices' software, no studies have evaluated the regional relationships between SD-OCT–measured neuroretinal rim thickness and visual field sensitivity. Previous studies have found comparable or slightly lower capabilities of SD-OCT neuroretinal rim parameters provided by the currently available machines compared with RNFL parameters for glaucoma diagnosis. ^22–25 A new anatomical parameter describing the neuroretinal rim was recently proposed by Reis et al. ²⁶ and Chauhan et al. ²⁷ and consists of the minimum distance between the Bruch membrane opening and the internal limiting membrane (Bruch membrane opening minimal rim width: BMO-MRW). Compared with currently available SD-OCT neuroretinal thickness measurement methods, BMO-MRW has the advantage of taking into account the variable orientation of rim tissue in the ONH. 

The purpose of the current study was to evaluate and compare the regional and global structure-function relationship between visual field sensitivity and RNFL thickness and ONH measurements using SD-OCT, including the BMO-MRW parameter. We also evaluated and compared the diagnostic capabilities of the SD-OCT RNFL, ONH, and macular ganglion cell analysis (GCA) measurements for distinguishing glaucomatous or suspected glaucomatous eyes from healthy eyes. 

Methods

Patients

We conducted a prospective investigation in a French university–affiliated hospital. The study followed the tenets of the declaration of Helsinki and has been prospectively approved by the Ethics Committee of the French Society of Ophthalmology (IRB 00008855 Société Française d'Ophtalmologie IRB#1). All subjects provided both verbal and written informed consent. 

All study participants underwent a full ophthalmic examination, including objective and subjective refraction, slit-lamp biomicroscopy, intraocular pressure measurement with Goldmann tonometry, gonioscopy, dilated fundus examination by indirect ophthalmoscopy, central corneal thickness measurement, and A-scan ultrasound biometry. Refraction was measured using an autorefractometer (AR-360; NIDEK Co., Ltd., Gamagori, Japan); gonioscopy using a three-mirror lens (Goldmann; Haag-Streit, Koeniz, Switzerland); and pachymetry and A-scan ultrasound biometry using an ophthalmic ultrasound system (OcuScanRxP; Alcon, Inc., Fort Worth, TX, USA). 

Inclusion criteria were age 18 years or older, best corrected visual acuity better than or equal to 20/40, spherical refraction between −6.00 and +3.00 D, open angles on gonioscopy, no retinal disease or nonglaucomatous neuropathy, and no intraocular surgery except for uncomplicated cataract surgery. One eye was selected randomly from each of 142 subjects: 55 with glaucoma, 47 with suspected glaucoma, and 40 healthy subjects. Glaucomatous eyes were defined as those with consecutive and reliable abnormal standard achromatic perimetry with abnormal Glaucoma Hemifield Test and pattern standard deviation outside 95% of normal limits, and optic nerve damage (asymmetric cup/disc ratio > 0.2, rim thinning, notching, excavation, or retinal nerve fiber layer defect). Suspected glaucomatous eyes were defined as those with optic nerve damage (asymmetric cup/disc ratio > 0.2, rim thinning, notching, excavation, or retinal nerve fiber layer defect) without repeatable abnormal standard achromatic perimetry results. Healthy eyes had intraocular pressure less than 22 mm Hg as shown by Goldman applanation tonometry, normal-appearing optic discs, and no repeatable abnormal standard achromatic perimetry results. 

Measurements

White-on-white standard achromatic perimetry was performed using a field analyzer (Humphrey Visual Field Analyzer; Zeiss-Humphrey Systems, Dublin, CA, USA) using the C-24-2 SITA–standard strategy. A reliable visual field test was defined as a less than 25% rate of fixation losses and fewer than 20% false-positive and false-negative results. The two points adjacent to the blind spot were excluded from the analysis. The 52 remaining points were grouped into six sectors, based on the topographic relationships between visual field locations and corresponding regions of the optic disc, as previously described by Garway-Heath et al. ^28,29 (Fig. 1). To calculate the mean total deviation of the six sectors, the decibel levels in each location of the total deviation field were converted to a linear scale (e.g., 0 dB converted to 1.0 and 30 dB to 0.001) before averaging the data within each sector. ³⁰ The averaged data were then converted back to decibel units. 

