The use of AVF.SIM system for the surgical planning of arteriovenous fistulae in routine clinical practice.

BACKGROUND: The number of patients treated with hemodialysis (HD) in Europe is more than half a million and this number increases annually. The arteriovenous fistula (AVF) is the vascular access (VA) of first choice, but the clinical outcome is still poor. A consistent number of AVFs fails to reach the desired blood flow rate for HD treatment, while some have too high flow and risk for cardiac complications. Despite the skill of the surgeons and the possibility to use Ultrasound investigation for mapping arm vasculature, it is still not possible to predict the blood flow volume that will be obtained after AVF maturation. METHODS: We evaluated the potential of using a computational model (AVF.SIM) to predict the blood flow volume that will be achieved after AVF maturation, within a multicenter international clinical investigation aimed at assessing AVF.SIM predictive power. The study population included 231 patients, with data on AVF maturation in 124 patients, and on long-term primary patency in 180 patients. RESULTS: At 1 year of follow-up, about 60% of AVFs were still patent, with comparable primary patency in proximal and distal anastomosis. The correlation between predicted and measured blood flow volume in the brachial artery at 40 days after surgery was statistically significant, with an overall correlation coefficient of 0.58 (p < 0.001). The percent difference between measured and predicted brachial blood flow 40 days after surgery was less than 30% in 72% of patients investigated. CONCLUSIONS: The results indicate that the use of the AVF.SIM system allowed to predict with a good accuracy the blood flow volume achievable after VA maturation, for a given location and type of anastomosis. This information may help in AVF surgical planning, reducing the AVFs with too low or too high blood flow, thus improving AVF patency rate and clinical outcome of renal replacement therapy.

Arteriovenous fistula creation with VasQTM device: A feasibility study to reveal hemodynamic implications.

BACKGROUND: Arteriovenous fistula (AVF) is the preferred vascular access (VA) for hemodialysis, but it is still affected by high non-maturation and early failure rates due to stenosis development. Increasing evidence suggests that the presence of turbulent-like flow may play a key role, therefore, to stabilize the flow in the venous segment, an external support device (VasQTM) has been designed. The aim of this study was to provide preliminary evidence of VasQTM impact on AVF hemodynamics as compared to AVFs created with conventional surgery. METHODS: In this pilot single-center prospective randomized study six patients were enrolled, three in the VasQ group and three in the control group. Contrast-free magnetic resonance imaging (MRI) scans were acquired at 3 days, 3 months and 1 year after AVF surgery and were used to generate 3D patient-specific models. Computational fluid dynamic (CFD) simulations were performed using pimpleFoam, imposing patient-specific flow waveforms derived from ultrasound (US) examinations at the inlet of the proximal and distal artery, and a traction-free condition at the venous outflow. Morphologic and hemodynamic changes occurring over time were compared between VasQ and control AVFs. RESULTS: Our MRI protocol provided high-quality images suitable for reliable segmentation and reconstruction of patient-specific 3D models of AVFs at all three timepoints in four out of six patients. The VasQTM device maintained the angle between the artery and the vein almost unchanged over time, with a more stable flow in the AVFs supported by the device. In contrast, one of the AVFs of the control group evolved to an extreme dilatation of the vein and highly disturbed flow, while the other developed a stenosis in the juxta-anastomotic region. CONCLUSIONS: This study demonstrated the feasibility of characterizing the morphological and hemodynamic changes occurring over time in AVFs created using the VasQTM device and provided preliminary evidence of the potential hemodynamic benefits of its use.

Case Report: Effects of Anti-SARS-CoV-2 Convalescent Antibodies Obtained With Double Filtration Plasmapheresis.

Passive antibody therapy has been used to treat outbreaks of viral disease, including the ongoing pandemic of severe respiratory acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2) or COVID-19. However, the real benefits of the procedure are unclear. We infused a concentrated solution of neutralizing anti-SARS-CoV-2 antibodies obtained from a convalescent donor with a single session of double filtration plasmapheresis (DFPP) into a 56-year-old woman with long history of unremitting, severe COVID-19. She was unable to establish an adequate antiviral immune response because of previous chemotherapy, including the infusion of the anti-CD20 monoclonal antibody rituximab, administered to treat a diffuse large B-cell lymphoma. The disease promptly recovered despite evidence of no endogenous anti-SARS-CoV-2 antibody production. The observation that passive antibody therapy might prove particularly effective in immunodepressed COVID-19 patients requires evaluation in prospective randomized controlled trial.

Prediction of the type of milk and degree of ripening in cheeses by means of artificial neural networks with data concerning fatty acids and near infrared spectroscopy.

The present study addresses the prediction of the time of ripening and type of mixtures of milk (cow's, ewe's and goat's) in cheeses of varying composition using artificial neural networks (ANN). To accomplish this aim, neural networks were designed using as input data the content of 19 fatty acids obtained with GC-FID of the cheese fat and scores obtained from principal component analysis (PCA) of NIR spectra. The best model of neuronal networks for the identification of the type of mixtures of milk was obtained using the information concerning the fatty acid concentration (80% of correct results in the training phase and 75% in the validation phase). Regarding the information of the near-infrared (NIR) spectra a neural network was designed. The aforesaid neural network predicted the ripening of cheeses with 100% accuracy in both training and in validation.