Utility of combining spectral domain optical coherence tomography structural parameters for the diagnosis of early Glaucoma: a mini-review
Mwanza et al. Eye and Vision
Utility of combining spectral domain optical coherence tomography structural parameters for the diagnosis of early Glaucoma: a mini-review
Jean-Claude Mwanza 0
Joshua L. Warren 1
Donald L. Budenz 0
0 Department of Ophthalmology, School of Medicine, University of North Carolina at Chapel Hill , Chapel Hill, NC , USA
1 Department of Biostatistics, Yale University , New Haven, CT , USA
Optical coherence tomography (OCT) has moved to the forefront of imaging modalities in the management of glaucoma and retinal diseases. It is modifying how glaucoma and glaucoma progression are diagnosed clinically and augmenting our understanding of the disease. OCT provides multiple parameters from various anatomic areas for glaucoma diagnosis, evaluation of treatment efficacy, and progression monitoring. While the use of multiple parameters has increased the likelihood of detecting early structural changes, diagnosing glaucoma in early stages is often challenging when the damages are subtle and not apparent on OCT scans, in addition to the fact that assessment of OCT parameters often yields conflicting findings. One promising approach is to combine multiple individual parameters into a composite parameter from the same test to improve diagnostic accuracy, sensitivity, and specificity. This review presents current evidence regarding the value of spectral domain OCT composite parameters in diagnosing early glaucoma.
Optical coherence tomography; Combination of parameters; Early glaucoma
Glaucoma is a slowly progressive degenerative optic
neuropathy characterized by the death of retinal
ganglion cells (RGCs) and their axons, with associated
morphologic changes to the optic nerve head (ONH),
retinal nerve fiber layer (RNFL), and ganglion cell-inner
plexiform layer (GCIPL). In most cases, the disease
slowly leads to complete blindness if inadequately or not
treated. Both medical and surgical treatment are often
able to significantly slow the disease progression, which
demonstrates the critical importance of accurate and
early detection of the disease for early initiation of
]. Over the years, a number of imaging
devices (i.e., retinal thickness analyzer, scanning laser
polarimetry, and confocal scanning laser
ophthalmoscopy) have been developed and used in the clinical
setting to aid the clinician in the diagnosis and monitoring
of glaucoma [
]. These modalities have since been
supplanted by optical coherence tomography (OCT),
particularly its spectral domain variant.
OCT has rapidly become the most widely used
imaging modality for glaucoma management. Since its
commercialization, it has revolutionized the
management of retinal diseases (i.e. AMD, diabetic maculopathy,
macular hole, central serous chorioretinopathy, retinal
vein occlusions, and vitreo-retinal interface disorders)
and glaucoma. In glaucoma, OCT provides objective,
precise, and highly reproducible quantitative evaluation
of inner retinal layers and the ONH [
diagnosing glaucoma is often unequivocal in moderate to
advanced stages, imaging of the ONH, RNFL, and macula
is, therefore, more valuable in the diagnosis of early than
moderate to advanced disease. This review presents a
compilation of available data on the usefulness of
spectral domain OCT (SDOCT) in diagnosing early
glaucoma by combining its parameters.
Diagnosis of Glaucoma
While identification of glaucomatous optic neuropathy
in moderate to advanced cases is often apparent
clinically, diagnosing glaucoma in early stages can be
challenging. Reasons for the challenge include the symptomless
nature of the disease until a substantial number of RGCs
and axons have been lost, the fact that no damage can
be detected at the stage of RGC apoptosis along the
glaucoma continuum [
], the wide interindividual
variation in the anatomy of the ONH and RNFL, and
the lack of a gold standard for establishing the diagnosis.
Studies have shown that glaucomatous structural
changes often precede functional loss [
that in some patients with early stage (i.e.,
preperimetric glaucoma) effort should be made to establish
the diagnosis based on structural changes alone. This is
crucial because waiting for more visible signs of the
disease would correspond to allowing the occurrence of
some irreversible damage. Thus, identification of early
damage to ocular structures affected by the disease is of
paramount importance for early treatment to prevent
irreversible functional loss.
SDOCT glaucoma modules currently include single
parameters from the ONH, peripapillary RNFL, and
macular GCIPL and/or ganglion cell complex (GCC).
