Ординатура / Офтальмология / Английские материалы / Essentials in Ophthalmology Glaucoma_Grehn, Stamper_2008

best-known application of this technique in the milieu of visual fields is pointwise linear regression of sensitivity over time [14].

4

4.1.2Historical Perspective: Optic Nerve Head Photography

Optic nerve head photography is the longest established and most widely available imaging technology. It relies on the observer’s subjective interpretation of the photograph, without recourse to analysis software (unlike the newer imaging devices). It is the only full-colour imaging technology, and allows a number of glaucomatous features (such as peripapillary atrophy and disc haemorrhages) to be detected that are not readily detected by automated imaging technologies. Optic nerve head photographs may be monoscopic or stereoscopic; the latter is preferred, as it achieves better inter-observer agreement [22]. Stereoscopic images may be acquired simultaneously or sequentially. Simultaneous stereophotographs are preferred as they are associated with more reliable determinations of rim loss and cup depth than sequential acquisitions [4, 22].

The assessment of progression from photographs is subjective, although in trained hands the sensitivity and agreement is fair. In OHTS, the independent optic disc reading centre was used to grade glaucomatous change in stereophotographs over time. The inter-observer agreement for detecting glaucomatous disc change by masked graders in that study has been reported as “good to excellent”, with kappa values in the range of 0.65–0.83, specificity from 98 to 100%, and sensitivity from 64 to 81% [26], comparing favourably with other masked grading studies [3, 6, 11, 37]. However the sensitivity of the masked graders—estimated from the number of stereophotographs correctly “regraded” as not demonstrating deterioration in OHTS—was poor: as low as 64% after the first year of the study [26]. This highlights the marked difficulties encountered in trying to consistently detect the small optic disc changes that occur at the earliest stages of glaucoma and ocular hypertension, despite having experienced graders and a robust protocol. Optic nerve head photography is limited by its reliance on the judgements of expert observers or trained graders. This is not the case with the newer, automated, optic nerve head imaging devices; in an animal model of glaucoma, confocal scanning laser tomography had greater sensitivity (with high specificity) for detecting surface change than expert clinicians viewing stereophotographs [12]. There are no reports of the ability of non-expert observers to identify progression using optic nerve photographs.

The greatest barrier to the routine use of optic nerve head photographs in clinical practice is the lack of appropriate viewing systems. New technology is becoming available that permits the viewing of stereoscopic photographs in three dimensions on the computer screen [22]. Retinal nerve fibre layer photographs may also be used to detect progression [6, 29]; however, this technique is perhaps too technically difficult and time-consuming to be used in routine clinical practice.

4.1.3The Potential of Optic Nerve Head Imaging Devices

Given the variability between observers in stereophotograph evaluation, automated imaging devices present an attractive proposition, with the potential for high intertest repeatability and the capacity to generate quantification data, both of which would be advantageous in the detection of structural progression. At the time of writing, three devices, each of which employs a different technology, are pre-eminent. These devices are: the Heidelberg retina tomograph (HRT, Heidelberg Engineering, GmbH, Heidelberg, Germany), which employs confocal scanning laser ophthalmoscopy; the GDx-VCC (Carl Zeiss Meditec, Dublin, CA, USA), which employs scanning laser polarimetry; and the optical coherence tomography scanner (OCT, Carl Zeiss Meditec, Dublin, CA, USA), which employs low-coherence interferometry. A detailed description of each technology is beyond the scope of this chapter. As the HRT has been available in the clinical setting for approximately 15 years, more longitudinal patient data are available for it than for the other two devices. As such, the HRT’s role in the identification of disease progression is established, with algorithms being available in the operational software and a number of other progression techniques proposed in the literature.