Figure 1

 

Division of the visual field and optic nerve head. Peripapillary retinal nerve fiber layer thickness sectors and corresponding visual field areas as established by Garway-Heath et al. ^28,29

A commercially available spectral-domain OCT (Cirrus OCT, software version 6.0; Carl Zeiss Meditec, Inc., Dublin, CA, USA) was used in the study. The ONH and RNFL were imaged using the 200 × 200 protocol optic disc cube (200 horizontal scan lines, each consisting of 200 A-scans). Only well-focused, well-centered images, without eye movement and with a signal strength of 7/10 or more were used. The RNFL parameters of the printout retained for the analysis were inferior, superior, nasal, temporal, average thickness, inter-eye symmetry, and retinal nerve fiber layer thickness for each of the twelve 30° sectors. These 30° sectors are centered on the acquired image frame axes (first sector −15° to +15°, etc.). The ONH parameters of the printout retained for the analysis were rim area, disc area, average cup/disc ratio, vertical cup/disc ratio, and cup volume. 

We collected the 256 retinal nerve fiber layer thicknesses of the TSNIT curve corresponding to the thicknesses extracted from the 256 A-scan along the path of the 3.46-mm calculation circle located around the optic disc by the Cirrus internal specific software. ³¹ The Cirrus OCT software has the facility to scroll a cursor along the TSNIT curve, making the 256 values appear successively. These 256 values were grouped into six sectors, based on the relation between visual field and regions of the optic disc as described above: inferonasal (231–270°); inferotemporal (271–310°); temporal (311–40°); superotemporal (41–80°); superonasal (81–120°); and nasal (121–230°). 

We collected 36 neuroretinal rim (NRR) thickness measurements of the neuroretinal rim thickness curve. The Cirrus OCT software has the facility to scroll a cursor along the curve, making the 360 values appear successively. Thirty-six values were collected, at the 2, 12, 22°, and so on, meridians, to avoid the boundaries of the above mentioned sectors (231–270°, 271–310°, etc.). These 36 values were then used to calculate the mean neuroretinal thickness into six sectors as described above. For example, the neuroretinal rim thickness in the superonasal sector (81–120°) is the mean of the neuroretinal thickness of the 82, 92, 102, and 112° meridians. 

We calculated 72 BMO-MRW from 36 cross-sectional ONH images (Fig. 2). Thirty-six cross-sectional images of the ONH (B-scans centered on the optic nerve head center) were obtained at 0–180°, 5–185°, 10–190°, and so on meridians. The image-processing software ImageJ (http://imagej.nih.gov/ij/; provided in the public domain by the National Institutes of Health, Bethesda, MD, USA) was used to analyze the images, which were viewed on a 21-inch monitor (1280 × 1024 × 24 bits). Fourfold magnifications and rectangular selections were used to obtain 72 clear cross-sectional images of the neuroretinal rim at the 0°, 5°, 10°, and so on, meridians. The minimum distance between the Bruch membrane opening and the internal limiting membrane was measured from each of the 72 neuroretinal rim cross sections. The end of the Bruch membrane is automatically delineated by the Cirrus software and appeared as small black circle. The internal limiting membrane is automatically delineated by the Cirrus software and appeared as a red line. The first step was to perform a manual delineation of external black circle contours. The second step in the procedure was to locate the centroid of the defined surface. This was performed automatically by the software. The third step was to draw the line segment joining the centroid of the black circle and the internal limiting membrane, perpendicular to the internal limiting membrane, and thus being the minimum distance between the black dot and the internal limiting membrane. The last step was to measure the line segment, corresponding to the BMO-MRW. These 72 values were then used to calculate the mean BMO-MRW into six sectors as described above. For example, the neuroretinal rim thickness in the superonasal sector (81–120°) is the mean of the neuroretinal thickness of the 85, 90, 95, 100, 105, 110, 115, and 120° meridians. 

Figure 2

 

Example of cross-sectional optic nerve head image in the inferonasal sector. Bruch membrane, Bruch membrane opening, internal limiting membrane, and internal limiting membrane landmark are automatically defined by the Cirrus internal-specific software. Note the difference between the BMO-MRW distance and the neuroretinal rim thickness provided by the Cirrus software (distance between black and red dots). ILM, internal limiting membrane.

