Retinal Vascular Changes in Heart Failure with Preserved Ejection Fraction Using Optical Coherence Tomography Angiography

Background: Systemic microvascular regression and dysfunction are considered important underlying mechanisms in heart failure with preserved ejection fraction (HFpEF), but retinal changes are unknown. Methods: This prospective study aimed to investigate whether retinal microvascular and structural parameters assessed using optical coherence tomography angiography (OCT-A) differ between patients with HFpEF and control individuals (i.e., capillary vessel density, thickness of retina layers). We also aimed to assess the associations of retinal parameters with clinical and echocardiographic parameters in HFpEF. HFpEF patients, but not controls, underwent echocardiography. Macula-centered 6 × 6 mm volume scans were computed of both eyes. Results: Twenty-two HFpEF patients and 24 controls without known HFpEF were evaluated, with an age of 74 [68-80] vs. 68 [58-77] years (p = 0.027), and 73% vs. 42% females (p = 0.034), respectively. HFpEF patients showed vascular degeneration compared to controls, depicted by lower macular vessel density (p < 0.001) and macular ganglion cell-inner plexiform layer thickness (p = 0.025), and a trend towards lower total retinal volume (p = 0.050) on OCT-A. In HFpEF, a lower total retinal volume was associated with markers of diastolic dysfunction (septal e', septal and average E/e': R2 = 0.38, 0.36, 0.25, respectively; all p < 0.05), even after adjustment for age, sex, diabetes mellitus, or atrial fibrillation. Conclusions: Patients with HFpEF showed clear levels of retinal vascular changes compared to control individuals, and retinal alterations appeared to be associated with markers of more severe diastolic dysfunction in HFpEF. OCT-A may therefore be a promising technique for monitoring systemic microvascular regression and cardiac diastolic dysfunction.

Inter-Rater Reliability of Proliferative Diabetic Retinopathy Assessment on Wide-Field OCT-Angiography and Fluorescein Angiography

Purpose

To assess inter-rater reliability in the detection of proliferative diabetic retinopathy (PDR) changes using wide-field optical coherence tomography angiography (WF-OCTA) versus fluorescein angiography (FA).

Methods

This retrospective, cross-sectional study included patients with severe nonproliferative and PDR. Images were acquired with 12 × 12 mm WF-OCTA and FA with a 55° lens. Images were cropped to represent the exact same field of view. Qualitative (detection of neovascularization at the disc [NVD] and elsewhere [NVE], enlarged foveal avascular zone [FAZ], vitreous hemorrhage [VH]) and quantitative analyses (FAZ area, horizontal, vertical, and maximum FAZ diameter) were performed by 2 masked graders using ImageJ. Inter-rater reliability was calculated using unweighted Cohen's kappa coefficient (κ) for qualitative analyses and intraclass correlation coefficients (ICC) for quantitative analyses.

Results

Twenty-three eyes of 17 patients were included. Inter-rater reliability was higher for FA than for WF-OCTA in qualitative analyses: κ values were 0.65 and 0.78 for detection of extended FAZ, 0.83 and 1.0 for NVD, 0.78 and 1.0 for NVE, and 0.19 and 1 for VH for WF-OCTA and FA, respectively. In contrast, inter-rater reliability was higher for WF-OCTA than for FA in the quantitative analyses: ICC values were 0.94 and 0.76 for FAZ size, 0.92 and 0.79 for horizontal FAZ diameter, 0.82 and 0.72 for vertical FAZ diameter, and 0.88 and 0.82 for maximum FAZ diameter on WF-OCTA and FA, respectively.

Conclusions

Inter-rater reliability of FA is superior to WF-OCTA for qualitative analyses whereas inter-rater reliability of WF-OCTA is superior to FA for quantitative analyses.

Translational Relevance

The study highlights the specific merits of both imaging modalities in terms of reliability. FA should be preferred for qualitative parameters, whereas WF-OCTA should be preferred for quantitative parameters.

Multiple instance learning detects peripheral arterial disease from high-resolution color fundus photography

Peripheral arterial disease (PAD) is caused by atherosclerosis and is a common disease of the elderly leading to excess morbidity and mortality. Early PAD diagnosis is important, as the only available causal therapy is addressing risk factors like smoking, hypercholesterolemia or hypertension. However, current diagnostic techniques often do not detect early stages of PAD. We theorize that PAD’s underlying cause atherosclerosis can be detected on color fundus photography (CFP) images with a convolutional neural network architecture, which might aid earlier PAD diagnosis and improve disease monitoring. In this explorative study a deep attention-based Multiple Instance Learning (MIL) architecture is used to capture retinal imaging biomarkers on CFP images of 135 examinations. To capture subtle variations in vascular structures, higher image resolution can be utilized by partitioning the CFP into patches. Our architecture converts each patch into a feature vector, and determines its relative importance via an automatically computed attention weight. Our best model achieves an ROC AUC score of 0.890. Visualizing these attention weights provides insights about the network’s decision and suggests ocular involvement in PAD. Statistical analysis confirms that the optic disc and the temporal arcades are weighted significantly higher (p < 0.001) than retinal background. Our results support the feasibility of detecting the presence of PAD with a modern deep learning approach.

Automated thresholding algorithms outperform manual thresholding in macular optical coherence tomography angiography image analysis

Introduction

For quantification of Optical Coherence Tomography Angiography (OCTA) images, Vessel Density (VD) and Vessel Skeleton Density (VSD) are well established parameters and different algorithms are in use for their calculation. However, comparability, reliability and ability to discriminate healthy and impaired macular perfusion of different algorithms are unclear, yet, of potential high clinical relevance. Hence, we assessed comparability and test-retest reliability of the most common approaches.

Materials and methods

Two consecutive 3×3mm OCTA en face images of the superficial and deep retinal layer were acquired with swept-source OCTA. VD and VSD were calculated with manual thresholding and six automated thresholding algorithms (Huang, Li, Otsu, Moments, Mean, Percentile) using ImageJ and compared in terms of intra-class correlation coefficients, measurement differences and repeatability coefficients. Receiver operating characteristic analyses (healthy vs. macular pathology) were performed and Area Under the Curve (AUC) values were calculated.

Results

Twenty-six eyes (8 female, mean age: 47 years) of 15 patients were included (thereof 15 eyes with macular pathology). Binarization thresholds, VD and VSD differed significantly between the algorithms and compared to manual thresholding (p < 0.0001). Inter-measurement differences did not differ significantly between patients with healthy versus pathologic maculae (p ≥ 0.685). Reproducibility was higher for the automated algorithms compared to manual thresholding on all measures of reproducibility assessed. AUC was significantly higher for the Mean algorithm compared to the manual approach with respect to the superficial retinal layer.

Conclusions

Automated thresholding algorithms yield a higher reproducibility of OCTA parameters and allow for a more sensitive diagnosis of macular pathology. However, different algorithms are not interchangeable nor results readily comparable. Especially the Mean algorithm should be investigated in further detail. Automated thresholding algorithms are preferable but more standardization is needed for clinical use.