Summary for the Clinician

■Documentation of the clinical examination of the disc in the patient’s records is not sufficient to monitor structural progression reliably

■Stereophotographic disc photographs have a proven track record in clinical trials, although their use in clinical practice is very much experiencedependent

■Semi-automated optic nerve head imaging devices such as the HRT, OCT and GDx-VCC have great potential in the monitoring of glaucomatous progression

4.2HRT

4.2.1HRT Progression: Available Techniques

There are two progression algorithms native to the HRT- 3 software, “trend analysis” and “topographical change analysis” (TCA) [16]. The trend analysis compares the values at follow-up to those at baseline for global and stereometric summary indices. This is illustrated graphically as the normalised change from baseline over time. Normalisation is performed to enable the same scaling of change for each parameter from +1 (maximum improvement) to −1 (maximum deterioration). Normalisation is achieved by using the ratio of the difference between a given value and baseline to the difference between the average value in a normal eye and in an eye with advanced glaucoma [7]. The trend analysis is therefore interpreted in terms of empirical values; a formal regression analysis giving a quantified rate of change over time is not performed. It is also interesting to note that, to date, there are no studies in the literature that have used this technique.

TCA examines changes in the topographical height of the HRT image at the superpixel level [8], the height of a pixel being measured from the mean height of the peripheral reference ring (Fig. 4.2). Superpixels are discrete areas of the ONH image measuring 4 × 4 pixels; there are 64 × 64 superpixels within a topography image. TCA quantifies the change within the disc margin contour. The key determinant in TCA is the variability in topographical height values within the superpixel over the two sets of three images (single topography images) taken at baseline and at follow-up. The statistical method estimates the probability of the value of the difference in height between images occurring by chance alone. Where p < 0.05, the probability is low and the change is therefore unlikely to be due to chance and is ascribed to glaucoma. Where the variability is high between images, which typically occurs at the edge of the cup and along blood vessels, a much greater difference in height values needs to be identified to reach significance. TCA generates a “change probability map”—the reflectivity map is overlaid with colour-coded pixels, red pixels representing significant height depression and green pixels representing significant elevation. In the current software, for two followup images, superpixels will be flagged as significant in the second follow-up if the change occurs in both images. For three follow-up images, superpixels are flagged as significant if the change occurs in all three follow-ups. Finally, for more than three follow-ups, the change needs to be observed in at least three of the last four images. In two longitudinal studies, the criterion

for progression by TCA was based on empirical data from normal subjects, whereby less than 5% of normal controls have greater than 20 significantly depressed superpixels within the optic disc margin [9, 24]. Progression was therefore identified when clusters of 20 or more significantly depressed superpixels within the disc margin were observed in three consecutive images. In a later study, the same group defined criteria for change by expressing the size of the largest cluster of depressed superpixels within the disc as a percentage of the total number of superpixels within the contour line, thereby accounting for variability in optic disc size [2]. In the HRT-3 software, the TCA report (an example of which is shown in Fig. 4.1) shows a “trend analysis of the cluster defect”. This enables the change of area and volume of a selected cluster to be monitored over time, allowing some scope to quantify the change over time observed using TCA.

A recent study has compared the ability of TCA (HRT-II software) to identify disease progression with expert assessment of optic disc stereophotographs [19]. A 65% concordance was observed between TCA and stereophotographic assessment; 30% of subjects progressed by TCA alone and 6% progressed by photographs alone. It is likely that the discrepancy between techniques is a reflection of the fact that both techniques are examining different aspects of structural progression. Stereophotographic assessments examine a number of features, such as rim loss, presence or absence of splinter haemorrhages and nerve fibre layer defects, which are not specifically picked up by TCA. The TCA, on the other hand, is looking for surface changes or deformation, which is not easy to identify in stereophotographic examination. It is therefore plausible that some of the subjects identified as progressing by TCA and not by photographs demonstrated genuine disease progression—suggesting that the two techniques should be used in a complementary fashion.

4.2.2HRT Progression: Stereometric Parameter vs. Pixel-Based Techniques

A number of different progression algorithms have been proposed in the literature for the HRT which are yet to be incorporated into the operational software. The majority of these strategies have focussed— unlike TCA, which assesses topographical height change—on the assessment of stereometric parameter change. TCA has the advantage of being able to identify surface height changes across the entire ONH

Heidelberg Retina Tomograph

TCA Overview

Patient:

Baseline: 29/Nov/1994

HEIDELBErG

ENGINEERING

OS

400

01/05 01/06 01/07 01/08

Total Volume Change

Elschnig’s ring

Reference ring

320 microns

The standard reference plane is set 50 microns below the temporal disc edge

Elschnig’s ring

Retinal surface

Reference plane

Neuroretinal rim

and parapapillary surface. However, it is difficult to quantify change in a way that translates into a clinically understandable phenomenon, such as neural rim narrowing or notching. Stereometric parameters, on the other hand, give numerical values to clinically recognisable features. The majority of stereometric parameters—particularly those relating to cup and rim dimension—are easily comprehensible to the glaucoma clinician. In two separate assessments of HRT test-retest variability [30, 35], rim area has been found to be the most repeatable and reliable parameter, and may therefore be a suitable metric for monitoring disease progression.