The GCA was imaged using the 512 × 128 macular cube protocol. Only well-focused, well-centered images, with no eye movement and with a signal strength of 7/10 or greater were used. The parameters of the printout retained for the analysis were average thickness, minimal thickness and thickness for each of the six 60° sectors. 

Data Analysis

Independent sample t-tests, ANOVA, and Mann-Whitney tests were used to compare means and percentages, depending of the normality of data distribution. Logarithmic (y = a*ln(x)+b) regression analyses were performed to evaluate the relationship between visual field sensitivity and retinal nerve fiber layer or neuroretinal rim thickness in the six matching areas. Visual field sensitivity was the dependent variable. The statistical significance of r was tested using a t-test. A two-tailed paired t-test was used to compare the strength of the association across structural parameters. Three pairs of correlation coefficients were used for each sector (in example, for one given structural sector and the corresponding visual field area, comparison of the RNFL–visual field (VF) correlation coefficient to the NRR-VF correlation coefficient, comparison of the RNFL-VF correlation coefficient to the BMO-MRW-VF correlation coefficient, and comparison of the NRR–VF correlation coefficient to the BMO-MRW-VF correlation coefficient. Correlation coefficients were compared using the Fisher transform. The area under the receiver operator characteristic curve (AUROC) was used to assess the capability of each OCT parameter to differentiate between healthy and suspected glaucomatous eyes, and between healthy and glaucomatous eyes. An AUROC of 0.5 represents discrimination no better than chance; an area of 1 represents perfect discrimination. Differences between AUROCs among parameters were determined using the nonparametric method described by De Long et al. ³² Statistical significance was set at P ≤ 0.05. P values were statistically significant if lower than the mean statistical significance 0.05 divided by the number of comparisons performed. Statistical software (SPSS, version 17.0; SPSS, Inc., Chicago, IL, USA) was used for the analyses. 

Results

Patient Characteristics

A total of 142 eyes of 142 subjects were included (40 healthy eyes, 55 eyes with glaucoma, and 47 eyes with suspected glaucoma). Their demographic, biometric and visual field data are shown in Table 1. 

Table 1

 

Subjects Characteristics

	Glaucoma, n = 55	Suspected Glaucoma, n = 47	Healthy, n = 40	P, ANOVA
Age, y, mean ± SD	64.2 ± 15.9	61.9 ± 14.2	56.8 ± 17.8	0.04
Sex, male/female	26/29	20/27	19/21	0.22
Spherical equivalent, diopters, mean ± SD	−1.31 ± 2.2	−0.99 ± 3.2	−1.14 ± 2.8	0.90
Axial length, mm, mean ± SD	23.2 ± 1.1	23.4 ± 1.2	23.4 ± 1.0	0.68
CCT, μm, mean ± SD	530 ± 44	532 ± 36	551 ± 51	0.06
Standard automated perimetry, dB
Mean deviation, mean ± SD	−8.28 ± 8.64	−1.56 ± 2.22	−1.60 ± 3.02	<0.01
Pattern standard deviation, mean ± SD	7.56 ± 4.54	2.78 ± 2.03	2.12 ± 1.07	<0.01

 Demographic, biometric, and visual field data in eyes with glaucoma, suspected glaucoma, and healthy eyes. CCT, central corneal thickness. Pairwise comparisons: age (glaucoma versus suspected glaucoma, P = 0.45; glaucoma versus healthy, P = 0.03; suspected glaucoma versus healthy, P = 0.12); CCT (glaucoma versus suspected glaucoma, P = 0.80; glaucoma versus healthy, P = 0.03; suspected glaucoma versus healthy, P = 0.04); mean deviation (glaucoma versus suspected glaucoma, P < 0.001; glaucoma versus healthy, P < 0.001; suspected glaucoma versus healthy, P = 0.92); and pattern standard deviation (glaucoma versus suspected glaucoma, P < 0.001; glaucoma versus healthy, P < 0.001; suspected glaucoma versus healthy, P = 0.07).

