To determine the key optical coherence tomography (OCT) and OCT angiography (OCTA) parameters that correlate with visual field loss in optic disc drusen (ODD).
Retrospective cross-sectional study.
Single academic center. Seventeen patients with ODD (29 eyes) and 35 age-matched controls (53 eyes). Static perimetry, OCT, and OCTA imaging of optic disc and macula. Static perimetry, OCT, and OCTA measurements.
We investigated the relationship between static perimetry and 14 OCT/OCTA measurements in patients with ODD vs age-matched controls and found 5 key measurements that most correlated with visual field loss included: peripapillary retinal nerve fiber layer (RNFL), macular ganglion cell complex (GCC), peripapillary vessel area density (VAD), macular vessel diameter (VD), and flux. Hierarchical clustering of these 5 measurements vs all clinical characteristics revealed 3 distinct clusters. ODD and control eyes with no visual field loss (mean deviation [MD] > −2.0 dB) had high RNFL and GCC, and low macular VD and flux. ODD eyes with mild visual field loss (MD −2.0 to −5.0 dB) had high RNFL, GCC, and increased macular VD and flux. ODD eyes with moderate/severe visual field loss (MD < −5.0 dB) had decreased RNFL, GCC, peripapillary VAD, and increased macular VD and flux.
OCT and OCTA provided objective measurements that can help predict visual field loss in ODD. Our data suggest that increased macular flow may be an early biomarker of visual field loss in ODD, while decreased peripapillary vessel density and RNFL thickness are late biomarkers of visual field loss in ODD.
Optic disc drusen (ODD) is an optic neuropathy that is associated with visual field loss.
There are 5 OCT and OCTA measurements that most correlated with visual field loss.
Increased macular blood flow may be early biomarker of vision loss in ODD.
Decreased peripapillary vessel density may be late biomarker of vision loss in ODD.
Optic disc drusen (ODD) are semitranslucent multilobulated yellowish deposits at the optic disc. These deposits when on the surface can be seen on fundus color and autofluorescence photography, and when calcified (even if buried) can be seen on ophthalmic B-scan ultrasonography. , Pathologic studies of ODD have shown that variable size deposits containing calcium phosphate are found in the optic nerve head. , These deposits are thought to arise from abnormal axonal metabolism and may form from extruded extracellular mitochondria.
In children, ODD is often diagnosed during workup for suspected papilledema or visual field loss. In adults, ODD presents as visual field loss in a pattern consistent with optic neuropathy, such as nasal or altitudinal visual field loss.
Visual field loss in ODD is thought to arise from compression of the unmyelinated retinal ganglion cell axons and surrounding blood vessels by the ODD. Vascular compression or altered autoregulation also lead to increased risk of nonarteritic ischemic optic neuropathy, central retinal artery occlusion, central retinal vein occlusion, and peripapillary choroidal neovascularization.
Advances in noninvasive ophthalmic imaging have revolutionized the clinical diagnosis of ODD and may be the most easily obtained biomarker for predicting which ODD eyes will develop visual field loss. Optical coherence tomography (OCT) with enhanced depth imaging (EDI) can detect and quantify ODD and associated findings. OCT studies have shown that visual field loss in ODD is correlated with significantly thinned peripapillary retinal nerve fiber layer (RNFL) and macular ganglion cell complex (GCC). , Worse visual field is also associated with older age, presence of superficial ODD, and larger drusen volume. ,
Optical coherence tomography angiography (OCTA) is a promising new technology for visualizing macular and peripapillary microvasculature and quantification of changes in optic neuropathies, including vessel density, vessel length density, tortuosity, and flow. Case reports of focal microvascular attenuation corresponding to ODD were first published in 2017, , and 3 studies have focused on the changes of OCTA measurements compared with the controls as well as the correlation between OCTA changes and visual field loss. Despite these studies, we still do not know which OCT and OCTA measurements are the key parameters that can help predict visual field loss in ODD.
In this study, we performed structure-function analysis using paired static perimetry as well as macular and peripapillary OCT and OCTA measurements in ODD and age-matched controls in order to determine which parameters are most correlated with visual field loss. Identification of these key OCT and OCTA biomarkers can help diagnose the impact of ODD on retinal structures and potentially be used to predict the risk of vision loss in ODD.
We performed a retrospective cross-sectional study of patients with ODD who had been evaluated at the Byers Eye Institute at Stanford University Medical Center between January 2016 and April 2019. This study was approved by the Institutional Review Board of Stanford University and adhered to the Declaration of Helsinki and the Health Insurance Portability and Accountability Act.
