47 eyes of 47 patients with unilateral BRVO who were treated at the Department of Ophthalmology of Peking Union Medical College Hospital (PUMCH), Beijing, China between January 2018 and December 2018 were enrolled in this retrospective observational study. Treatment naïve patients and patients who have been treated with intravitreal medication were included. Patients with poor OCTA images quality (quality index below 5) because of poor eye fixation, media opacities were excluded. Subjects with retinal surgery history, pathologic myopia, ocular trauma, retinal artery occlusion, and other concomitant ocular diseases such as age-related macular degeneration, glaucoma and diabetic retinopathy were also excluded. The exclusion criteria also included eyes with CRVO or hemicentral retinal vein occlusion (HRVO).
One of the control groups consisted of the clinically unaffected fellow eyes of the enrolled subjects if the fellow eye was otherwise healthy without any known ocular diseases or ocular surgical operation. A normal anterior and posterior segment on examination of the eye and a normal intraocular pressure were required. Another control group was made up with 47 age-matched normal individuals who had no history of any ocular diseases or ocular surgical operation. The ophthalmic examination of the normal individuals was unremarkable. One eye of each normal individual was selected randomly as the control eye.
Information including age, sex, duration of disease and best corrected visual acuity (BCVA, logMAR) were obtained from the medical records. Each patients underwent a careful examination including slit lamp-assisted biomicroscopy, intraocular pressure, fluorescein angiography (FA), SD-OCT (Spectralis Heidelberg Engineering, Heidelberg, Germany), and OCTA using the AngioVue OCTA system version 2017.1 (Optovue Inc., Fremont, CA, USA).
Macular microvascular OCTA imaging
Macular OCT angiograms images were captured using the AngioVue OCTA system version 2017.1 (Optovue Inc., Fremont, CA, USA) with the Angio Retina mode. A newly developed 3D projection artifact removal (PAR) algorithm was included in the program. In situ OCTA signal was differentiated from projection artifacts by the software based on the information from OCT and OCTA volume and removes the projection artifacts.
For each eye, a voxel image with a side of 3mm centered on the fovea was chosen for analyze. The scanned vascularized tissue was automatically segmented into four enface slabs by the installed Angiovue software based on the default settings: the superficial vascular complex (SVC), the deep vascular complex (DVC), the outer retinal layer, and the choriocapillaris layer. Vascular tissue from the internal limiting membrane to 10 μm above the inner plexiform layer (IPL) consisted of the superficial capillary network. The deep capillary network was defined as vasculature from10 μm above the IPL to 10 μm below the outer plexiform layer (OPL)，no overlap existed between the 2 slabs.
FAZ metrics including size, perimeter, foveal acircularity index(AI), and foveal vessel density 300(FD-300) were evaluated with the software(Table 3). AI is defined as the ratio between the measured perimeter and the perimeter of a circular area of the same size: the closer the shape is to the circle, the closer the value is to 1. FD-300 is the vessel density in a 300-mm wide area encompassing the FAZ, including both SVC and DVC. FAZ area is excluded in measurement of vessel density in this area, due to its high variability among different individuals. FD-300 is a useful parameter that gives us additional information about vasculature surrounding FAZ area and it has been used in the detection of early signs of diabetic retinopathy in previous studies.
Radial peripapillary capillary measurement
A rectangle scan of 4.5*4.5mm centered on ONH was obtained for each eye with AngioVue OCTA system using Angio-Disc mode. The software automatically fits a 2.0 mm diameter circle, centered on ONH, and defines a circle 2.0 mm wide that extends from the optic disc as the peripapillary region. The peripapillary area was divided into the following eight regions automatically based on Garway-Heath method: nasal superior (NS), nasal inferior (NI), inferior nasal (IN), inferior temporal (IT). Temporal inferior (TI), temporal superior (TS), superior temporal (ST) and superior nasal (SN). Vessel densities of the whole image, inside disc and each sector of peripapillary area were generated by the software automatically.
The patients were divided into two sub groups based on the location (superior or inferior) of the affected vein and vessel density in each sector of the superficial and deep vascular plexus and RPC were compared in each group.
The automated layer segmentation and FAZ delineation in all scans were reviewed by two independent experts. In case of segmentation errors or FAZ delineation error, the examiners corrected errors manually until agreement achieved.
Statistic analysis was performed by SPSS (SPSS for Mac, version 25.0; IBM/SPSS, Chicago, IL, USA). Continuous variables are sumerized as mean and SD. Paired t test was applied to compare the demographics and evaluate the difference in macular metrics, FAZ parameters and peripapillary vessel densities between BRVO eyes and the contralateral ones. Unpaired t test was applied between contralateral eyes and normal control ones. A two-tailed P value of < 0.05 as statistically significant.