Structure-Function Relationships

Logarithmic regression parameters are presented in Table 2. Analyses showed a significant relationship between sectorial thickness and visual field sensitivity for most areas studied. The correlations (r ²) between RNFL thickness and visual field sensitivity ranged from 0.07 (nasal RNFL and corresponding visual field area) to 0.60 (inferotemporal RNFL and corresponding visual field area); between Cirrus-based neuroretinal rim thickness and visual field sensitivity ranged from 0.15 (nasal thickness and corresponding visual field area) to 0.49 (inferotemporal thickness and corresponding visual field area); and between BMO-MRW and visual field sensitivity ranged from 0.24 (nasal thickness and corresponding visual field area) to 0.66 (inferotemporal thickness and corresponding visual field area). Logarithmic associations were strongest between the inferotemporal sector measurements and corresponding visual field areas, for the RNFL, neuroretinal rim, and BMO-MRW measurements (Fig. 3). In pairwise comparisons, logarithmic structure-function associations were stronger with the BMO-MRW measurements than with the neuroretinal rim measurements provided by the Cirrus software, for all 6 sectors (P < 0.01). In pairwise comparisons, logarithmic structure-function associations were significantly stronger with the BMO-MRW measurements than with the RNFL measurements for the temporal, superonasal, nasal, and inferotemporal sectors (P < 0.01), and nonsignificantly different for the superotemporal and inferonasal sectors (P = 0.12 and P = 0.08, respectively). 

Figure 3

 

Structure-function relationships. Scatter plots showing the association between visual field sensitivity (dB) and Cirrus optical coherence tomography retinal nerve fiber layer thickness in the inferotemporal sector (top left, μm), neuroretinal rim thickness in the inferotemporal sector (top right, μm), and Bruch membrane opening minimum rim width in the inferotemporal sector (bottom, μm), with logarithmic fit.

Table 2

 

Structure-Function Relationships

Peripapillary Sector	Logarithmic Regression
	r 2	P	y = a*ln(x) + b
RNFL
Temporal	0.093	0.003	y = 9.9ln(x) − 19.3
Superotemporal	0.495	<0.001	y = 15.8ln(x) − 24.6
Superonasal	0.274	<0.001	y = 11.2ln(x) − 4.6
Nasal	0.065	0.005	y = 9.51ln(x) − 25.8
Inferonasal	0.263	<0.001	y = 15.4ln(x) − 67.1
Inferotemporal	0.598	<0.001	y = 17.2ln(x) − 77.5
NRR
Temporal	0.401	<0.001	y = 8.70ln(x) − 17.3
Superotemporal	0.457	<0.001	y = 8.58ln(x) − 33.7
Superonasal	0.328	<0.001	y = 9.57ln(x) − 25.6
Nasal	0.154	0.05	y = 7.67ln(x) − 3.44
Inferonasal	0.259	<0.001	y = 9.77ln(x) − 26.4
Inferotemporal	0.494	<0.001	y = 8.91ln(x) − 41.1
BMO-MRW
Temporal	0.457	<0.001	y = 8.67ln(x) − 8.50
Superotemporal	0.604	<0.001	y = 8.88ln(x) − 19.7
Superonasal	0.523	<0.001	y = 8.75ln(x) − 21.8
Nasal	0.241	<0.001	y = 6.41ln(x) − 5.44
Inferonasal	0.460	<0.001	y = 8.85ln(x) − 23.6
Inferotemporal	0.658	<0.001	y = 10.1ln(x) − 42.3

 Logarithmic regression between Cirrus optical coherence tomography–measured retinal nerve fiber layer thickness, neuroretinal rim thickness, and Bruch membrane opening minimum rim width in the six peripapillary sectors and visual field sensitivity (dB) in the six corresponding areas.

From the scatter plots showing the association between visual field sensitivity and anatomical parameters (Fig. 3), it is interesting to note that in advanced glaucoma (low visual field sensitivity and reduced RNFL or neuroretinal rim thickness: left parts of the scatterplots), the relationship between neuroretinal rim thickness (measured either by the Cirrus software or the BMO-MRW) and retinal sensitivity seems to be much stronger than the relationship between RNFL thickness and visual field sensitivity is. By contrast, earlier in the disease (MD > −15 dB: right part of the scatterplots), RNFL thickness seems to correlate with function better than the neuroretinal rim thickness does (measured either by the Cirrus software or the BMO-MRW). 