One device, Spectralis OCT by Heidelberg, also provides
total retina thickness measurements in the macula.
There are currently more than a dozen OCT parameters
for glaucoma assessment; this number varies slightly
between platforms. A number of other parameters have
been described (i.e., lamina cribrosa depth or LCD [
lamina cribrosa curvature index or LCCI [
neuroretinal rim minimum distance band or MDB [
]) that are
not currently reported on OCT printouts. Despite good
diagnostic abilities of single parameters in early
glaucoma, SDOCT devices still falsely classify healthy eyes as
having glaucoma or miss the diagnosis of early glaucoma
in substantial proportions of subjects [
]. Use of
more than one individual parameter from the ONH,
RNFL, and GCIPL or GCC for glaucoma assessment is
therefore justified because it increases the likelihood of
detecting a structural abnormality in at least one
anatomic area. Indeed, findings from the three areas do not
always show agreement. The caveat of such an approach
is that it may increase the rate of false-positive
conclusions unless appropriate corrections for multiple
comparisons are made.
Combination of parameters
There is abundant and convincing in vivo evidence of the
association between glaucoma and structural damage to
the ONH, RNFL, and macular GCIPL or GCC. OCT
provides proof regarding qualitative and quantitative
information collected on multiple parameters. The diagnosis is
then based on careful interpretation of data on parameters
from these anatomic structures combined with the clinical
impression from the visual field and ocular examination.
While the ideal situation to ascertain the diagnosis is to
have an agreement among results on parameters from
anatomic areas, that is not always the case in reality.
Indeed, the results are more likely to agree in moderate to
advanced disease. On the contrary, they often disagree in
early stages when structural changes are subtle. Thus,
OCT results classified as within normal range at initial
visits in early stages do not necessarily indicate the
absence of glaucomatous structural damage. It may only
mean that the magnitude of the changes is still below the
threshold of detection by OCT. Monitoring with serial
scans over time is then required for OCT to detect an
abnormality, when the device reaches its minimum
sensitivity threshold. In addition, change within the normal range
beyond the change expected from aging may also be an
important sign of early disease. Therefore, it is important
to develop methods to optimize OCT’s ability to
differentiate healthy eyes from eyes with early glaucoma.
The availability of refined statistical methods allows
the development of combinatorial algorithms as tools
for disease risk categorization, diagnostic classification,
and prognostic determination. These methods combine
information from single parameters to enhance
diagnostic accuracy. Although there is still a shortage of data,
available evidence shows that combining individual
SDOCT parameters using various methods can offer
improved diagnostic performance for early glaucoma. Such
an approach minimizes the clinician’s challenge of
mentally integrating and processing the panoply of clinical
information and OCT data from various parameters
when attempting to determine whether a subject has
glaucoma or not. This challenge is expected to be
greater should OCT glaucoma modules include
additional parameters in the future. The sections below
present available data on detection of early glaucoma
using a combination of SDOCT parameters. Figure 1
shows locations of the scans on four selected SDOCT
platforms and the anatomical structure from which the
parameters are measured.
AND- and OR-logic combinations
AND- and OR-Logic are binary concepts and basic
operations of Boolean algebra. In this framework x AND
y = 1 if both x = 1 and y= 1, so x AND y= 0 if x, y, or
both = 0. x OR y=1 if x = 1 and y = 0 or the opposite, or
if both x and y=1; so x OR y= 0 if both x and y=0.
AND- and OR-Logic combination methods have been
investigated as means to improve the diagnostic
discriminating ability of SDOCT parameters. Mwanza et al.
