Ophthalmic Surgery, Lasers and Imaging Retina

Clinical Science 

Repeatability of Split-Spectrum Amplitude-Decorrelation Angiography to Assess Capillary Perfusion Density Within Optical Coherence Tomography

Felipe F. Conti, MD; Jason M. Young, MD; Fabiana Q. Silva, MD; Eduardo B. Rodrigues, MD; Rishi P. Singh, MD

Abstract

BACKGROUND AND OBJECTIVE:

To evaluate the repeatability of retinal thickness and vascular density measurements using split-spectrum amplitude-decorrelation angiography (SSADA) with optical coherence tomography (OCT).

PATIENTS AND METHODS:

Forty patients were divided into seven categories according to their diagnosis: no retinopathy (control), retinal vein occlusion, diabetes with no retinopathy, diabetes with retinopathy, non-exudative age-related macular degeneration (AMD), exudative AMD, and epiretinal membrane. Capillary density and retinal thickness measurements were taken and evaluated for reliability by determination of statistically significant differences and coefficient of variability (CoV) between measurements.

RESULTS:

No significant differences (P > .05) were found in any of the within-visit measurements. CoVs ranged from 0.26% to 52.76%, depending on the measure and the disease settings.

CONCLUSION:

The SSADA OCT angiography analysis has a low level of variability between measurements and, thus, is a reliable tool for evaluation of retinal perfusion.

[Ophthalmic Surg Lasers Imaging Retina. 2018;49:e9–e19.]

Abstract

BACKGROUND AND OBJECTIVE:

To evaluate the repeatability of retinal thickness and vascular density measurements using split-spectrum amplitude-decorrelation angiography (SSADA) with optical coherence tomography (OCT).

PATIENTS AND METHODS:

Forty patients were divided into seven categories according to their diagnosis: no retinopathy (control), retinal vein occlusion, diabetes with no retinopathy, diabetes with retinopathy, non-exudative age-related macular degeneration (AMD), exudative AMD, and epiretinal membrane. Capillary density and retinal thickness measurements were taken and evaluated for reliability by determination of statistically significant differences and coefficient of variability (CoV) between measurements.

RESULTS:

No significant differences (P > .05) were found in any of the within-visit measurements. CoVs ranged from 0.26% to 52.76%, depending on the measure and the disease settings.

CONCLUSION:

The SSADA OCT angiography analysis has a low level of variability between measurements and, thus, is a reliable tool for evaluation of retinal perfusion.

[Ophthalmic Surg Lasers Imaging Retina. 2018;49:e9–e19.]

Introduction

Introduced by Huang et al. in 1991, optical coherence tomography (OCT) is a noninvasive, cross-sectional imaging of biological systems.1 Since then, it has risen as an important imaging modality in several medical fields. In 2007, Kawana et al. reported for the first time images captured using a swept-source OCT (SS-OCT).2 This new technique offers higher imaging speeds, higher detection power, and provides the capability of a wider field of acquisition. These innovations made possible the development of a three-dimensional angiography algorithm that quantifies blood flow.3 The split-spectrum amplitude-decorrelation angiography (SSADA) algorithm distinguishes blood flow from immobile tissue using the theory that the signal amplitude returning from non-static tissue varies quickly over time in contrast to static tissue.4

In 2005, OCT angiography (OCTA) was approved by the U.S. Food and Drug Administration (FDA)5 for clinical use. Since then, it has been widely used in several areas, especially glaucoma and retinal diseases. Leveque et al. reported the reduction in capillary perfusion density (CPD) in patients with open-angle glaucoma compared with healthy individuals along with a decrease in retinal nerve fiber layer thickness associated with the decreases in vessel density.6 Kim et al., in a retrospective study of 98 patients with diabetes, reported microvascular changes in vessel morphology and density using OCTA.7 Lee et al., in a retrospective study of 83 eyes, correlated the low density of deep vascular plexus with the poor anti-vascular endothelial growth factor response in patients with diabetic macular edema.8 Agemy et al. used OCTA to create vascular perfusion density maps in patients with different stages of diabetic retinopathy (DR), finding an objective method to monitor the progression of the disease.4 Several other studies suggest OCTA has value as an accurate diagnostic device to visualize vascular abnormalities9 and foveal avascular zone (FAZ) remodeling as parameters to assess progressive changes in DR.10

Little is known about factors that may affect the repeatability of retinal thickness and vascular density measurements using OCTA. If investigators use the capillary density outputs from the SSADA algorithm, is there variability or “noise” from within the system that would prevent adequate comparisons over time? Few authors have reported on the matter. Spina et al. tested reproducibility and reliability of FAZ evaluation by OCTA after different vasoactive stimuli.11 Al-Sheikh et al. assessed the repeatability of OCTA-derived automated vascular density measures in the superficial and deep retinal layer showing good repeatability in healthy individuals.12 Herein, the purpose of this study is to assess various repeatability measures of the SSADA OCTA across a variety of retinal diseases.