Participants and Clinical Evaluation
In total, 35 controls (53 eyes) and 17 patients (29 eyes) with ODD were enrolled. All subjects had comprehensive ophthalmic examination and comprehensive measurements, including best corrected visual acuity (BCVA) using the Snellen chart to calculate the logarithm of reciprocal decimal visual acuity (LogMAR VA), refraction, intraocular pressure measurement, and ophthalmoscopy.
We recruited age- and sex-matched controls of age ≥ 18 years. The control subjects had BCVA of equal or better than LogMAR VA of 0.2 and normal optic nerves per fundus examination, no subjective visual field loss, and normal RNFL and GCC.
All ODD subjects had comprehensive neuro-ophthalmic examination by 1 investigator (Y.J.L.) and confirmation by color and autofluorescence funduscopic photography and OCT according to the International Optic Disc Drusen Studies Consortium. All ODD eyes had 1 or more hyporeflective center with hyperreflective margin on OCT. ODD that were visible by ophthalmoscopy were classified as superficial ODD, while ODD that were only detected by B-scan ultrasound or OCT were classified as buried ODD. ,
We excluded all subjects with history of optic neuropathies other than ODD and those with ophthalmic, neurologic, or systemic diseases that may impact measurements of peripapillary or macular OCT and OCTA. Subjects with unreliable visual field tests were excluded, which were defined as fixation loss > 20%, or false-positive or false-negative error rates > 20%. We also excluded ODD patients with vascular complications such as anterior ischemic optic neuropathy and central retinal artery or vein occlusion because they may affect visual function as well as OCT and OCTA measurements.
Visual Field Examination and Analysis
Automated static perimetry was performed using Humphrey Field Analyzer II 750 (SITA 24-2 programs, Swedish interactive threshold algorithm, Carl Zeiss Meditech, Inc., Dublin, CA, USA). The mean deviation (MD) and pattern standard deviation (PSD) were automatically calculated by the Humphrey field analyzer. The visual field patterns were categoried per previous studies: (a) normal visual field (without visual field defect), which had mean deviation (MD) less than −2.0 dB; and (b) with visual field defect , which had MD worse than −2.0 dB and must meet at least 1 of the 3 criteria: glaucoma hemifield test (GHT) outside normal limits; PSD with probability less than 5%; a cluster of 3 or more adjacent points on pattern deviation plot with probability less than 5%, one of which must have a probability level of at least 1%. ,
Spectral-domain OCT and OCTA Data Acquisition
OCT and OCTA images were acquired using Cirrus HD-OCT AngioPlex (Model 5000; Carl Zeiss Meditec, Jena, Germany). This machine utilizes light of 840 nm wavelength. The OCT scanner has maximum A-scan speed of 68,000 scans/sec with optical axial resolution of 5 μm and scanning depth of 2 mm. We performed the Optic Disc Cube 200 × 200 acquiring 200 horizontal scan lines each composed of 200 A-scans and Macular Cube 512 × 128 scans acquiring 128 horizontal scan lines each composed of 512 A-scans. The thickness of the RNFL was automatically measured in a circle with 3.46 mm diameter centered on the optic disc. The thicknesses of the macular GCC was measured in an elliptical annulus (vertical inner and outer radius of 0.5 mm and 2.0 mm, horizontal inner and outer radius of 0.6 and 2.4 mm, respectively) around the fovea, which consists of the combined thickness of the ganglion cell layer and inner plexiform layers. We also required a high resolution 5-line axial raster or radial scans through the optic disc, which helps to detect ODD.
For OCTA, we acquired 3 × 3 mm scans of the peripapillary and macular superficial capillary plexus. We automatically segmented en face OCTA images using optical microangiography (OMAG). The inner surface of superficial capillary plexus was defined by the internal limiting membrane (ILM). The outer surface of superficial capillary plexus was an approximation of inner plexiform layer (IPL), where IPL is estimated to be at 70% of the thickness between the ILM and the retinal pigment epithelium. All OCT and OCTA scans were performed by trained ophthalmic photographers. Only images with signal strength >7 were saved and used for analysis. Custom quantification software with an interactive interface was used to quantify vascular density and morphology in the peripapillary and macular OCTA images (MATLAB R2016a; MathWorks, Natick, MA, USA) based on modification of previous algorithm, which measures 6 vessel parameters provided distinct and biologically relevant information about microvasculature perfusion in the peripapillary and macular regions. Briefly, the algorithm transforms the original OCTA image into a binary vessel map using a 3-way combined method consisting of global thresholding, Hessian filter, and adaptive threshold. We removed large vessels from the peripapillary and macular OCTA images. Vessel skeleton map was developed by linearizing vessel signals into single-pixel width. Vessel perimeter map was created by outlining all vessels identified in the binarized image.