used this approach to assess how GCIPL parameters
performed in discriminating between 50 patients with
early perimetric glaucoma and 49 age-matched normal
subjects when used individually or in combination with
peripapillary RNFL or ONH parameters measured with
Cirrus HD-OCT (Carl Zeiss Meditec, Inc., Dublin,
California, USA) [
]. The results indicated that pairing
the minimum GCIPL and average RNFL, the minimum
GCIPL and rim area, or the minimum GCIPL and
inferior quadrant RNFL through OR-Logic method improved
the sensitivity, negative predictive value (NPV) and
negative likelihood ratio (NLR) relative to the best single
GCIPL, RNFL, and ONH parameters, without
significantly affecting the specificity. The binary OR-Logic
combination of minimum GCIPL and average
peripapillary RNFL provided the best overall sensitivity (94%),
specificity (85.7%), positive likelihood ratio (PLR, 6.58),
and NLR (0.07) compared to the best single GCIPL
(minimum: 82%, 87.8%, 6.69, and 0.21), RNFL (inferior
quadrant: 74%, 95.9%, 18.1, and 0.27), ONH (rim area:
68%, 98%, 33.3, and 0.33), and best AND-Logic
combination (minimum GCIPL + inferior quadrant RNFL: 64%,
100%, infinity, and 0.36). The same approach was used
by Jeoung et al., who reported that combining average
RNFL and minimum GCIPL measured with Cirrus
HDOCT achieved significantly higher sensitivity (81.1%) and
specificity (97.5%) than other OR-Logic and AND-Logic
combinations, and single parameters [
]. The findings
by both Mwanza et al., [
] and Jeoung et al., [
suggest that AND-Logic combinations are associated with
low diagnostic performances in early glaucoma, likely
because of the disagreement between RNFL and GCIPL
results at this stage of the disease. From the practical
standpoint, the findings also suggest that the diagnosis
of early glaucoma should be considered in the presence
of either abnormal GCIPL or RNFL parameters, not
necessarily both combined.
The MDB is a recently described SDOCT
threedimensional (3D) quantitative neuroretinal rim parameter,
although it was first mentioned a decade ago [
]. It is
captured with high-density raster scan (i.e., 193 raster line
volume scan) with Spectralis OCT (Heidelberg
Engineering GmbH, Heidelberg, Germany) and represents the
shortest distance between the internal limiting membrane
(ILM) and the Bruch’s membrane/retinal pigmented
epithelium (BM/RPE) termination . It differs from the
MRW, a 3D neuroretinal rim parameter obtained with a
low-density ONH scan made of 24 radial lines, defined as
the shortest distance between the ILM and the BMO [
The MRW uses the BMO to determine the disc margin
whereas the MDB uses the RPE/BM complex as disc
]. Although by itself it distinguishes normal eyes
from eyes with early glaucoma well (area under the curve
of the receiver operating characteristics or AUC of 0.952
and sensitivity of 77.4% at 95% specificity for global MDB
thickness), AND-Logic combinations of MDB of the
inferior, superotemporal, and superonasal sectors, with
the inferior quadrant RNFL performed significantly better
(AUC: 0.984) than the best combination of RNFL
parameters (0.966) and all single RNFL parameters . The
model suggested by Gmeiner and colleagues was created
by combining each of the 7 Spectralis BMO-MRW
parameters (global, temporal superior, nasal superior,
nasal, nasal inferior, temporal inferior, and temporal) (Fig. 2)
to its corresponding RNFL parameter [
], based on the
The combined parameters were compared to single
ones for their ability to distinguish healthy subjects and
patients with pre-perimetric glaucoma. The overall best
multivariable parameter resulted from the combination
of global parameters (AUC: 0.849, sensitivity at 90% and
95% specificity: 56% and 42%), which increased the
performance, but non-significantly, compared to global
BMO-MRW (0.821, 52% and 28%) and global RNFL
(0.839, 50% and 44%). This method is, in fact, an
AND-Logic strategy, although the combinations are
limited to BMO-based parameters of the same location.
Machine learning classifiers and linear discriminant analysis
Imaging data are commonly used in medical
decisionmaking for both diagnosis and treatment and
monitoring of diseases. Machine learning classifiers (MLCs) (i.e.