Linear and logarithmic regression analyses were performed to evaluate the relationship between RNFL and BMO-MRW in the six matching areas. The results are displayed in Table 3. In four of the six sectors (temporal, nasal, superonasal, and inferonasal), the correlations are rather weak. In these four sectors, the strengths of the linear and logarithmic regressions are not significantly different. In two of the six sectors (superotemporal and inferotemporal), the correlations are stronger. In these two sectors, logarithmic associations were significantly stronger than linear associations. 

Table 3

 

Relationships Between RNFL and NRR Thicknesses

Peripapillary Sector	Linear Regression (Dependent Variable: BMO-MRW)			Logarithmic Regression (Dependent Variable: BMO-MRW)
	r 2	P	y = a*x + b	r 2	P	y = a*ln(x) + b
Temporal	0.279	0.001	y = 2.9x − 3.7	0.273	0.001	y = 185ln(x) − 582
Superotemporal	0.486	<0.001	y = 2.2x + 1.75	0.560	<0.001	y = 170ln(x) − 555
Superonasal	0.216	0.003	y = 2.1x + 56.5	0.137	0.2	y = 80.5ln(x) − 109
Nasal	0.143	0.6	y = 5.2x − 51.7	0.139	0.7	y = 331ln(x) − 1090
Inferonasal	0.349	<0.001	y = 4.3x − 63.7	0.335	<0.001	y = 354ln(x) − 1261
Inferotemporal	0.497	<0.001	y = 3.1x − 54.1	0.588	<0.001	y = 296ln(x) − 1094

Diagnostic Performance

Table 4 shows the capability of Cirrus OCT parameters to discriminate glaucomatous or suspected glaucomatous eyes from healthy eyes, with sensitivities at 80% and 95% specificity. For RNFL-derived parameters, the largest AUROCs were seen for average RNFL in eyes with glaucoma and suspected glaucoma (0.922 [95% confidence interval 0.881, 0.963] and 0.863 [0.769, 0.957], respectively). For GCA-derived parameters, the largest AUROCs were seen for the GCA inferotemporal sector in eyes with glaucoma and suspected glaucoma (0.901 [0.836, 0.965] and 0.769 [0.657, 0.880], respectively). For ONH-derived parameters, the largest AUROCs were seen for rim area in eyes with glaucoma and suspected glaucoma (0.926 [0.875, 0.977] and 0.853 [0.759, 0.947], respectively). For NRR-derived parameters, the largest AUROCs were seen for the inferotemporal sector in eyes with glaucoma and suspected glaucoma (0.910 [0.855, 0.965] and 0.842 [0.742, 0.942], respectively). For BMO-MRW–derived parameters, the largest AUROCs were seen for inferotemporal sector rim area in eyes with glaucoma and suspected glaucoma (0.917 [0.856, 0.978] and 0.851 [0.759, 0.943], respectively). 

Table 4

 

Diagnostic Capability of Cirrus OCT Parameters

Structure	Parameter	Glaucoma vs. Healthy	Suspected vs. Healthy
RNFL	Average	0.922 (0.881, 0.963)	0.863 (0.769, 0.957)
		91%; 45%	76%; 66%
	Temporal	0.792 (0.743, 0.841)	0.732 (0.612, 0.851)
		78%; 24%	52%; 44%
	Superior	0.909 (0.854, 0.963)	0.785 (0.671, 0.899)
		70%; 32%	48%; 42%
	Nasal	0.712 (0.614, 0.810)	0.649 (0.509, 0.788)
		76%; 36%	46%; 40%
	Inferior	0.910 (0.855, 0.965)	0.850 (0.754, 0.946)
		88%; 40%	60%; 68%
GCA	Average	0.884 (0.809, 0.958)	0.716 (0.596, 0.835)
		85%; 34%	64%; 4%
	Minimum	0.857 (0.775, 0.939)	0.706 (0.588, 0.824)
		78%; 28%	66%; 12%
	Best of the six sectors (GCA inferotemporal)	0.901 (0.836, 0.965)	0.769 (0.657, 0.880)
		84%; 30%	68%; 4%
ONH	Rim area	0.926 (0.875, 0.977)	0.853 (0.759, 0.947)
		88%; 62%	71%; 42%
	Disc area	0.551 (0.433, 0.669)	0.572 (0.435, 0.709)
		32%; 8%	36%; 4%
	Average c/d ratio	0.855 (0.780, 0.929)	0.738 (0.638, 0.838)
		84%; 60%	66%; 30%
	Vertical c/d ratio	0.876 (0.811, 0.941)	0.761 (0.643, 0.878)
		76%; 54%	68%; 32%
	Cup volume	0.793 (0.700, 0.885)	0.689 (0.565, 0.812)
		74%; 46%	54%; 18%
NRR	Average	0.878 (0.805, 0.950)	0.761 (0.663, 0.859)
		75%; 6%	41%; 6%
	Best of the 6 sectors (inferotemporal)	0.910 (0.855, 0.965)	0.842 (0.742, 0.942)
		81%; 5%	46%; 4%
BMO-MRW	Average	0.906 (0.839, 0.972)	0.816 (0.690, 0.941)
		80%; 54%	62%; 16%
	Best of the 6 sectors (inferotemporal)	0.917 (0.856, 0.978)	0.851 (0.759, 0.943)
		80%; 60%	68%; 18%

