Advertisement

PentaVision

Ophthalmology Management

Article

Glaucoma progression detection, part 2

Optical coherence tomography provides a reliable, objective measure.

Ophthalmology Management November 1, 2018

Glaucoma is the leading cause of irreversible blindness worldwide.1 The key to managing the disease is to lower IOP, which halts damage to the optic nerve and visual loss. In part one in my series on detecting glaucoma progression, I covered the visual field testing aspect (http://bit.ly/2xOsuXR ) of this mission. But, due to the variability of visual field testing2 and subjective optic nerve head examination,3 more objective measures such as optical coherence tomography (OCT) are now standard of care.

OCT can measure the peripapillary retinal nerve fiber layer (RNFL), the macular ganglion cell thickness and the optic nerve head. The RNFL is measured in a scan circle surrounding the optic nerve and was the first OCT parameter developed for glaucoma analysis. The optic nerve head analysis can evaluate disc morphology and parameters such as rim volume, disc area and cup-to-disc ratio. The macular ganglion cell layer is analyzed via the ganglion cell complex (GCC) parameter. This includes the RNFL, the ganglion cell layer and the macular region’s inner plexiform layer. These parameters are strongly correlated with glaucoma disease stages4-6 and have been used to accurately diagnose glaucoma,7-15 as well as predict conversion of pre-glaucoma to glaucoma16 and visual field worsening.17,18

In this article, we will discuss the key points in using OCT to detect glaucoma progression: where to look for progression, scan quality, “floor” effect, age-related loss, intervisit reproducibility, event- and trend-based progression analyses as well as guided progression analysis.

WHERE TO LOOK

OCT RNFL progression falls into three main patterns:

  1. Widening of an existing RNFL defect, which is best seen on the RNFL 12-clock-hour pie chart.
  2. Deepening without widening of an existing RNFL defect, which is also best seen on the clock hour or temporal superior nasal inferior temporal (TSNIT) graph.
  3. Development of a new RNFL defect, which can be seen on the quadrant or clock hour or TSNIT overlay. The RNFL thickness deviation map is also a good place to identify a new defect.

A longitudinal study of factors predicting conversion of glaucoma suspects to glaucoma, as well as progression of glaucoma, found that the best predictors were measures of focal defects.16 For conversion of glaucoma suspects to glaucoma, focal loss volume of the peripapillary RNFL and of the macular GCC were the best predictors of future change. For subjects with glaucoma, the same factors were identified as having the highest predictive value for progression.

These data indicate that focal parameters are better at predicting and detecting progression than more global parameters such as overall RNFL or overall GCC thickness.

SCAN QUALITY

To reliably detect glaucoma progression, the clinician needs to ensure good scan quality on baseline and subsequent visits. The signal strength should be 7 out of 10 or greater; suggestions for improving this are dilation, artificial tears, control of movement or blinking artifacts and scan alignment. Any opacities such as vitreous floaters cause loss of signal, and peripapillary atrophy or retinal scarring in the scan circle severely decreases scanning ability and results in artificially lower RNFL measurements. If one area is affected, place more emphasis on measurements from another area (ie, with severe peripapillary atrophy, consider using macular GCC over RNFL).

The scan must be centered over the optic nerve for RNFL, or it will result in spurious measurements. The overall RNFL thickness may be the same, but the part of the scan circle that is closer to the optic nerve measures thicker and the part that is further away thinner, showing apparent focal defects (Figure 1). Missing data and segmentation errors also give false positive results (Figure 2).

Figure 1. Centration error on OCT: This peripapillary RNFL scan shows a decentered scan in the top image with apparent focal thinning temporally (the part furthest away from the optic nerve head). When the scan circle is centered in the bottom image, this abnormality goes away (Zeiss Cirrus OCT).
Figure 1. Centration error on OCT: This peripapillary RNFL scan shows a decentered scan in the top image with apparent focal thinning temporally (the part furthest away from the optic nerve head). When the scan circle is centered in the bottom image, this abnormality goes away (Zeiss Cirrus OCT).
Figure 2. Segmentation error on OCT: This peripapillary RNFL scan shows a normal scan in the right eye with good quality and a poor quality scan in the left eye with abnormal RNFL measurement. Note the poor scan signal (3/10) and error in segmentation in the RNFL circular tomogram (Zeiss Cirrus OCT).
Figure 2. Segmentation error on OCT: This peripapillary RNFL scan shows a normal scan in the right eye with good quality and a poor quality scan in the left eye with abnormal RNFL measurement. Note the poor scan signal (3/10) and error in segmentation in the RNFL circular tomogram (Zeiss Cirrus OCT).

