Intereye Microvascular Differences in Patients With Same-Stage Diabetic Retinopathy Revealed by OCTA.

Purpose: To evaluate microvascular intereye differences in diabetic patients with same-stage diabetic retinopathy (DR) in both eyes as assessed using optical coherence tomography angiography (OCTA). Methods: In this cross-sectional study, fovea-centered swept-source 6 × 6 mm OCTA scans were acquired using a 200 kHz OCTA device. Vessel density (VD) and fractal dimension were calculated on binarized, vessel-segmented images in the superficial capillary plexus (SCP) and deep capillary plexus (DCP). Foveal avascular zone (FAZ) area (FAZA) and perimeter (FAZP) was measured and FAZ circularity (FAZC) calculated. Absolute difference (δabs) and asymmetry index between eyes was assessed and compared across DR stages. Differences of VD, FD, and FAZ parameters between left and right eye were evaluated using linear mixed models. Results: A total of 336 eyes of 168 diabetic patients without DR and with DR stages ranging from mild nonproliferative to proliferative DR were included for analysis. The intereye comparison revealed significantly lower VD in the SCP (estimate [95% CI] = -0.009 [-0.01; -0.006], P < 0.01), as well as a significantly lower FD in the SCP (-0.007 [-0.009; -0.005], P < 0.01) of the left compared to the right eye. FAZC of the left compared to the right eye was lower in eyes without DR, moderate DR, and PDR (P < 0.05). FAZ δabs and asymmetry index were higher in more advanced disease stages (P < 0.05). Conclusions: OCTA metrics provide important information on the retinal microvasculature in systemic diseases such as DR. Our results reveal a significant intereye difference with lower VD and FD in the SCP as well as higher FAZ impairment of the left compared to the right eye.

Developing a fully automated evidence synthesis tool for identifying, assessing and collating the evidence.

Evidence synthesis is a key element of evidence-based medicine. However, it is currently hampered by being labour intensive meaning that many trials are not incorporated into robust evidence syntheses and that many are out of date. To overcome this, a variety of techniques are being explored, including using automation technology. Here, we describe a fully automated evidence synthesis system for intervention studies, one that identifies all the relevant evidence, assesses the evidence for reliability and collates it to estimate the relative effectiveness of an intervention. Techniques used include machine learning, natural language processing and rule-based systems. Results are visualised using modern visualisation techniques. We believe this to be the first, publicly available, automated evidence synthesis system: an evidence mapping tool that synthesises evidence on the fly.