A criticism of the use of stereometric parameters is that their magnitude and variability are dependent on the position of the disc margin contour line (which is operator-dependent) and the position of the reference plane (which is not the case for TCA, which instead depends on the height of the more stable peripheral reference ring). The reference plane is located parallel to and below the retinal surface within the threedimensional optic nerve image, and is used to delineate structures within the disc margin above as neuroretinal rim and below as cup (Fig. 4.2). Most stereometric parameter values are dependent on the position of the reference plane; a deeply placed plane generates a smaller cup and a greater rim, whereas a superficially placed plane generates a greater cup and a smaller rim. Disc area, height variation contour and cup shape

measure are parameters which are independent of reference plane.

The standard reference plane is the default plane in the Heidelberg software. It is located 50 µm below the contour line at the temporal disc margin, between −10 and −4°. The choice of location was based on the mean surface inclination angle of the optic nerve head and because it coincides with the papillomacular bundle [5]. It was assumed that the papillomacular bundle maintains a stable thickness, as central visual acuity is not affected until the latter stages of glaucoma. This has not been supported by OCT studies, which demonstrate reduced bundle thickness in glaucoma despite maintenance of good visual acuity [10]; it is therefore likely that the reference plane height changes as glaucoma progresses. An alternative reference plane, at 320 µm, has been shown to generate less variable rim area measurements than the standard reference plane [31]; it may therefore be a more appropriate option for discriminating true structural change from measurement “noise”. This plane has a fixed offset situated 320 µm posterior to the reference ring located in the image periphery. This reference plane has the advantage of greater stability, but it may not be appropriate for use in discs with oblique insertions, where the difference between the retinal height and the cup level may exceed 320 µm. Also, disease processes occurring outside the disc margin, such as peripapillary atrophy, may influence measurements.

4.2.3HRT Progression: Stereometric Parameter Event Analyses

The first indication that the HRT could be used to iden-

4tify glaucomatous changes utilised stereometric parameters [17]. This study was the first to demonstrate that

the HRT could identify structural changes prior to the identification of repeatable glaucomatous field loss, thereby highlighting the great clinical potential of ONH imaging devices in the monitoring of glaucomatous progression. The investigators compared sequential HRT images acquired one year apart from two cohorts: a cohort of 13 eyes of 11 normal control subjects and a cohort of 13 eyes from 13 ocular hypertensive subjects who developed repeatable glaucomatous VF loss at a date subsequent to their second HRT image. The Wilcoxon signed-rank statistical test was used to identify whether a significant change in stereometric parameters had occurred between the first and second image acquisitions. No significant global or segmental parameter changes were identified in the control cohort. In the ocular hypertensive cohort, significant (p < 0.05) changes were identified in global and superior sector rim area. The technique was subsequently refined by estimating 95% confidence limits for change in sequential HRT images acquired from normal control eyes, which was used as an estimate of normal measurement variability [18]. The 95% normal variability limits were used to define thresholds for glaucomatous change within stereometric parameters, whereby any change exceeding the limits was ascribed to glaucomatous damage and within the limits as measurement noise. A potential shortcoming of this approach is that the limits of variability to identify change were based on the test-retest variability of a cohort of normal subjects. As some individuals may have greater measurement variability than others, limits of variability derived from a population may not be suitable for all individuals. In this context, it may be more appropriate to identify variability limits for each individual subject. Tan and Hitchings derived limits for change for individual ONHs based on the RA variability between each of the single topography images used to construct the mean topography image

[33].Limits of variability were calculated for 30° disc sectors from the standard deviation of all possible permutations of paired intra-visit (between single topographies) rim area differences. In the initial description of this technique, limits of variability were defined at p < 0.05, equivalent to a 95% confidence limit. In a longitudinal HRT image series, when the rim area value for a particular 30° sector exceeds the sector’s variability limits for that series, this was defined as “tentative progression”.