THE RIGHT TEST FOR THE RIGHT PATIENT: WHAT IS THE FLOOR EFFECT?

It is generally believed that structural testing is most helpful in earlier stages of glaucoma and visual field testing in the more advanced stages.19-21 This is due to the fact that the RNFL bottoms out in very advanced glaucoma and will no longer show progression after this late stage has been reached: the so-called “floor” effect. The RNFL rarely shows progressive thinning beyond 40-45 μm, as only residual non-neural tissue remains at this stage.22 Therefore, once this stage has been reached, the OCT RNFL is not helpful for progression detection (Figure 3, page 36).

Figure 3. “Floor” effect: On the left, OCT shows a patient with advanced glaucomatous optic nerve damage with a very thin RNFL who may not show progressive thinning from this point on. The macula OCT on the right shows the ganglion cell complex of the macula, which may still be useful to detect progression in this same advanced patient (Zeiss Cirrus OCT).
Figure 3. “Floor” effect: On the left, OCT shows a patient with advanced glaucomatous optic nerve damage with a very thin RNFL who may not show progressive thinning from this point on. The macula OCT on the right shows the ganglion cell complex of the macula, which may still be useful to detect progression in this same advanced patient (Zeiss Cirrus OCT).

However, our results from the Advanced Imaging in Glaucoma Study (AIGS), a longitudinal study of imaging and visual fields in patients ranging from glaucoma suspects to advanced glaucoma, support OCT macula GCC as a useful tool even in the later stages of glaucoma, with similar ability to detect progression as visual fields.22

AGE-RELATED LOSS

To detect progressive RNFL thinning, we must adjust for age-related loss. Many studies, both cross-sectional and longitudinal, have estimated the thinning of RNFL due to aging. Leung et al observed a cross-sectional age difference of -0.33 μm per year and a longitudinal thinning of -0.52 μm per year.23 The AIGS reported a cross-sectional difference of -0.21 μm per year for RNFL and -0.17 μm for GCC; additionally, it found a longitudinal change of -0.14 μm per year for RNFL and -0.25 μm for GCC.24

The software programs for progression take this into account, but one must remember to manually adjust for age-related loss if comparing OCTs years apart.

REPEATABILITY AND REPRODUCIBILITY OF OCT

In order to detect change, one must take into consideration the accuracy of the testing parameter. The intervisit reproducibility of RNFL measurements with OCT is approximately 4 μm.25 Therefore, any changes in thickness must exceed this level to be significant. A more conservative cutoff would be an 8-10 μm change, which is twice the maximum standard deviation.

Be wary of false progression from one test to another that can be caused by artifacts (Figure 4, page 36).

Figure 4. False progression caused by artifact: Patient had apparent progressive thinning of the inferior RNFL progressing to 50 μm on the recent test (left) and 63 μm on the previous test (right). This is attributable to the scan circle superimposed on a vitreous floater, giving a 0-μm measurement in the area visualized on the RNFL thickness TSNIT map and RNFL circular tomogram (Zeiss Cirrus OCT).
Figure 4. False progression caused by artifact: Patient had apparent progressive thinning of the inferior RNFL progressing to 50 μm on the recent test (left) and 63 μm on the previous test (right). This is attributable to the scan circle superimposed on a vitreous floater, giving a 0-μm measurement in the area visualized on the RNFL thickness TSNIT map and RNFL circular tomogram (Zeiss Cirrus OCT).

EVENT- VS. TREND-BASED ANALYSIS

These concepts are based on analysis methods of visual fields and refer to how the threshold for progression is defined.

Event-based analysis defines progression as a follow-up measurement that exceeds a pre-established threshold for change from baseline. For example, if a particular measurement of overall RNFL mean thickness or superior or inferior quadrant shows a repeatable reduction of 8 μm or more, this represents progression. We have chosen this value as it is twice the resolution measurement error (4 μm) of the OCT.

This type of analysis is easy to perform, can be used to compare tests from different machines and can detect progression in fewer tests. It is important not to compare measurements from different generations of OCT, such as time-domain and spectral-domain OCT, or from different manufacturers. The limitations of this type of analysis are that it is susceptible to outliers and may falsely identify progression (“false positive”).

Trend-based analysis monitors the change over time of a particular measurement or group of measurements using statistical regression analysis. This rate of change is compared to a normative database of stable individuals and given a value of statistical significance to see if it represents an actual change. The major disadvantage of this method is that it requires a larger number of tests to determine whether a change is significant.