linear regression, logistic regression, decision trees,
Random Forest, support vector machines, artificial neural
networks) and linear discriminant analysis (LDA) are
well-established analytical methods for combining input
parameters into discriminant functions for classification
of patients into groups. Fang et al., studied 34 eyes with
early glaucoma and 42 normal eyes and assessed the
discriminating abilities of single ONH, RNFL, and GCC
parameters measured with RTVue OCT (Optovue Inc.,
Fremont, California, USA) [
]. Vertical cup-to-disc
ratio (VCDR) (AUC: 0.930 and 79.4% sensitivity at 95%),
average RNFL (0.915 and 76.5%) and rim area (0.913
and 61.8%) were the best single discriminants. Their
combination using a logistic regression model improved
the discriminating ability (0.949 and 82.4%) relative to
the best single parameter, but the increase was not
statistically significant. The disadvantage of this approach is
that the choice of parameters used in the combination
ignores other factors that may also contribute to the
improvement of the diagnostic performance. A recent
study compared the diagnostic performance of 19
individual ONH and RNFL Cirrus OCT parameters and a
multivariable predictive model using logistic regression
with backward elimination technique in a study
population of African Americans (103 healthy and 52 with early
]. The best combination included age, disc
area, and RNFL parameters and the multivariable model
was defined as:
0:147 þ 0:73SQ RN FL þ 0:002CH8 RN FL
þ 0:016CH12 RN F L þ 0:045CH1 RN FL
þ 0:001CH6 RN FL þ 2:409Disc Area þ 0:098Age
where SQ is superior quadrant and CH is clock-hour.
Despite the multivariable model having an improved
performance (AUC: 0.892) compared to the best single
RNFL parameters (clock-hour 12: 0.868; inferior
quadrant RNFL: 0.857; and average RNFL: 0.855), the
improvement was not statistically significant. Individual
GCIPL parameters were not included in the logistic
regression analysis. It is unclear whether adding
inferotemporal GCIPL (AUC: 0.936) would have further improved
the performance of the combination. In another
investigation, the diagnostic performances of linear
discriminant analysis (LDA) and Classification And Regression
Tree (CART) were compared to those of single Cirrus
HD-OCT ONH and RNFL parameters in early glaucoma
]. Both the CART model (0.99) and the LDA (0.94)
discriminated better than any of the single parameters
(AUCs: 0.61–0.89). They also had much lower
misclassification rates than single parameters. The CART model
included thicknesses of the average, superior, inferior,
and nasal quadrant RNFL, disc area, VCDR, cup volume,
and RNFL symmetry. LDA combined disc area, rim area,
average CDR, VCDR, inferior quadrant RNFL, superior
quadrant RNFL, and average RNFL in the following
1:56Disc Area−1:83Rim Area−6:21Average CDR
þ5:12V CDR−0:022SQ RN F L T hickness
−0:031IQ RN FL T hickness
þ0:016Average RN FL T hickness
ONH, peripapillary RNFL, and GCC parameters
measured with RTVue were also assessed by Huang et al.
for their ability to differentiate normal from
glaucomatous eyes, as single parameters and after their
combination using LDA [
]. Although glaucomatous eyes
were classified as stage 1 (MD: − 5 to − 0.01 dB) and
stage 2 (MD: − 12 to − 5.01 dB) on the Bascom Palmer
Modified Glaucoma Staging System [
], the MD of the
group was − 3.30 ± 2.64 dB, indicating that they all had
early glaucoma[Hodapp, 1993 #464]. Their final linear
discriminant function was as follows:
−4:332−0:969Disc Area þ 0:17ST 1 RN FL þ 0:22ST 2 RN FL
þ0:01NU2 RN FL þ 0:012IT 1 RN FL
þ0:048Standard Deviation o f Superior
−In f erior Hemisphere GCC
This combination provided an overall better diagnostic
performance (AUC: 0.970, sensitivity: 86.3%, and
specificity: 95.9%) in early glaucoma than the best single
variables (0. 919, 81.5%, and 87.8% for average RNFL; 0.871,
75.3% and 90.5% for inferior hemisphere GCC; 0.854,
71.9%, and 91.9% for VCDR). Yoshida et al. also used
the random forests classification method to investigate
the discrimination between 126 glaucomatous and 84
normal eye using a total of 151 peripapillary RNFL,
macular RNFL, and GCIPL parameters measured with
3D-OCT 1000 (Topcon Corp., Tokyo, Japan) [
method determined that 81 of the 151 parameters
(average RNFL; mean, superior and inferior hemiretina
macular RNFL; mean, superior and inferior and hemiretina
GCIPL; grid macular RNFL in the inferior and superior
temporal areas; grid GCIPL in the inferior and superior
temporal areas; superior, nasal and inferior quadrant
peripapillary RNFL; 30o superotemporal, superonasal,
nasal superior, inferior and inferotemporal peripapillary
RNFL) were significant predictors of early glaucoma.
