The aim of this study was This approach, based on the time domain analysis of the radiofrequency signals, appears promising as a means to establish certain aspects of ultrasonic diagnosis on a more quantitative basis.3,4 The assessment of regional myocardial fibrosis would be of particular interest since excessive myocardial fibrosis is both an important sign and is associated with a variety of myocardial diseases.Even though there were substantial problems in comparing exactly the anatomic region interrogated by the ultrasound technique versus the same area sampled by the endomyocardial biopsy, the aim of this study was to assess in vivo whether the regional ultrasonic reflectivity, evaluated by a real-time integrated backscatter

SMARTool aims to the development of a clinical decision support system (CDSS) for the management and stratification of patients with coronary artery disease (CAD). This will be achieved by performing computational modeling of the main processes of atherosclerotic plaque growth. More specifically, computed tomography coronary angiography (CTCA) is acquired and 3-dimensional (3D) reconstruction is performed for the arterial trees. Then, blood flow and plaque growth modeling is employed simulating the major processes of atherosclerosis, such as the estimation of endothelial shear stress (ESS), the lipids transportation, low density lipoprotein (LDL) oxidation, macrophages migration and plaque development. The plaque growth model integrates information from genetic and biological data of the patients. The SMARTool system enables also the calculation of the virtual functional assessment index (vFAI), an index equivalent to the invasively measured fractional flow reserve (FFR), to provide decision support for patients with stenosed arteries. Finally, it integrates modeling of stent deployment. In this work preliminary results are presented. More specifically, the reconstruction methodology has mean value of Dice Coefficient and Hausdorff Distance is 0.749 and 1.746, respectively, while low ESS and high LDL concentration can predict plaque progression.

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SMARTool aims to the accurate risk stratification of coronary artery disease patients as well as to the early diagnosis and prediction of disease progression. This is achieved by the acquisition of data from about 300 patients including computed tomography angiographic images, clinical, molecular, biohumoral, exposome, inflammatory and omics data. Data are collected in two time points with a follow-up period of approximately 5 years. In the first step, data mining techniques are implemented for the estimation of risk stratification. In the next step, patients, who are classified as medium to high risk are considered for coronary imaging and computational modelling of blood flow, plaque growth and stenosis severity assessment. Additionally, patients with increased stenosis are selected for stent deployment. All the above modules are integrated in a cloud-based platform for the clinical decision support (CDSS) of patients with coronary artery disease. The work presents preliminary results employing the SMARTool dataset as well as the concept and architecture of the under development platform.

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Coronary artery disease (CAD) is one of the most common causes of death in western societies. SMARTool project proposes a new concept for the risk stratification, diagnosis, prediction and treatment of CAD. Retrospective and prospective data (clinical, biohumoral, computed tomography coronary angiography (CTCA) imaging, omics, lipidomics, inflammatory and exposome) have been collected from ~250 patients. The proposed patient risk stratification, relying on machine learning analysis of non-imaging data, discriminates low and medium-to-high risk patients. The CAD diagnosis module is based on the 3D reconstruction and automatic blood flow dynamics of the coronary arteries, and the non-invasive estimation of smartFFR, an index correlated with invasively measured fractional flow reserve (FFR). CAD prediction is based on complex computational models of plaque growth considering the blood rheology, the lipoproteins transport and the major mechanisms of plaque growth, such as the inflammation and the foam cells formation. Finally, the treatment module is based on the simulation of virtual stent deployment. Preliminary analysis of 101 patients yielded an overall accuracy of 85.2% with the sensitivity of Class II reaching 98%. The reconstruction methodology is validated against intravascular ultrasound data and the correlation of the geometry derived metrics such as the degree of stenosis, minimal lumen area, minimal lumen diameter, plaque burden are 0.79, 0.85, 0.81 and 0.75, respectively. SmartFFR has been validated compared to invasively measured FFR with a correlation coefficient of 0.90. Plaque growth modelling demonstrates that the inclusion of variables such as the macrophages and foam cells concentrations can increase to 75% the prediction accuracy of regions prone to plaque formation.

Background Leptin is an adipokine involved in energy homeostasis and has been related with established vascular risk factors. However, studies on the association of leptin plasma levels with coronary artery disease (CAD) have yielded conflicting results. Purpose Aim of the present study was to evaluate the association between leptin plasma levels and presence, severity and progression of coronary atherosclerosis in patients with suspected stable CAD. Methods In a cohort of 257 patients with symptoms of stable CAD enrolled in the SMARTool study, coronary computed tomography angiography (CTA), plasma leptin levels and clinical and bio-humoral CAD risk profile (including glucose, lipid and inflammation variables) were obtained at enrolment and after 6±1yrs of follow-up. Sixty-four patients were revascularized and the remaining 193 represent the population for the present study. CTA findings were categorised as no-minimal CAD (<30% stenosis), non-obstructive CAD (30%-50% stenosis) and obstructive CAD (≥50% stenosis in at least one major coronary vessel). A CTA risk score (based on plaque extent, severity, composition, and location) was calculated at baseline and at follow-up to assess coronary atherosclerotic burden and its progression (Δ CTA score≥5). Results CTA findings showed obstructive CAD in 11% of patients at baseline and in 15% at follow-up (p<0.0001). CTA risk score, was 8.03±7.80 at baseline and increased to 10.33±8.17 at follow-up (p<0.0001) with CAD progression in 20% of patients. Leptin plasma levels were inversely related with CTA findings both at baseline and follow-up (Figure). In a Cox model, baseline plasma leptin was an independent predictor of CAD progression, after adjustment for clinical risk factors, biomarkers, and treatment (HR 0.572, 95% CI 0.393–0.834, P=0.0037). Figure 1 Conclusion Plasma leptin is inversely associated with coronary atherosclerotic burden and disease progression in patients with stable CAD. This association is independent of known factors affecting leptin levels. These results could prompt further investigations on the pathophysiological mechanisms of this association. Acknowledgement/Funding EU H2020 research and innovation program under grant agreement No 689068