En face optical coherence tomography image with foveal area demarcation (1.5 mm diameter circle), parafoveal area (2.5 mm diameter circle) and whole image (4.8 mm side square).

Figure 1.

En face optical coherence tomography image with foveal area demarcation (1.5 mm diameter circle), parafoveal area (2.5 mm diameter circle) and whole image (4.8 mm side square).

En face optical coherence tomography example of patient with diabetes with retinopathy. (A) 3 × 3 superficial capillaries area. (B) 3 × 3 deep capillaries area. (C) 6 × 6 superficial capillaries area. (D) 6 × 6 deep capillaries area. The star marks the irregularly increased foveal avascular zone area.

Figure 2.

En face optical coherence tomography example of patient with diabetes with retinopathy. (A) 3 × 3 superficial capillaries area. (B) 3 × 3 deep capillaries area. (C) 6 × 6 superficial capillaries area. (D) 6 × 6 deep capillaries area. The star marks the irregularly increased foveal avascular zone area.

En face optical coherence tomography example of patient with diabetes without retinopathy with no vascular abnormalities. (A) 3 × 3 superficial capillaries area. (B) 3 × 3 deep capillaries area. (C) 6 × 6 superficial capillaries area. (D) 6 × 6 deep capillaries area.

Figure 3.

En face optical coherence tomography example of patient with diabetes without retinopathy with no vascular abnormalities. (A) 3 × 3 superficial capillaries area. (B) 3 × 3 deep capillaries area. (C) 6 × 6 superficial capillaries area. (D) 6 × 6 deep capillaries area.

En face optical coherence tomography example of a patient with non-exudative age-related macular degeneration with no vascular abnormalities. (A) 3 × 3 superficial capillaries area. (B) 3 × 3 deep capillaries area. (C) 6 × 6 superficial capillaries area. (D) 6 × 6 deep capillaries area.

Figure 4.

En face optical coherence tomography example of a patient with non-exudative age-related macular degeneration with no vascular abnormalities. (A) 3 × 3 superficial capillaries area. (B) 3 × 3 deep capillaries area. (C) 6 × 6 superficial capillaries area. (D) 6 × 6 deep capillaries area.

En face optical coherence tomography example of a patient with epiretinal membrane (ERM). (A) 3 × 3 superficial capillaries area. (B) 3 × 3 deep capillaries area. (C) 6 × 6 superficial capillaries area. (D) 6 × 6 deep capillaries area. The star marks the irregular form of the foveal avascular zone, caused by the ERM vessel dragging in the retinal superficial layer.

Figure 5.

En face optical coherence tomography example of a patient with epiretinal membrane (ERM). (A) 3 × 3 superficial capillaries area. (B) 3 × 3 deep capillaries area. (C) 6 × 6 superficial capillaries area. (D) 6 × 6 deep capillaries area. The star marks the irregular form of the foveal avascular zone, caused by the ERM vessel dragging in the retinal superficial layer.

En face optical coherence tomography example of a patient without retinopathies. (A) 3 × 3 superficial capillaries area. (B) 3 × 3 deep capillaries area. (C) 6 × 6 superficial capillaries area. (D) 6 × 6 deep capillaries area.

Figure 6.

En face optical coherence tomography example of a patient without retinopathies. (A) 3 × 3 superficial capillaries area. (B) 3 × 3 deep capillaries area. (C) 6 × 6 superficial capillaries area. (D) 6 × 6 deep capillaries area.

En face optical coherence tomography example of a patient with retinal vein occlusion. (A) 3 × 3 superficial capillaries area. (B) 3 × 3 deep capillaries area. (C) 6 × 6 superficial capillaries area. (D) 6 × 6 deep capillaries area. The stars mark the areas of capillaries drop-out seen on the retinal superficial and deep layers.

Figure 7.