 Areas under the receiver operator characteristic curve of the Cirrus OCT parameters for discriminating glaucomatous or suspected glaucomatous eyes from healthy eyes, with sensitivities at specificities of 80% and 95%. Entries in each cell: line 1, area under the receiver operator characteristic curve and 95% confidence interval; line 2, sensitivity at specificity of 80%—sensitivity at specificity of 95%.

Comparisons of the AUROCs of the best parameters from each structure analyzed to distinguish glaucomatous or suspected glaucomatous eyes from healthy eyes are displayed in Table 5. The AUROC for the best RNFL and ONH parameters (RNFL average and rim area) was significantly greater than the area for the best GCA and NRR parameters (GCA inferotemporal sector and inferotemporal NRR; P < 0.05), but not significantly greater than the area for the best BMO-MRW parameter (inferotemporal sector; P > 0.1). The receiver operating curves of the best Cirrus RNFL, GCA, and ONH parameters for distinguishing glaucomatous or suspected glaucomatous eyes from healthy eyes are shown in Figure 4. 

Figure 4

 

Diagnostic capability of Cirrus OCT. Receiver operating curves for the best parameters from each structure analyzed to distinguish glaucomatous (left) or suspected glaucomatous (right) eyes from healthy eyes.

Table 5

 

Comparisons of the AUROCs of the Best Parameters From Each Structure Analyzed to Distinguish Glaucomatous or Suspected Glaucomatous Eyes From Healthy Eyes

	RNFL Average	GCA Inferotemporal Sector	Rim Area	NRR Inferotemporal Sector	BMO-MRW Inferotemporal
RNFL average	NA	0.02	0.62	0.04	0.18
		0.01	0.30	0.04	0.11
GCA inferotemporal sector	0.02	NA	0.03	0.18	0.12
	0.01		0.02	0.20	0.10
Rim area	0.62	0.03	NA	0.04	0.35
	0.30	0.02		0.05	0.19
NRR inferotemporal sector	0.04	0.18	0.04	NA	0.16
	0.04	0.20	0.05		0.10
BMO-MRW inferotemporal	0.18	0.12	0.35	0.16	NA
	0.11	0.10	0.19	0.10

 Glaucomatous eyes are indicated in the first line in each cell. Suspected glaucomatous eyes are indicated in the second line in each cell.

As the sensitivity at very high specificities could be of particular interest when evaluating the diagnostic abilities of such parameters, we have also evaluated the partial AUROCs at 95% specificity cutoff levels. For RNFL-derived parameters, the largest partial AUROCs were seen for average RNFL in eyes with glaucoma and suspected glaucoma (0.021 ± 0.001 and 0.014 ± 0.002, respectively). For GCA-derived parameters, the largest partial AUROCs were seen for average GCA in eyes with glaucoma and suspected glaucoma (0.09 ± 0.001 and 0.06 ± 0.001, respectively). For ONH-derived parameters, the largest partial AUROCs were seen for rim area in eyes with glaucoma and suspected glaucoma (0.026 ± 0.002 and 0.015 ± 0.002, respectively). For NRR-derived parameters, the largest partial AUROCs were seen for the inferotemporal sector in eyes with glaucoma and suspected glaucoma (0.008 ± 0.001 and 0.004 ± 0.001, respectively). For BMO-MRW–derived parameters, the largest partial AUROCs were seen for inferotemporal sector rim area in eyes with glaucoma and suspected glaucoma (0.024 ± 0.002 and 0.008 ± 0.001, respectively). 