“Definite progression” required confirmation in at least two out of three consecutive tests, thereby accounting for spurious change or potential reversal on subsequent testing. This approach yielded a sensitivity of 85% and specificity (1 – false positive rate) of 95% when performed using HRT series acquired from 20 ocular hypertensive subjects demonstrating glaucomatous field conversion and 20 normal control subjects. The 95% statistical limit and two-of-three confirmatory criterion were subsequently shown to be optimal in terms of sensitivity and false-positive rate compared to alternative statistical limits (80%, 90%, 99%) and different confirmatory permutations (single test, two-of-two consecutive tests, three-of-three consecutive tests, two adjacent sectors in a single test, two adjacent sectors in two-of-three consecutive tests) yielding a sensitivity of 83.3% and a false positive rate of 3.1% [34].

More recently, Fayers et al. have defined criteria for rim area change according to ONH sector rim area repeatability coefficients for images acquired on different occasions by different individuals [13]. These repeatability coefficients were calculated using data derived from a test-retest study of HRT imaging [30]. Ninety-five percent of repeated measurement differences are within the value of the repeatability coefficient; larger differences are likely to be outside expected measurement error. As with the technique proposed by Tan previously [34], the specificity of this technique was improved by incorporating confirmatory testing, in particular a “two-of- three” strategy. Estimated specificity was also improved by requiring change to be confirmed in more than one HRT ONH sector. A unique feature of this event analysis is that image quality (as measured by mean pixel height standard deviation) is taken into consideration, with different repeatability coefficients applied to each sector according to the level of image quality (good, medium and poor quality).

4.2.4HRT Progression: Stereometric Parameter Trend Analyses

Artes and Chauhan have described a trend analysis in which a Spearman rank correlation is performed on a longitudinal series of ONH sector rim area values [2]. The statistical rationale for this approach is that the Spearman rank correlation identifies the likelihood that the slope generated from the sequence of observed sector rim area values over time occurred by chance in a random sequence. The significance values for the four HRT sectors were graded according to level of significance and

were summated to give an overall “evidence of change” score. In that particular study, the evidence of change score was used to allow an objective comparison between different tests: HRT, static automated perimetry and high-pass resolution perimetry.

A similar statistical approach (with similar results) was employed by Strouthidis et al., who assessed rim area change by performing a linear regression analysis of rim area over time [32]. The progression criteria (p-value of slope of rim area/time) were tailored according to the variability of the image series such that highly variable series required tighter criteria than less variable series. The technique is somewhat limited by the fact that the definition of image variability was based on the range of variability of the tested population—a cut-off at the 50th centile defined high or low variability. It is uncertain whether these cut-offs would be valid for a different population, and the technique has yet to be validated in an independent patient group. Linear regression of rim area over time is, however, easily applied and well understood, given that it parallels the technique of pointwise linear regression of visual field sensitivity over time.

4.2.5HRT Progression: Pixel-Based Technique

Statistical image mapping (SIM) is an established technique in the radiology milieu, being used for the analysis of three-dimensional images of the brain acquired using positron emission tomography and magnetic resonance imaging. The methodology has been applied to HRT image series, with promising results [27]. SIM estimates topographic change by the linear regression of the topographical height of each pixel within the disc over time. This generates a test statistic summarising the amount of change at each pixel. The sequence of images is then shuffled in a permutation analysis, and the test statistic is recalculated for each pixel; this step is repeated a number of times, using a unique reordering sequence on each occasion. A distribution of test statistics is therefore generated for each pixel. Significant change is identified by comparing the observed test statistic to the test statistical distribution for that pixel. A pixel is flagged as “active” if it exceeds the 95th percentile (p < 0.05). A global probability value for the entire image series is derived by comparing the largest cluster of active pixels in the observed image series to the permuted distribution of largest clusters. When applied to simulated and real longitudinal HRT data, SIM performed favourably compared to TCA in detecting change.