En face optical coherence tomography example of a patient with retinal vein occlusion. (A) 3 × 3 superficial capillaries area. (B) 3 × 3 deep capillaries area. (C) 6 × 6 superficial capillaries area. (D) 6 × 6 deep capillaries area. The stars mark the areas of capillaries drop-out seen on the retinal superficial and deep layers.

En face optical coherence tomography example of a patient with exudative age-related macular degeneration with no vascular abnormalities. (A) 3 × 3 superficial capillaries area. (B) 3 × 3 deep capillaries area. (C) 6 × 6 superficial capillaries area. (D) 6 × 6 deep capillaries area.

Figure 8.

En face optical coherence tomography example of a patient with exudative age-related macular degeneration with no vascular abnormalities. (A) 3 × 3 superficial capillaries area. (B) 3 × 3 deep capillaries area. (C) 6 × 6 superficial capillaries area. (D) 6 × 6 deep capillaries area.

Patients and Methods

Study Design

This retrospective study was performed at Cole Eye Institute, Cleveland, Ohio, after approval from the Cleveland Clinic investigational review board. All study-related procedures were performed in accordance with good clinical practice (International Conference on Harmonization of Technical Requirements of Pharmaceuticals for Human Use E6), applicable FDA regulations, and the Health Insurance Portability and Accountability Act. All subjects provided written informed consent for participation in the study.

Participants

A total of 40 patients (40 eyes) underwent OCTA imaging (Avanti RTVue XR; Optovue, Fremont, CA) during their ophthalmology visit at the Cleveland Clinic from July 2016 to January 2017. The demographic and clinical data including age, gender, and ocular history were collected. Patients with branch or ± 6.62%, and 57.22 ± 3.70%, respectively.

Statistical Outcomes

Repeatability analysis revealed no statistically significant differences (P > .05) between any of the measurement time points. In the scan area assessment, differences were found for WID of the superficial (P = .01) and the deep layers (P = .03) in the control group. Differences were also noticed in FVD of the superficial layer (P = .04) in the exudative AMD group and FVD of the deep layer (P = .01) in the epiretinal membrane (ERM) group (Table 2). CoVs ranged from 0.26% to 52.76%, depending on the measure and the disease setting (Table 3).

Demographics and Baseline Characteristics of Patients Enrolled

Table 1:

Demographics and Baseline Characteristics of Patients Enrolled

Statistical Analysis of Measurements in Each Diagnostic Category

Table 2:

Statistical Analysis of Measurements in Each Diagnostic Category

Coefficient of Variation

Table 3:

Coefficient of Variation

Discussion

The SSADA algorithm is used to compute the location of blood vessels based on blood flow. Although studies are addressing the usefulness of OCTA in various retinal pathologies,13 few studies have evaluated this algorithm for repeatability in the setting of retinal disease. In this study, the SSADA algorithm for OCTA in eyes with retinal pathology was evaluated, and the variability between recorded measurements was not statistically significant, confirming the reliable repeatability of the diagnostic test (P > .05).

An important aspect is the terminology used in measurement error studies. There are diverse distinct terms used to characterize measurement error, such as reproducibility, repeatability, and CoV. Reproducibility indicates the variation in measurements made on a subject under changing circumstances.14 The changing circumstances may be due to different measurement techniques or devices, or even being made by different investigators or raters. Repeatability is the capability of a test to consistently repeat the same measurement from the same part, using the same meter, under the same conditions.14 The CoV is a standardized measure of dispersion that allows comparing data sets even if the means are drastically different from one another.14 It is calculated dividing the standard deviation by the mean of an expected variable, and it describes the amount of variability relative to the mean. A reference interval for the CoV is not defined because the analysis is done individually, with an independent comparison between each measurement/test and control.

Recent studies examining repeatability have primarily focused on the FAZ in healthy subjects. In a prospective study of 60 healthy eyes by Carpineto et al., two observers imaged each eye at three time points with SSADA OCTA.15 They found concordance correlation coefficients ranging from 0.994 to 0.999 between observers and variability for the FAZ area of 0.015 mm2 and 0.013 mm2, meaning that there was a low discrepancy between the same time points for each observer. Magrath et al. conducted a similar prospective case series looking at the FAZ in 25 healthy patients and found CoVs corresponding to a low degree of deviation between images taken with the modality.16 Using SSADA OCTA, Hwang et. al reported CoV and repeatability coefficients indicative of high repeatability in a study of 12 patients with diabetes; however, other various retinal diseases were not evaluated within this study.17

