No abstract

Epicardial adipose tissue volume (EAT) has been linked to coronary artery disease and the risk of major adverse cardiac events. As manual quantification of EAT is time-consuming, requires specialized training, and is prone to human error, we developed a deep learning method (DeepFat) for the automatic assessment of EAT on non-contrast low-dose CT calcium score images. Our DeepFat intuitively segmented the tissue enclosed by the pericardial sac on axial slices, using two preprocessing steps. First, we applied a HU-attention-window with a window/level 350/40-HU to draw attention to the sac and reduce numerical errors. Second, we applied a novel look ahead slab-of-slices with bisection (“bisect”) in which we split the heart into halves and sequenced the lower half from bottom-to-middle and the upper half from top-to-middle, thereby presenting an always increasing curvature of the sac to the network. EAT volume was obtained by thresholding voxels within the sac in the fat window (− 190/− 30-HU). Compared to manual segmentation, our algorithm gave excellent results with volume Dice = 88.52% ± 3.3, slice Dice = 87.70% ± 7.5, EAT error = 0.5% ± 8.1, and R = 98.52% (p < 0.001). HU-attention-window and bisect improved Dice volume scores by 0.49% and 3.2% absolute, respectively. Variability between analysts was comparable to variability with DeepFat. Results compared favorably to those of previous publications.

No abstract

Disease prevention frameworks and clinical practice guidelines in the United States (US) have traditionally ignored upstream social determinants of health (SDOH), which are critical for reducing disparities in cardiovascular disease (CVD)—the leading cause of death in the US. Existing evidence demonstrates a protective effect of social support, social cohesion, and community engagement on overall health and wellbeing. Increasing community and social support is a major objective of the Healthy People 2030 initiative, with special provisions for vulnerable populations. However, to date, existing evidence of the association between community and social context (CSC)—an integral SDOH domain—and CVD has not been reviewed extensively. In particular, the individual and cumulative impact of CSC on CVD risk and the pathways linking CSC to cardiovascular outcomes are not well understood. In this review, we critically appraise current knowledge of the association between CSC and CVD, describe potential pathways linking CSC to CVD, and identify opportunities for evidence-based policy and practice interventions to improve CVD outcomes.

No abstract