Discussion

In this study, we found a significant regional relationship between visual field sensitivity and retinal nerve fiber layer thickness and optic nerve head measurements with spectral-domain OCT. Associations were frequently stronger with the new parameter Bruch membrane opening minimal rim width than other RNFL and ONH parameters, particularly in the later stages of the disease. The correlations (r ²) ranged from 0.07 to 0.60 with RNFL parameters, from 0.15 to 0.49 with Cirrus-based neuroretinal rim thickness, and from 0.24 to 0.66 with BMO-MRW. 

To the best of our knowledge, no previous study has evaluated and compared the regional relationship between RNFL and ONH structural measurements taken by OCT and visual field sensitivity. We found a stronger regional structure-function relationship with RNFL measurements than with neuroretinal rim thickness provided by the Cirrus internal-specific software. We do not know how the thickness of the neuroretinal rim is calculated by the Cirrus specific software. This information is not clearly provided in the user manual or in the original publications made by the study group that validated the study of the optic nerve head using the Cirrus OCT. ^33–38 When studying the horizontal and vertical scan images that appear on the Cirrus printout, we can see that the thickness of the neuroretinal rim given by the software seems to be the distance between a black dot placed on the termination of the Bruch membrane and a red circle placed on the internal limiting membrane. The distance thus defined is generally not the shortest distance between the termination of the Bruch membrane and the inner limiting membrane, and is also generally not the horizontal distance between the Bruch membrane termination and the inner limiting membrane (i.e., the distance between BMO and internal limiting membrane along the BMO reference plane). 

When we calculated the minimum distance between the Bruch membrane opening and the internal limiting membrane by exporting and analyzing the scan with image-processing software, we found a stronger relationship with visual field sensitivity than with the RNFL and the Cirrus-based neuroretinal rim thickness. This new anatomical parameter was recently proposed by Reis et al. ²⁶ and Chauhan et al. ²⁷ to take into account the variable orientation of rim tissue in the optic nerve head. Indeed, because the trajectory of the axons over the ONH depends on the orientation of border tissue, assessment of the nonminimal rim width (e.g., horizontal rim width) could be inaccurate. In eyes where the trajectory of the axons is more horizontal to the measurement plane, a wider rim estimate is obtained compared with eyes with the same number of axons but where the trajectory of the axons is more perpendicular. Even if we do not know the exact calculation strategy used by Cirrus software, obtaining stronger relationships with visual field sensitivity validates the pertinence of this calculation strategy, which should probably be used instead of those used by the commercially available software. From a clinical point of view, the relationship between functional measurements with standard achromatic perimetry and structural measurements is important, because glaucoma diagnosis and follow-up are often based on a combination of structural and functional assessments. 

From the scatter plots showing the structure-function relationships, another important finding is that the neuroretinal rim thickness, either measured by the Cirrus software or using the BMO-MRW, seems to be much better correlated to the retinal sensitivity in advanced glaucoma than the RNFL is. The lack of association between RNFL thickness and visual field sensitivity in advanced glaucoma is well described and hypothesized to be due to a floor effect that occurs when there is almost no residual ganglion cell layer. ^37,38 For this reason, RNFL evaluation with OCT is usually less sensitive than visual field to track progression in advanced glaucoma. ³⁷ Neuroretinal rim thickness seems to be much better correlated to visual field sensitivity in advanced glaucoma. From the present data, it could be hypothesized that there is less of a floor effect with neuroretinal rim thickness, with changes that can be detected in eyes with advanced visual field loss. Further longitudinal studies should evaluate the value of the neuroretinal rim thickness assessment by OCT to monitor progression in advanced glaucoma. By contrast, in earlier stages of the disease, the RNFL thickness seems to be better correlated to the retinal sensitivity than the neuroretinal rim thickness is. This could be explained by RNFL tissue changes being easier to detect early in the disease, when there is still sufficient tissue left. 