The diagnostic performance of the random forests
combination (AUC: 0.985, sensitivity: 92.9%, specificity:
96.0%) was significantly larger than that of macular
RNFL (AUC: 0.934). While random forests are considered
an effective MLC algorithm with higher classification
accuracy, its prediction performance beyond the limits of
the response values in the training data is weak,
particularly when used for regression tasks. Overall, it
appears from these studies that combination of single
OCT parameters using MLCs and LDA allows incremental
diagnostic performance in early glaucoma. The magnitude
of the improvement varies from one method to another
based on the type of device used; the original parameters
entered in the model, and the characteristics of the
population. Table 1 summarizes the main features of
selected combinatorial models discussed below.
The UNC OCT index
The UNC OCT Index is a combinatorial paradigm that
was developed to facilitate the diagnosis of early
]. The significant steps for constructing the
model is summarized in Fig. 3. Briefly, the model inputs
age and 16 SDOCT quantitative parameters (5
peripapillary RNFL, 8 GCIPL, and 3 ONH). Because of high
correlation (positive and negative) between these
parameters, they were first submitted to exploratory factor
analysis (EFA) with promax rotation to extract latent
factors accounting for a large proportion of the
variability seen in the original set of parameters. This process
identified 5 latent factors accounting for 94.1% of the
total variability. Fitting a multivariable logistic regression
model with these 5 factors as explanatory variables and
glaucoma status as the dependent variable (early
glaucoma vs. normal status) identified 3 of the elements as
significant predictors of early glaucoma. Using the final
formula in Fig. 1, the algorithm instantly and
automatically outputs a predicted probability for early glaucoma
that defines the UNC OCT Index. The index is a
continuous value between 0.0 and 1.0, 0 being no probability
of glaucoma and 1 being 100% probability of glaucoma.
This model has determined 0.34 as the predicted
probability cutoff. Values below 0.34 and those above 0.34
suggest low and high likelihood that the observed
structural changes are glaucomatous, respectively. The
UNC OCT Index differentiated eyes with early
glaucoma from normal eyes better than all single
parameters both in the modeling and internal validation sets,
based on AUC (0.995 vs. 0.943), sensitivity (98.6% vs.
89.9% at 95% specificity), Akaike Information Criterion
(AIC, 43.3 vs. 59.6), median 95% prediction interval
length (PIL: 0.05 vs. 0.095–0.15). The robustness of the
UNC OCT Index has also undergone an independent
validation using a separate cohort of normal eyes and
two cohorts of glaucomatous eyes with milder visual
field deficit (group 1 MD: - 1.3 ± 1.3 dB and group 2
AUC = area under the curve; CI = confidence intervals; GSDI = Glaucoma Structural Diagnostic Index; RNFL = retinal nerve fiber layer; GCC = ganglion cell complex;
VCDR = vertical cup-to-disc ratio; GCIPL = ganglion cell-inner plexiform layer; CDR = cup-to-disc ratio; UNC = University of North Carolina
MD: − 0.7 ± 1.0 dB) than eyes used in the modeling
group (MD: − 3.19 ± 1.69 dB). The AUC and sensitivity
at 95% specificity of the UNC OCT Index were 0.96 and
85.4% in patients with visual field mean deviation ≥ − 4 dB
and 0.95 and 81.7% in those with mean deviation > − 2 dB.
Relative to the UNC OCT index, the diagnostic
performance indices of the best single variables from each
anatomic area the two ≥ − 4 dB group were 0.93 (P = 0.05)
and 0.92 (P = 0.06) for VCDR, 0.92 (P = 0.014) and 0.91
(P = 0.03) for average RNFL, and 0.91 (P = 0.009) and 0.90
(P = 0.026) for minimum GCIPL. The sensitivities of the
best single parameters were all significantly (all P ≤ 0.008),
except for rim rea (P = 0.07). The results of the independent
validation confirmed the effectiveness of the UNC OCT
Index combinatorial algorithm over that of single OCT
parameters in detecting early glaucoma. The algorithm is
stable regarding accuracy and computational speed, and
allows more OCT and/or non-OCT parameters to be
added as necessary. It is a promising way forward for
improving the diagnostic performance of OCT information,
and it could be a useful tool for clinical decision-making in
glaucoma practice. Figure 4 shows Cirrus HD-OCT
data obtained in a glaucoma suspect in whom the UNC
OCT Index algorithm suggested a high probability that
the right eye was likely glaucomatous (predictive
probability: 0.768) whereas the left eye was likely
nonglaucomatous (predictive probability: 0.087).
