CARDIAC IMAGINGC oronary artery calcium (CAC) scoring in dedicated CT examinations is frequently performed to measure coronary atherosclerotic plaque burden and predict cardiovascular disease (CVD) risk. The CAC score may have increasing applications, as noted in the 2018-2019 American Heart Association and American College of Cardiology Guidelines for Cholesterol and Prevention, whereby the CAC score is a tool to refine the 10-year risk of atherosclerotic CVD when the CVD risk may be uncertain (1). CAC scoring is performed by using nonenhanced electrocardiographical-Purpose: To evaluate the performance of a DL method for automatic calcium scoring across a wide range of CT examination types and to investigate whether the method can adapt to different types of CT examinations when representative images are added to the existing training data set. Materials and Methods:The study included 7240 participants who underwent various types of nonenhanced CT examinations that included the heart: coronary artery calcium (CAC) scoring CT, diagnostic CT of the chest, PET attenuation correction CT, radiation therapy treatment planning CT, CAC screening CT, and low-dose CT of the chest. CAC and thoracic aorta calcification (TAC) were quantified using a convolutional neural network trained with (a) 1181 low-dose chest CT examinations (baseline), (b) a small set of examinations of the respective type supplemented to the baseline (data specific), and (c) a combination of examinations of all available types (combined). Supplemental training sets contained 199-568 CT images depending on the calcium burden of each population. The DL algorithm performance was evaluated with intraclass correlation coefficients (ICCs) between DL and manual (Agatston) CAC and (volume) TAC scoring and with linearly weighted k values for cardiovascular risk categories (Agatston score; cardiovascular disease risk categories: 0, 1-10, 11-100, 101-400, .400).Results: At baseline, the DL algorithm yielded ICCs of 0.79-0.97 for CAC and 0.66-0.98 for TAC across the range of different types of CT examinations. ICCs improved to 0.84-0.99 (CAC) and 0.92-0.99 (TAC) for CT protocol-specific training and to 0.85-0.99 (CAC) and 0.96-0.99 (TAC) for combined training. For assignment of cardiovascular disease risk category, the k value for all test CT scans was 0.90 (95% confidence interval [CI]: 0.89, 0.91) for the baseline training. It increased to 0.92 (95% CI: 0.91, 0.93) for both data-specific and combined training. Conclusion:A deep learning calcium scoring algorithm for quantification of coronary and thoracic calcium was robust, despite substantial differences in CT protocol and variations in subject population. Augmenting the algorithm training with CT protocol-specific images further improved algorithm performance.

Diabetes was 3-fold more common in Southeast Asian compared to white patients with HF, despite younger age and less obesity, and more strongly associated with poor outcomes in Asian patients than white patients. These results underscore the importance of ethnicity-tailored aggressive strategies to prevent diabetes and its complications.

Cardiovascular disease (CVD) is the leading cause of death worldwide and its prevalence is expected to rise rapidly worldwide in the coming decades. Atherosclerosis, the syndrome underlying CVD, is a chronic progressive disease of the arteries already present at a young age. Strokes, heart attacks and heart failure are acute CVD events that occur after decades, however, and require timely diagnosis and treatment. Plasma extracellular vesicles (EVs) are microstructures with a lipid bilayer membrane involved in hemostasis, inflammation and injury. Both EV-counts and EV-content are associated with CVD and the identification of plasma EVs is a novel source of blood-based biomarkers with the potential to improve diagnosis and prognosis of CVD. Presented in this review is an overview of the current use of EVs in CVD and a discussion of the need for robust and easy isolation technologies for plasma EV subsets. This is needed to bring this promising field towards clinical application in the patient.

Diagnosing stable ischemic heart disease (IHD) is challenging, especially in females. Currently, no blood test is available. Plasma extracellular vesicles (EV) are emerging as potential biomarker source. We therefore aimed to identify stress induced ischemia due to stable IHD with plasma extracellular vesicle protein levels in chest pain patients. We analyzed 450 patients suspected for stable IHD who were referred for 82 Rb PET/CT in the outpatient clinic. Blood samples were collected before PET/CT and plasma EVs were isolated in 3 plasma subfractions named: TEX, HDL, LDL. In total 6 proteins were quantified in each of these subfractions using immuno-bead assays. CD14 and CystatinC protein levels were independent significant predictors of stress-induced ischemia in the LDL and the HDL subfraction and SerpinC1 and SerpinG1 protein levels in the HDL fraction. Subgroup-analysis on sex revealed that these associations were completely attributed to the associations in women. None of the significant EV proteins remained significant in men. Plasma EV proteins levels are associated with the presence of stable IHD in females presenting with chest pain. This finding, if confirmed in larger cohort studies could be a crucial step in improving diagnostic assessment of women with suspected IHD.