Summary for the Clinician

■As the HRT has been available for over a decade, and its hardware and software are backward compatible, it is possible to assess long temporal series of HRT images

■Assessment of HRT images over time has been shown to identify progression prior to visual field progression in some patients, although visual field progression may occur in the absence of detectable change in HRT images

■A number of HRT event and trend analyses have been described, although there is no clear consensus as to which technique is optimal for the detection of progression

4.3Detecting Progression by GDx-VCC

As previously stated, the majority of published data relating to longitudinal imaging in glaucoma has been obtained using the HRT. There is currently a paucity of longitudinal data for the GDx because of major alterations in the image acquisition technology (with the introduction of variable corneal compensation) and upgrades to the analysis software. As a consequence, historical images acquired prior to the introduction of variable corneal compensation cannot be analysed meaningfully using the most recent software editions. However, it is clear that the GDx-VCC has the potential to be a useful tool in the monitoring of structural progression because it has been proven to be a highly reproducible device, and the structural correlates assessed (principally nerve fibre layer polarizing properties) are known to alter in progressive glaucoma. In a recent study, longand short-term variability of GDx-VCC measurements were assessed in a cohort of glaucoma “suspects” known to have stable visual fields over a nine-year follow-up [21]. Long-term variability was slightly higher than short-term variability, but the long-term variability estimates for RNFL thickness parameters ranged from 3.21 to 4.97 µm and are sufficiently low for the nerve fibre indicator (4.93) to support the use of the GDx-VCC for the longitudinal assessment of disease progression.

The current software does not have statistically guided glaucoma progression algorithm. Despite this, it is possible to identify progressive nerve fibre layer loss “empirically” in serial GDx-VCC imaging (Fig. 4.3). At the time of writing, however, the introduction of a glaucoma progression analysis tool into the GDx-VCC software is planned

(personal communication, Dr Zhou, Carl Zeiss Meditec). The progression algorithm is intended to achieve an estimated specificity of at least 95% using either a “fast mode” algorithm or an “extended mode” algorithm. In the fast mode, a single scan per visit is required, and change is detected according to whether population-based

shortterm test-retest variability is exceeded. A “change from baseline” approach equivalent to that used in HRT TCA is used, whereby the difference between follow-up visit measurements and two baseline visit measurements are compared to test-retest variability. This is performed for RNFL parameters, the TSNIT (RNFL thickness in the

parapapillary measurement annulus) plot and for the RNFL image. In the extended mode, three repeat scans per visit are required and change is defined according to the individual’s own test-retest variability (estimated from the three scans at each imaging session). In addition to the “change from baseline” approach, which is applied to RNFL image data, the SIM technique (previously adapted for use with HRT images [27]) is applied to the RNFL parameters and to the TSNIT plot. By requiring change to be detected in at least one of the three structural correlates, it is hoped that both diffuse and focal structural change will be identified.

Summary for the Clinician

■In theory, the GDx-VCC should be a useful tool for monitoring progression as it generates repeatable and clinically meaningful measurements

■As the device has been subject to extensive redevelopment, there is minimal longitudinal data compared to the HRT

■At the time of writing, a statistically supported progression algorithm is due to be incorporated into the GDx-VCC software

4.4Detecting Progression by OCT

As with the GDx, the OCT is potentially useful for monitoring disease progression. However, there are currently few published longitudinal OCT studies, and a statistically guided progression algorithm has not yet been incorporated into the operational software. At present, it is possible to assess change by performing a serial RNFL analysis (Fig. 4.4), which allows the RNFL thinning over time to be evaluated.Wollsteinetal.havedefinedOCTprogressionas a reproducible thinning of the mean RNFL thickness of at least 20 µm, a value chosen on the basis of the known reproducibility error of the OCT [36]. It should be noted that this study utilised older OCT technology, which is known to have poorer test-retest variability than the newer STRATUSOCT.

As with the GDx-VCC, the lack of available longitudinal data for the OCT prevents any detailed discussion relating to its ability to monitor progression. In many respects, great potential in terms of the monitoring of progression may be expected given the excellent reproducibility and discriminatory power of the STRATUSOCT. However, it is the HRT, with its older and more slowly evolving technology, as well as backward-compatible software, which has the longevity necessary for researchers to be able to examine long series of ONH images over time.

Progression algorithms will be developed and tested as the newer technologies become more established. OCT is poised to become the pre-eminent ocular imaging technology; not least because of its versatility in imaging multiple anatomical sites: the macula, the optic nerve, the peripapillary nerve fibre layer and anterior segment structures (using a different wavelength source).