To standardize the region of interest, we used an annulus with an outer diameter of 2.75 mm and an inner diameter of 1.5 mm for the optic disc OCTA and an annulus with the same outer diameter and a smaller inner diameter of 1 mm for the macular OCTA. From the annulus, we calculated 6 measurements: (1) vessel area density (VAD) was the proportion of total sum area of white pixels (blood vessels) divided by the total area of all pixels in the binarized image; (2) vessel skeleton density (VSD) was the sum of white pixels divided by the sum of all pixels in the skeleton map; (3) vessel complexity index (VCI) was the square of the sum of pixels occupied by the vessel perimeter image divided by 4 times the sum of white pixels in the binarized image; (4) vessel perimeter index (VPI) was calculated using the vessel perimeter map as the ratio of the vessel perimeter to the total area of the OCTA image; (5) vessel diameter (VD) measured the averaged vessel caliber within the image; (6) flux measured the number of blood cells passing through a retinal vessel cross-sectional area per unit time. All OCTA data analysis was performed by one investigator to maximize consistency.
The data were analyzed by SPSS version 23.0 for Mac (SPSS Inc., Chicago, IL, USA) and R (R Foundation for Statistical Computing, Vienna, Austria). Quantitative continuous variables were presented as mean ± standard error or median (95% confidence interval). A 2-tailed Mann-Whitney U test was used to compare means of 2 groups of continuous variables. The frequencies of categorical variables were compared using chi-square test. Spearman correlation test was performed to determine the correlation between parameters. A value of P < .05 was considered statistically significant. Principal component analysis, Spearman correlation matrix, and hierarchical clustering were performed using custom R scripts. In the hierarchical clustering, each measurement has been centered and scaled to have standard deviation one in order to see differences between eyes. The PSD values of visual field were multiplied by −1 because they were negatively correlated with some of the other measurements.
ODD Eyes Had Significantly Worse Visual Field and Peripapillary OCT and OCTA
To identify key determinants of vision field defect in ODD, we conducted a cross-sectional study of 17 patients with ODD (29 eyes) and 35 controls (53 eyes). Representative ODD eyes with different severity of visual field loss, OCT, and OCTA images are shown in Figures 1 and 2 . There were 12 (71%) patients with bilateral ODD and 5 (29%) with unilateral ODD. Nineteen (66%) eyes had superficial ODD, and 10 (34%) eyes had buried ODD. There were no significant differences between the groups with regard to age and sex. Both controls and ODD eyes had good LogMAR VA ( P = .287). On static perimetry, the ODD eyes had significantly worse average MD than controls (controls: −0.36 ± 0.15 dB; ODD: −5.07 ± 1.15 dB, P < .0001). On OCT, ODD eyes had significantly decreased average peripapillary RNFL by 7.1 μm ( P = .006) and no difference in average macular GCC. On OCTA, ODD eyes had significantly lower peripapillary VAD (controls: 0.45 ± 0.00; ODD: 0.41 ± 0.01, P = .011) but not macular VAD compared with controls ( Table 1 ).
|Variable||Controls (n = 53 Eyes)||ODD (n = 29 Eyes)||P Value|
|Age a (y)||43.3 ± 2.0||37.9 ± 4.3||.208|
|Sex a , n (%)||.558|
|Female||19 (54.3)||11 (64.7)|
|Male||16 (45.7)||6 (35.3)|
|LogMAR||0.01 ± 0.01||0.02 ± 0.01||.287|
|VF MD (dB)||−0.36 ± 0.15 b||−5.07 ± 1.15 c||<.0001 d|
|VF PSD (dB)||1.44 ± 0.07 b||4.26 ± 0.71 c||.001 d|
|OCT average RNFL (μm)||93.1 ± 1.2||86.0 ± 4.9||.006 d|
|OCT average GCC (μm)||81.8 ± 0.8||77.7 ± 2.1||.244|
|OCTA optic disc VAD||0.45 ± 0.00||0.41 ± 0.01||.011 d|
|OCTA macula VAD||0.41 ± 0.00||0.41 ± 0.01||.182|