GUIDED PROGRESSION ANALYSIS

Guided Progression Analysis (GPA) is a program designed to analyze for development of RNFL (Figure 5). It analyzes the RNFL thickness of individual clusters of OCT A-scans, comparing results from baseline and follow-up RNFL thickness maps to create an estimated measure of test-retest variability. Two baseline scans are used, and any pixels exceeding the test-retest variability are highlighted as yellow for the first event and red if the same changes are seen on three consecutive tests. Therefore, three follow-up tests are required to detect a statistically significant change.

Figure 5. Guided Progression Analysis of OCT RNFL: Most recent exam is compared to two baseline tests in a trend analysis for progression. The inferior RNFL thickness rate of change reaches a statistically significant level and is highlighted on the graph and the RNFL Thickness Profile. The area of change is plotted on the RNFL deviation map (Zeiss Cirrus OCT).
Figure 5. Guided Progression Analysis of OCT RNFL: Most recent exam is compared to two baseline tests in a trend analysis for progression. The inferior RNFL thickness rate of change reaches a statistically significant level and is highlighted on the graph and the RNFL Thickness Profile. The area of change is plotted on the RNFL deviation map (Zeiss Cirrus OCT).

The RNFL deviation map is used to show the changes mapped over time. It is important to reset a new baseline if progression has been determined to occur. Otherwise, future progression cannot be detected, as comparison of future tests to the original baseline tests will always show a difference.

THE FUTURE OF STRUCTURAL IMAGING

The future of structural imaging in glaucoma progression will evolve along several lines. The first is the refinement of accuracy and test-retest variability. As tests become more accurate and repeatable, progression will be detectable at earlier time intervals.

The second is the combination of the different OCT parameters such as RNFL, GCC and optic nerve head into indices that have greater accuracy for predication than any single parameter. Newer parameters, such as Bruch’s membrane and lamina cribrosa measurements and OCT angiography, will be validated and then added to the mix.

Finally, OCT may be combined with other patient parameters such as age, central corneal thickness and functional testing to give a composite index for progression.18

CONCLUSIONS

Structural imaging provides indicators of glaucomatous optic nerve damage and progression that complement those from functional visual field testing. Structural changes may help predict visual loss and guide clinical treatment. One must ensure that scans are of sufficient quality while also taking into account age-related loss and test reproducibility. Event-based analysis can be used as a quick and simple determinant of progression, but trend-based analysis programs are more powerful and accurate. As always, structural imaging obtained by OCT must always be taken into context with the clinical exam, functional testing and other individual patient characteristics. OM