In recent months, a new generation of OCT device, “spectral-domain” OCT, has become commercially available. Spectral-domain OCT allows much faster image acquisition, which enables the measurement (in addition to RNFL parameters) of true optic nerve topography and optic disc cupping, although some movement artefact from axial motion is still a feature.

Summary for the Clinician

■As with the GDx-VCC, there is a paucity of longitudinal OCT data compared to the HRT

4.5Frequency of Testing

As with visual field testing in glaucoma, there is no consensus about the most appropriate frequency of testing that is required to enable optimal detection of progression. Owen and coworkers have performed a detailed examination of HRT rim area variability and identified that it was best characterised by a hyperbolic distribution, whereby the majority of rim area measurements are highly repeatable but with a few extreme deviations [25]. The estimates of rim area variability were used to construct computer simulations of disease progression. These computer simulations were used to try to identify the optimal frequency of testing required to identify realistic rates of progression. Detection of progression improved with increased frequency of testing, albeit at the cost of a steep decline in specificity. This latter shortcoming will only be addressed by identifying methods that can limit the number of false-positive tests. This may be best achieved by ensuring that only the highest quality image acquisitions are used. However, in practical terms, this may not always be possible. Glaucoma is largely prevalent in the elderly population, in whom lens opacity often coexists. In such patients it may be more appropriate to apply some form of post hoc image processing to ameliorate image variability. One such technique, “maximum likelihood deconvolution”, has been shown to improve the repeatability of topographical height measures, particularly in poor-quality images [28].

Summary for the Clinician

■Improved detection of progression occurs with increased frequency of testing, although this may be at the cost of declining specificity (higher false-positive rates)

■Post hoc techniques for improving image quality may improve specificity

4.6Lack of Concordance

A universal finding in the admittedly small number of published longitudinal imaging studies in glaucoma has been the surprising lack of concordance between identified structural and functional progression. A comparison of eventand trend-based progression techniques was performed in 84 glaucoma subjects and 41 normal

controls followed longitudinally using the HRT, standard achromatic perimetry and high-pass resolution perimetry. A poor agreement between the three test modalities as regards progression was identified regardless of the stringency of the progression criteria applied, with agreement varying from 4 to 19% in the glaucoma group [2]. A similar poor agreement was found by Strouthidis et al., who applied linear regression techniques to HRT rim area and visual field data acquired from ocular hypertensive subjects [32]. Agreement between subjects progressing by structure and function varied between 3 and 12%. More recently, the same group compared agreement between an HRT rim area trend analysis, an HRT rim area event analysis, a visual field trend analysis and a visual field event analysis applied to a cohort of 198 ocular hypertensive subjects followed longitudinally [13]. Agreement as regards progression across all four techniques was only 2%. This poor level of concordance has also been observed using the OCT, with only 3% of 64

glaucoma or glaucoma suspect eyes progressing by both OCT and by visual field [36].

In summary, measures of structural change and of functional change appear to identify similar numbers of progressing patients, but not necessarily the same patients. If one assumes that the tests have a high level of specificity, as reported in all of these published studies, then both tests are identifying genuine “progressors”. One should therefore use both tests in a complementary fashion to have the best chance of identifying progression.

The reasons for the discrepancy between structural and functional progression are uncertain. One may speculate that the causes relate to the test methodology, to physiology or to both. We are not yet at a stage when either imaging or functional testing in glaucoma has reached their apogee. One should therefore apply the caveat that there is poor concordance between structural and functional progression using currently available techniques. Both visual field testing and optic nerve imaging are prone to differing levels of measurement error, even within the same subject. It is therefore plausible that some of the disagreements may be explained by differences in measurement error between the two testing modalities.

It is clear that some structural changes in glaucoma, such as laminar bowing and connective tissue remodelling, do not necessarily result in functional loss. Likewise, IOP-dependent ganglion cell dysfunction may occur in the absence of structural change. Perhaps a more simple explanation relates to the fact that the published studies are relatively short in duration, being less than ten years. It is likely that the level of agreement between structure and function will increase as the length of follow-up increases.

Summary for the Clinician

■A poor agreement has been observed when comparing HRT and OCT progression with visual field progression

■In order to have the best chance of identifying progression in clinical practice, one needs to continue to monitor both visual field and structural changes

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