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We describe a cost-effective, reproducible circuit in a porcine, ex vivo, continuous warm-blood, bi-ventricular, working heart model that has future possibilities for pre-transplant assessment of marginal hearts donated from brain stem dead donors and hearts donated after circulatory determination of death (DCDD). In five consecutive experiments over five days, pressure volume loops were performed. During working mode, the left ventricular end systolic pressure volume relationship (LV ESPVR) was 23.1±11.1 mmHg/ml and the LV preload recruitable stroke work (PRSW) was 67.8±7.2. (Standard PVAN analysis software) (Millar Instruments, Houston, TX, USA) All five hearts were perfused for 219±64 minutes and regained normal cardiac function on the perfusion system.They displayed a significant upward and leftward shift of the end systolic pressure volume relationship, a significant increase in preload recruitable stroke work and minimal stiffness. These hearts could potentially be considered for transplantation. The circuit was effective during reperfusion and working modes whilst proving to be successful in maintaining cardiac function in excess of four hours. Using an autologous prime of approximately 20% haematocrit (Hct), electrolytes and blood gases were easy to control within this period using standard perfusion techniques.

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Background Cardiovascular diseases remain the world’s primary cause of death. The identification and treatment of patients at risk of cardiovascular events thus are as important as ever. Adipose tissue is a classic risk factor for cardiovascular diseases, has been linked to systemic inflammation, and is suspected to contribute to vascular calcification. To further investigate this issue, the use of texture analysis of adipose tissue using radiomics features could prove a feasible option. Methods In this retrospective single-center study, 55 patients (mean age 56, 34 male, 21 female) were scanned on a first-generation photon-counting CT. On axial unenhanced images, periaortic adipose tissue surrounding the thoracic descending aorta was segmented manually. For feature extraction, patients were divided into three groups, depending on coronary artery calcification (Agatston Score 0, Agatston Score 1–99, Agatston Score ≥ 100). 106 features were extracted using pyradiomics. R statistics was used for statistical analysis, calculating mean and standard deviation with Pearson correlation coefficient for feature correlation. Random Forest classification was carried out for feature selection and Boxplots and heatmaps were used for visualization. Additionally, monovariable logistic regression predicting an Agatston Score > 0 was performed, selected features were tested for multicollinearity and a 10-fold cross-validation investigated the stability of the leading feature. Results Two higher-order radiomics features, namely “glcm_ClusterProminence” and “glcm_ClusterTendency” were found to differ between patients without coronary artery calcification and those with coronary artery calcification (Agatston Score ≥ 100) through Random Forest classification. As the leading differentiating feature “glcm_ClusterProminence” was identified. Conclusion Changes in periaortic adipose tissue texture seem to correlate with coronary artery calcium score, supporting a possible influence of inflammatory or fibrotic activity in perivascular adipose tissue. Radiomics features may potentially aid as corresponding biomarkers in the future.

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