Optical Coherence Tomography Angiography Assessment of the Deep Retinal Capillary Plexus in Patients With Diabetic Retinopathy

Abstract

Purpose : To describe the correlation between conventional high-definition optical coherence tomography (OCT) and optical coherence tomography angiography (OCT-A) in patients with diabetic retinopathy and deep retinal capillary ischemia.

Methods : Patients with diabetic retinopathy were analyzed by OCT and OCT-A (Heidelberg Engineering) macular scan and those showing any degree of deep retinal capillary plexus dropout were included in the present study. A qualitative correlation between areas of preserved and damaged deep capillary plexus as observed with OCT-A, and the morphometric appearance of them in the corresponding OCT scans was performed. Patients with cystoid retinal changes were excluded from the analysis in order to avoid possible bias induced by the disrupted retinal anatomy in such cases.

Results : A total number of 20 eyes of 10 diabetic patients (7 men and 3 women; age range: 25-72 years). A robust and constant qualitative association was evidenced between areas of deep retinal capillary ischemia and focal thinning of the INL. Deep capillary ischemic changes were evidenced to show a honeycomb distribution in the OCT-A images that was translated into a wreath-like contour of the INL in the corresponding sectional OCT scan.

Conclusions : The advent of OCT-A is one of the most exciting advances within the field of macular diseases. For the first time we are able to clearly differentiate and visualize both the superficial and deep retinal capillary plexi. Patients with retinal vascular diseases may benefit from this new imaging modality as changes at the level of the deep plexus have an increasing role in their prognosis. We have found a consistent correlation between areas of INL thining in the conventional OCT scans with the deep capillary dropout in the OCT-A images, suggesting that the OCT changes may constitute an indirect sign of those ischemic changes.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.