The Glaucoma structural diagnostic index (GSDI)
The GSDI is a tool developed to improve glaucoma
diagnostic accuracy using a combination of SDOCT
ONH, peripapillary RNFL, and GCC parameters [
The parameters were measured with RTVue OCT in
glaucomatous eyes (n = 236), a reference normal eye
population (n = 105), and a cohort of normal eyes (n = 118).
The multivariable logistic model used to construct the
GSDI identified the following 3 significant predictors: 1)
composite overall RNFL + GCC thickness, 2) composite
RNFL focal loss volume (FLV) + GCC FLV, and 3) VCDR.
The final model function was defined as:
regarded as early glaucoma, the GSDI was at most 0.874
with a sensitivity of 60.7% at 95% specificity although it
was not clear how it compared to single variables since
their performances at this stage were not provided.
−0:74Composite Overall T hickness
þ 0:70Composite FLV þ 3:37V CDR−3:69
The overall diagnostic accuracy of the combination of
these parameters (0.922) was significantly better than
that of the best single parameter (nerve fiber layer global
loss volume, NFL GLV: 0.896). Below stage 2 of the
Glaucoma Severity Staging 2 (GSS2) [
], which may be
The OCT Glaucoma diagnostic calculator
The OCT Glaucoma Diagnostic Calculator was
proposed as a tool for the detection of glaucoma regardless
of the stage of the disease [
]. It is based on a
multivariable predictive model that uses a combination of Cirrus
HD-OCT ONH, peripapillary RNFL thickness, and
macular GCIPL thickness parameters. A total of 17
parameters were evaluated. The development and validation
of this model included data of 500 healthy eyes and a
separate group of 187 glaucomatous eyes of all severity stages.
The study and validation groups covered 92 and 37 stage 1
glaucomatous eyes based on the GSS2, respectively. Three
different models were evaluated and compared, with model
#1 using quantitative data only, model #2 qualitative data
only, and model #3 a combination of qualitative and
quantitative information. Model #3 proved to be the best
and used a combination of age, color classification code for
superonasal GCIPL, superotemporal GCIPL, minimum
GCIPL and average CDR; thicknesses of inferotemporal
GCIPL and inferior quadrant RNFL; and values of average
CDR and VCDR. Colors are based on the classification
relative to the normative database and are given scores of 0 for
green (all parameters), 1 for yellow (all parameters), 2 for
red (all parameters), and 3 for gray (average CDR). Though
details were not provided, it was reported that this model
significantly outperformed all single parameters in early
glaucoma. The predicted probability of the model 3 is
e^ð0:905 þ 0:044Age−1:477ðSNGCC ¼ yellowÞ
−1:190ðSNGCC ¼ redÞ þ 1:403ðSTGCC ¼ yellowÞ
þ1:095ðSTGCC ¼ redÞ þ 1:455ðMCGC ¼ yellowÞ
þ1:109ðMCGC ¼ redÞ þ 0:006ðCDAC ¼ yellowÞ
þ2:231ðCDAC ¼ redÞ þ 0:583ðCDAC ¼ grayÞ
þ0:117VCDð 100Þ=ð1 þ e^ð0:905 þ 0:044Age
−1:477ðSNGCC ¼ yellowÞ−1:190ðSNGCC ¼ redÞ
þ1:403ðSTGCC ¼ yellowÞ þ 1:095ðSTGCC ¼ redÞ
þ1:455ðMCGC ¼ yellowÞ þ 1:109ðMCGC ¼ redÞ
þ0:006ðCDAC ¼ yellowÞ þ 2:231ðCDAC ¼ redÞ
þ0:583ðCDAC ¼ grayÞ−0:034ITGC−0:035IRNFL
−0:099CDAð 100Þ þ 0:117VCDð 100ÞÞÞ
with SNGCC, STGCC, and MCGC being colors of the
superonasal, superotemporal, and minimum GCIPL;
respectively. CDAC is the color of the average CDR; ITGC,
IRNFL are values of the inferotemporal GCIPL and
inferior quadrant RNFL thicknesses, respectively. CDA C/D
and CVD are values of the average and vertical CDR,
respectively. The calculator outputs a probability
classification that ranges between 0.00 and 1.00 and labelled the
result as positive (high probability of glaucoma), negative
(low probability), or inconclusive (intermediate
probability). This model achieved an AUC of 0.937 and sensitivity
of 77.8% at 95% specificity compared to 0.877 and 59.8%
(all P < 0.001) for inferotemporal RNFL.