REFERENCES

  1. Quigley HA, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol. 2006;90:262-267.
  2. Chauhan BC, Garway-Heath DF, Goni FJ, et al. Practical recommendations for measuring rates of visual field change in glaucoma. Br J Ophthalmol. 2008;92:569-573.
  3. Jampel HD, Friedman D, Quigley H, et al. Agreement among glaucoma specialists in assessing progressive disc changes from photographs in open-angle glaucoma patients. Am J Ophthalmol. 2009;147:39-44 e1.
  4. Huang D, Swanson EA, Lin CP, et al. Optical coherence tomography. Science. 1991;254:1178-1181.
  5. Tan O, Li G, Lu AT, Varma R, Huang D. Mapping of macular substructures with optical coherence tomography for glaucoma diagnosis. Ophthalmology. 2008;115:949-956.
  6. Tan O, Chopra V, Lu AT, et al. Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography. Ophthalmology. 2009;116:2305-14 e1-2.
  7. Bowd C, Zangwill LM, Berry CC, et al. Detecting early glaucoma by assessment of retinal nerve fiber layer thickness and visual function. Invest Ophthalmol Vis Sci. 2001;42:1993-2003.
  8. Zangwill LM, Bowd C, Berry CC, et al. Discriminating between normal and glaucomatous eyes using the Heidelberg Retina Tomograph, GDx Nerve Fiber Analyzer, and Optical Coherence Tomograph. Arch Ophthalmol. 2001;119:985-993.
  9. Loewen NA, Zhang X, Tan O, et al. Combining measurements from three anatomical areas for glaucoma diagnosis using Fourier-domain optical coherence tomography. Br J Ophthalmol. 2015.
  10. Mwanza JC, Oakley JD, Budenz DL, Anderson DR. Ability of cirrus HD-OCT optic nerve head parameters to discriminate normal from glaucomatous eyes. Ophthalmology. 2011;118:241-248 e1.
  11. Chang RT, Knight OJ, Feuer WJ, Budenz DL. Sensitivity and specificity of time-domain versus spectral-domain optical coherence tomography in diagnosing early to moderate glaucoma. Ophthalmology. 2009;116:2294-2299.
  12. Medeiros FA, Zangwill LM, Bowd C, Vessani RM, Susanna R, Jr., Weinreb RN. Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography. Am J Ophthalmol. 2005;139:44-55.
  13. Schuman JS, Hee MR, Arya AV, et al. Optical coherence tomography: a new tool for glaucoma diagnosis. Curr Opin Ophthalmol. 1995;6:89-95.
  14. Hood DC, Raza AS, Kay KY, et al. A comparison of retinal nerve fiber layer (RNFL) thickness obtained with frequency and time domain optical coherence tomography (OCT). Opt Express. 2009;17:3997-4003.
  15. Huang JY, Pekmezci M, Mesiwala N, Kao A, Lin S. Diagnostic power of optic disc morphology, peripapillary retinal nerve fiber layer thickness, and macular inner retinal layer thickness in glaucoma diagnosis with fourier-domain optical coherence tomography. J Glaucoma. 2011;20:87-94.
  16. Zhang X, Loewen N, Tan O, et al. Predicting Development of Glaucomatous Visual Field Conversion Using Baseline Fourier-Domain Optical Coherence Tomography. American Journal of Ophthalmology;163:29-37.
  17. Sehi M, Bhardwaj N, Chung YS, Greenfield DS. Evaluation of baseline structural factors for predicting glaucomatous visual-field progression using optical coherence tomography, scanning laser polarimetry and confocal scanning laser ophthalmoscopy. Eye (Lond). 2012;26:1527-1535.
  18. Zhang X, Dastiridou A, Francis BA, et al. Baseline Fourier-Domain Optical Coherence Tomography Structural Risk Factors for Visual Field Progression in the Advanced Imaging for Glaucoma Study. Am J Ophthalmol. 2016;172:94-103.
  19. Sommer A, Katz J, Quigley HA, et al. Clinically detectable nerve fiber atrophy precedes the onset of glaucomatous field loss. Arch Ophthalmol. 1991;109:77-83.
  20. Abe RY, Diniz-Filho A, Zangwill LM, et al. The Relative Odds of Progressing by Structural and Functional Tests in Glaucoma. Invest Ophthalmol Vis Sci. 2016;57:OCT421-428.
  21. Ohnell H, Heijl A, Brenner L, Anderson H, Bengtsson B. Structural and functional progression in the early manifest glaucoma trial. Ophthalmology. 2016;123:1173-1180.
  22. Zhang X, Dastiridou A, Francis BA, et al. AIGS Group. Comparison of glaucoma progression detection by optical coherence tomography and visual field. Am J Ophthalmol. 2017;184:63-74.
  23. Leung CK, Yu M, Weinreb RN, et al. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: a prospective analysis of age-related loss. Ophthalmology. 2012;119:731-737.
  24. Zhang X, Francis BA, Dastiridou A, et al. AIGS Group Longitudinal and cross-sectional analyses of age effects on retinal nerve fiber layer and ganglion cell complex thickness by Fourier-domain OCT. Translational Vision Science and Technology. 2016 Mar 4;5(2):1.
  25. Mwanza JC, Chang RT, Budenz DL, et al. Reproducibility of peripapillary retinal nerve fiber layer thickness and optic nerve head parameters measured with cirrus HD-OCT in glaucomatous eyes. Invest Ophthalmol Vis Sci. 2010;51:5724-5730.

About the Authors

Brian A. Francis, MD, MS, is professor of Ophthalmology, Rupert and Gertrude Stieger Endowed Chair in Vision Research at Doheny Eye Institute and Stein Eye Institute at UCLA’s David Geffen School of Medicine.

Brian A. Francis, MD, MS, is professor of Ophthalmology, Rupert and Gertrude Stieger Endowed Chair in Vision Research at Doheny Eye Institute and Stein Eye Institute at UCLA’s David Geffen School of Medicine.

Vikas Chopra, MD, is associate professor of Ophthalmology, Charles and Hildegard Warren Endowed Research Chair at Doheny Eye Institute and Stein Eye Institute at UCLA’s David Geffen School of Medicine.

Vikas Chopra, MD, is associate professor of Ophthalmology, Charles and Hildegard Warren Endowed Research Chair at Doheny Eye Institute and Stein Eye Institute at UCLA’s David Geffen School of Medicine.

Disclosures: Dr. Francis is a consultant for BVI/Endo Optiks, Inc.

What to Read Next