In the current study, repeatability was assessed by two different methods. First, significant differences were determined between within-visit measurements and scan acquisition sizes. Calculation of the CoV was the second method of repeatability evaluation chosen. Across all diseases and all measurements, the only instances of a significant difference were between different size scans in both superficial and deep WID of control group. Significant differences were also found in the superficial FVD of exudative AMD patients and between the deep FVD of patients with ERM. These findings could be explained by the difference between resolution in 3 mm × 3 mm and 6 mm × 6 mm images. Nevertheless, these P values are raw and do not take into account the multiple comparisons that have been made within diagnosis for each outcome. When adjustments were made for multiple comparisons, these P values were not significant. It is important to notice that, even though not significant, greater variability between measurements was reported in patients with central subfield thickness greater than 250 μm. This could be explained by automated segmentation algorithm errors in subjects with marked anatomical alterations in the macular morphology as atrophy or severe retinal edema. Furthermore, lower repeatability could also be seen in patients with signal intensity lower than 60, hypothesizing that media opacity that affects eyes of sight-impaired patients may be another determinant factor in decreasing the software repeatability of automatic segmentation scans.

A strength of this study is the patient population. Subjects were randomly selected and pictures were taken at routine clinic visits, meaning that these results may have greater generalizability as opposed to a structured clinical trial. A major limitation of the study was the failure to control for severity of each disease, making the groups less homogeneous. Larger studies with strict inclusion criteria targeted toward specific diseases may provide further understanding of the reliability of the SSADA algorithm.

In summary, our analysis demonstrated that the SSADA OCTA algorithm is a reliable tool in the evaluation of some retinal disease such as RVO, DR, AMD, and ERM. The machine performed consistently in all conditions tested, proving to be a powerful diagnostic tool for disease detection and progression in this small spectrum of retinal pathologies. However, variation still exists within the diseases themselves, and strict inclusion criteria should be considered when using this tool within large prospective studies.

References

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Demographics and Baseline Characteristics of Patients Enrolled

Diagnostic Category Males Females Age (Years) Avg Signal Intensity Avg Foveal Thickness (μm) Avg Parafoveal Thickness (μm)
All 19 21 69.7 ± 8.7 60.29 284.25 318.40
No Retinopathy 2 6 70.8 ± 9.9 60.79 265.88 319.54
RVO 5 0 67.4 ± 7.9 57.93 351.33 360.93
Diabetes with retinopathy 4 7 67.3 ± 6.1 59.85 295.24 352.12
Diabetes without retinopathy 0 3 67.3 ± 13.4 58.78 229.89 289.00
Non-exudative AMD 2 4 66.0 ± 8.7 66.33 239.83 292.72
Exudative AMD 4 1 79.4 ± 6.9 56.27 265.13 289.33
Epiretinal Membrane 2 0 74.5 ± 4.9 60.83 392.17 364.33

Statistical Analysis of Measurements in Each Diagnostic Category

Diagnostic Category P Values (Time) P Values (Size)

Without Retinopathies (n = 8) Superficial Deep Superficial Deep
  Foveal thickness .96 .91
  Parafoveal thickness .97 .97
  Whole image density .97 .96 .01 .03
  Foveal vascular density .81 .95 .30 .21
  Parafoveal vascular density .82 .99 .42 .91

RVO (n = 5)
  Foveal thickness 1.00 .91
  Parafoveal thickness 1.00 .97
  Whole image density .94 .98 .59 .91
  Foveal vascular density .88 .39 .65 .72
  Parafoveal vascular density .93 .97 .25 .58

Diabetes With Retinopathy (n = 11)
  Foveal thickness 1.00 .91
  Parafoveal thickness .97 .97
  Whole image density .84 .81 .33 .50
  Foveal vascular density .99 .93 .80 .70
  Parafoveal vascular density .88 .82 .66 .96

Diabetes Without Retinopathy (n = 3)
  Foveal thickness .99 .91
  Parafoveal thickness .98 .97
  Whole image density .90 .89 .09 .35
  Foveal vascular density .87 .99 .44 .56
  Parafoveal vascular density .71 .92 .79 .95

Non-Exudative AMD (n = 6)
  Foveal thickness 1.00 .91
  Parafoveal thickness .88 .97
  Whole image density .83 .63 .21 .78
  Foveal vascular density .99 .95 .34 .14
  Parafoveal vascular density .42 .77 .46 .52

Exudative AMD (n = 5)
  Foveal thickness .98 .91
  Parafoveal thickness .88 .97
  Whole image density .38 .99 .61 .98
  Foveal vascular density .93 .59 .04 .17
  Parafoveal vascular density .52 .98 .51 .66