Conclusions and future perspectives
Multiple SDOCT parameters from various ocular
anatomic areas are now available that clinicians use for
distinguishing between diseased and non-diseased subjects,
particularly in early stages. The challenge for diagnosing
early glaucoma clinically and the difficulty of
interpreting several parameters that yield conflicting information
have been the impetus for investigating various ways to
improve diagnosis of early glaucoma while alleviating
the clinician’s tasks. A desirable approach has been to
combine multiple diagnostic tests or parameters from
the same test to obtain an optimal composite diagnostic
test with higher sensitivity and specificity that detects
the presence of the disease more accurately. This
minireview has outlined how combining information from
different structural OCT parameters may be a
complementary tool for the diagnosis of early glaucoma. It
transpires from this review that: (1) combinatorial
models of OCT structural parameters for glaucoma have
so far remained research tools, (2) such models for early
glaucoma should be prioritized, as the clinical diagnosis
of moderate to advanced glaucoma is generally
straightforward, and (3) combination of single parameters into
composite improves the diagnostic ability of OCT in
early glaucoma. The improvement should not be judged
based on AUC alone, but together with sensitivity,
specificity and other diagnostic performance indices. However
as of to date, just as there is no agreed-upon unique
standard guideline for diagnosing early glaucoma with
the aid of single OCT parameters, there is equally no
consensus yet on what constitutes the best combinatorial
model for OCT parameters. Although some patients
with early glaucoma can be diagnosed with a single
baseline visit, many of them will be diagnosed after
followup and detection of progressive glaucomatous changes
to the structures affected by the disease even if they
remain in the normal range for age. The question for
future research is whether OCT combinatorial models
may help detect progression earlier than single
parameters in early glaucoma. Despite a few recent reports to
the contrary [
], it is generally known that
glaucomatous structural changes are more difficult to detect
in moderate to advanced disease. Thus, future research
may also need to investigate whether combinatorial
models may improve detection of structural progression
in moderate to advanced glaucoma. It is an
improvement in detection of early glaucoma and progression
throughout the course of the disease that will allow
earlier diagnosis and timely initiation or adjustment of
treatment, to reduce the burden of glaucoma-related visual
loss and its consequences.
AIC: Akaike’s Information Criterion; BM: Bruch’s membrane;
CART: Classification and regression tree; CDR: Cup-to-disc ratio;
EFA: Exploratory factor analysis; FLV: Focal loss volume; GCC: Ganglion cell
complex; GCIPL: Ganglion cell-inner plexiform layer; GLV: Global loss volume;
GSS: Glaucoma staging system; ILM: Internal limiting membrane;
LCCI: Lamina cribrosa curvature index; LCD: Lamina cribrosa depth;
LDA: Linear discriminant analysis; MD: Mean deviation; MDB: Minimum
distance band; MLC: Machine learning classifier; NFL: Nerve fiber layer;
NLR: Negative likelihood ratio; ONH: Optic nerve head; PIL: Prediction interval
length; PRL: Positive likelihood ratio; RNFL: Retinal nerve fiber layer;
RPE: Retinal pigmented epithelium; SDOCT: Spectral domain optical
coherence tomography; UNC: University of North Carolina
This publication was supported by the Department of Ophthalmology,
University of North Carolina at Chapel Hill, USA, and by CTSA Grant UL1
TR001863 from the National Center for Advancing Translational Science, Yale
University, New Haven, Connecticut, USA. These institutions had no role in
the design, data collection, and drafting of the manuscript.
Availability of data and materials
Conception, design, and drafting of the article by JCM, JLW, and DLB. All
authors have read and approved the final version of the manuscript.
JCM, JLW, and DLB have a patent licensed to Carl Zeiss Meditec, Inc.
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