We also evaluated the discriminating capabilities of Cirrus RNFL, ONH, neuroretinal rim thickness, and macular ganglion cell complex measurements. The best discrimination of glaucomatous and suspected glaucomatous eyes from healthy eyes were seen for rim area, ONH-derived parameters (AUROC 0.926 and 0.853, respectively); for average RNFL, RNFL-derived parameters (AUROC 0.922 and 0.863, respectively); for inferotemporal neuroretinal rim thickness, BMO-MRW–derived parameters (AUROC 0.917 and 0.851, respectively); for inferotemporal neuroretinal rim thickness, Cirrus-derived parameters (AUROC 0.910 and 0.842, respectively), and for the GCA inferotemporal sector, GCA-derived parameters (AUROC 0.901 and 0.769, respectively). 

A few previously published studies have evaluated and compared the capabilities of RNFL and ONH parameters measured with OCT to discriminate between glaucomatous and healthy eyes. Medeiros et al. ²² used Stratus OCT and found no significant differences between the AUROC for the RNFL thickness parameters (inferior 0.91) and the ONH parameters (cup/disc ratio area, 0.88). Also using the Stratus, Leung et al. ²³ found that the best parameters for distinguishing early glaucoma from healthy eyes were rim volume (AUROC: 0.966), cup/disc vertical ratio (AUROC: 0.962), cup/disc area ratio (AUROC: 0.960), and RNFL thickness (AUROC: 0.957). Using a more recent OCT technique with commercial SD-OCT equipment (RTVue; Optovue, Fremont, CA, USA), Rao et al. ²⁴ found inferior performance with ONH measurements compared with RNFL thickness parameters (best AUROC inferior rim area: 0.812; and inferior RNFL: 0.884, respectively). Using Cirrus OCT, Sung et al. ²⁵ found that RNFL average thickness was a better parameter for distinguishing advanced glaucoma from healthy eyes than rim area or cup/disc ratio or cup volume (AUC: 0.957 vs. 0.871). Using Cirrus OCT, Mwanza et al. ³³ found comparable diagnostic capabilities of ONH parameters compared with RNFL thickness (AUROC: vertical rim thickness 0.963; RNFL at 7′ 0.957; average RNFL: 0.950). Differences in acquisition speed, scanning rate, spatial resolutions, together with different layer detection algorithms and analytical software, may lead to different retinal nerve fiber layer thickness and ONH measurements and could explain the differences between the different commercially available OCT machines. Methodological reasons such as differences in the number of subjects included, differences in the representation of the different stages of glaucoma or differences in ethnicity could explain the variable results obtained with the Cirrus OCT. For instance, Sung et al. ²⁵ included Asians, who could have anatomical differences in RNFL and ONH compared with Caucasians. With a very similar methodology and population to those used in our study, Mwanza et al. ³³ obtained comparable results to those of the present study. 

When analyzing the discriminating capabilities provided by the BMO-MRW and Cirrus for neuroretinal thickness measurements, we found a slightly but significantly better performance with the BMO-MRW parameters. It is interesting to note that we found a smaller difference than Chauhan et al. ²⁷ found when comparing the capabilities of the BMO-MRW and BMO-HRW obtained with the Spectralis. This could be due to the fact that the Spectralis software used the optic disc plane as reference (horizontally defined by the opening of the Bruch membrane), whereas Cirrus OCT software measures the rim thickness as the RNFL turns to exit the optic nerve in a less horizontal manner. As the exact algorithm used by the Cirrus is not publicly available, caution should be exercised when assuming that Cirrus measurements are probably closer to the BMO-MRW. Similarly, these findings confirm that this new parameter should probably be used instead of those used by commercially available software. 

In conclusion, the present study shows that measurements of the retinal nerve fiber layer thickness and optic nerve head with SD-OCT correlate well with retinal ganglion cell function as assessed using standard achromatic perimetry. The structure-function relationship was in general stronger with the new parameter Bruch membrane opening minimal rim width than the retinal nerve fiber layer parameters, and stronger with the retinal nerve fiber layer parameters than the other optic nerve head parameters. This new parameter could be integrated into the commercially available OCT software to improve glaucoma diagnosis and follow-up. 

Acknowledgments

Supported by the Association de Recherche et de Formation en Ophtalmologie (ARFO). The authors alone are responsible for the content and writing of the paper. 

Disclosure: F. Pollet-Villard, None; C. Chiquet, None; J.-P. Romanet, None; C. Noel, None; F. Aptel, None