Epiretinal Membrane (n = 2)
  Foveal thickness 1.00 .91
  Parafoveal thickness .99 .97
  Whole image density .45 .79 .67 .83
  Foveal vascular density .99 .78 .15 .01
  Parafoveal vascular density .60 .47 .61 .21

Coefficient of Variation

Whole Image Superficial Fovea Density Superficial Parafoveal Density Superficial Whole Image Deep Fovea Density Deep Parafoveal Density Deep
CoV Control 3 mm × 3 mm 1.03 – 11.62% 2.43 – 11.10% 1.09 – 11.4% 3.48 – 5.56% 0.66 – 13.49% 1.41 – 4.91%
CoV Control 6 mm × 6 mm 2.31 – 7.84% 6.29 – 11.51% 2.10 – 7.39% 0.92 – 7.22% 2.11 – 15.28% 0.26 – 10.48%
CoV RVO 3 mm × 3 mm 0.50 – 6.79% 2.98 – 12.95% 1.05 – 6.22% 2.31 – 7.63% 5.22 – 36.55% 0.97 – 6.20%
CoV RVO 6 mm × 6 mm 1.65 – 8.88% 3.97 – 12.96% 2.23 – 6.95% 1.12 – 8.27% 0.91 – 33.26% 1.10 – 7.35%
CoV AMD 3 mm × 3 mm 1.43 – 10.27% 1.62 – 9.08% 1.19 – 9.59% 0.84 – 2.23% 0.51 – 8.64% 0.97 – 4.99%
CoV AMD 6 mm × 6 mm 1.08 – 5.34% 1.98 – 9.44% 1.54 – 31.71% 1.17 – 6.77% 3.51 – 10.29% 1.14 – 4.62%
CoV nAMD 3 mm × 3 mm 1.61 – 8.65% 4.57 – 42.38% 3.18 – 9.50% 0.78 – 4.63% 3.67 – 52.76% 1.29 – 4.42%
CoV nAMD 6 mm × 6 mm 1.42 – 8.27% 5.57 – 11.93% 1.01 – 9.36% 1.04 – 7.86% 6.72 – 22.46% 1.54 – 7.49%
CoV Diabetes Without Retinopathy 3 mm × 3 mm 5.57 – 14.37% 2.38 –37.67% 10.71 – 15.23% 3.81 – 17.21% 3.43 – 51.37% 1.71 – 17.58%
CoV Diabetes Without Retinopathy 6 mm × 6 mm 4.75 – 10.26% 2.19 – 8.37% 1.30 – 14.04% 0.70 – 10.00% 8.00 – 19.74% 2.45 – 6.09%
CoV Diabetes With Retinopathy 3 mm × 3 mm 0.97 – 8.72% 0.77 – 15.34% 1.82 – 23.71% 0.74 – 8.31% 5.66 – 21.18% 0.41 – 8.89%
CoV Diabetes With Retinopathy 6 mm × 6 mm 2.80 – 7.50% 0.41 – 23.98% 1.62 – 9.08% 1.33 – 10.12% 0.79 – 46.59% 0.67 – 13.33%
CoV ERM 3 mm × 3 mm 3.48 – 7.49% 2.80 – 8.46% 4.56 – 8.80% 2.19 – 3.39% 5.22 – 9.08 1.93 – 2.18%
CoV ERM 6 mm × 6 mm 1.28 – 3.47% 2.81 – 7.42% 2.06 – 7.28% 1.52 – 3.18% 1.73 – 6.31% 2.79 – 5.16%
Authors

From Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio (FFC, JMY, FQS, RPS); and the Department of Ophthalmology, Federal University of São Paulo, São Paulo, Brazil (FFC, EBR).

This study was accepted for presentation at the Association for Research in Vision and Ophthalmology annual meeting, May 7–11, 2016, in Baltimore. The authors received a waiver for publication in journals other than Investigative Ophthalmology & Visual Science.

Dr. Singh reports grants and personal fees from Regeneron and Genentech, personal fees from Shire, and grants from Apellis and Alcon during the conduct of the study. The remaining authors report no relevant financial disclosures.

Address correspondence to Rishi P. Singh, MD, 9500 Euclid Avenue, Desk i32, Cleveland, OH 44195; email: singhr@ccf.org.

Received: August 16, 2017
Accepted: January 22, 2018

10.3928/23258160-20180907-02

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