Computational investigation of the haemodynamics shows criticalities of central venous lines used for chronic haemodialysis in children

Background: Haemodialysis is a life-saving treatment for children with kidney failure. The majority of children have haemodialysis through central venous lines (CVLs). The use of CVLs in pediatric patients is often associated to complications which can lead to their replacement. The aim of this study is to investigate haemodynamics of pediatric CVLs to highlight the criticalities of different line designs. Methods: Four models of CVLs for pediatric use were included in this study. The selected devices varied in terms of design and sizes (from 6.5 Fr to 14 Fr). Accurate 3D models of CVLs were reconstructed from high-resolution images including venous and arterial lumens, tips and side holes. Computational fluid dynamics (CFD) analyses were carried out to simulate pediatric working conditions of CVLs in ideal and anatomically relevant conditions. Results: The arterial lumens of all tested CVLs showed the most critical conditions with the majority of blood flowing through the side-holes. A zone of low flow was identified at the lines' tip. The highest shear stresses distribution (>10 Pa) was found in the 8 Fr line while the highest platelet lysis index in the 10 Fr model. The analysis on the anatomical geometry showed an increase in wall shear stress measured in the 10 F model compared to the idealised configuration. Similarly, in anatomical models an increased disturbance and velocity of the flow was found inside the vein after line placement. Conclusion: This study provided a numerical characterization of fluid dynamics in pediatric CVLs highlighting performance criticalities (i.e. high shear stresses and areas of stagnation) associated to specific sizes (8 Fr and 10 Fr) and conditions (i.e. anatomical test)

Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields

Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in ∼0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy

Deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields

Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N=67). Inference performed on 200 test shapes resulted in average errors of 6.01% +/-3.12 SD and 3.99% +/-0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in +/-0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with high accuracy.Comment: 22 pages, 19 figure

Characterization of flow dynamics in the pulmonary bifurcation of patients with repaired Tetralogy of Fallot : a computational approach

The hemodynamic environment of the pulmonary bifurcation is of great importance for adult patients with repaired tetralogy of Fallot (rTOF) due to possible complications in the pulmonary valve and narrowing of the left pulmonary artery (LPA). The aim of this study was to computationally investigate the effect of geometrical variability and flow split on blood flow characteristics in the pulmonary trunk of patient-specific models. Data from a cohort of seven patients was used retrospectively and the pulmonary hemodynamics was investigated using averaged and MRI-derived patient-specific boundary conditions on the individualized models, as well as a statistical mean geometry. Geometrical analysis showed that curvature and tortuosity are higher in the LPA branch, compared to the right pulmonary artery (RPA), resulting in complex flow patterns in the LPA. The computational analysis also demonstrated high time-averaged wall shear stress (TAWSS) at the outer wall of the LPA and the wall of the RPA proximal to the junction. Similar TAWSS patterns were observed for averaged boundary conditions, except for a significantly modified flow split assigned at the outlets. Overall, this study enhances our understanding about the flow development in the pulmonary bifurcation of rTOF patients and associates some morphological characteristics with hemodynamic parameters, highlighting the importance of patient-specificity in the models. To confirm these findings, further studies are required with a bigger cohort of patients

A multi-omics investigation of tacrolimus off-target effects on a proximal tubule cell-line

Introduction: Tacrolimus, an immunosuppressive drug prescribed to a majority of organ transplant recipients is nephrotoxic, through still unclear mechanisms. This study on a lineage of proximal tubular cells using a multi-omics approach aims to detect off-target pathways modulated by tacrolimus that can explain its nephrotoxicity. Methods: LLC-PK1 cells were exposed to 5 µM of tacrolimus for 24 h in order to saturate its therapeutic target FKBP12 and other high-affine FKBPs and favour its binding to less affine targets. Intracellular proteins and metabolites, and extracellular metabolites were extracted and analysed by LC-MS/MS. The transcriptional expression of the dysregulated proteins PCK-1, as well as of the other gluconeogenesis-limiting enzymes FBP1 and FBP2, was measured using RT-qPCR. Cell viability with this concentration of tacrolimus was further checked until 72 h. Results: In our cell model of acute exposure to a high concentration of tacrolimus, different metabolic pathways were impacted including those of arginine (e.g., citrulline, ornithine) (p < 0.0001), amino acids (e.g., valine, isoleucine, aspartic acid) (p < 0.0001) and pyrimidine (p < 0.01). In addition, it induced oxidative stress (p < 0.01) as shown by a decrease in total cell glutathione quantity. It impacted cell energy through an increase in Krebs cycle intermediates (e.g., citrate, aconitate, fumarate) (p < 0.01) and down-regulation of PCK-1 (p < 0.05) and FPB1 (p < 0.01), which are key enzymes in gluconeogenesis and acid-base balance control. Discussion: The variations found using a multi-omics pharmacological approach clearly point towards a dysregulation of energy production and decreased gluconeogenesis, a hallmark of chronic kidney disease which may also be an important toxicity pathway of tacrolimus

La communication comme vecteur de bien-être professionnel en milieu hospitalier : étude comparative

Master [60] en information et communication, Université catholique de Louvain, 201

Patient-specific blood flow modelling

Cardiovascular diseases that affect arteries often result in vessel blockage which can have dramatic consequences on the patient's life. In situation of obstructed arteries, graft surgery is a common treatment to improve blood flow circulation. Nevertheless, in the particular case of lower limb arteries, the rate of success of this medical intervention is quite low. The patients suffer from graft blockages induced by Intimal Hyperplasia (IH). Local flow patterns are known to have a strong influence on the development of IH. In order to better understand the causes of graft failure, Computational Fluid Dynamics is used to study the role of hemodynamics in each patient bypass geometry. This thesis focuses on both the automatic mesh generation from medical images and the blood flow simulation. First, the patient graft shape is constructed from image segmentation with particular focus on the distal junction with the native artery. The initial mesh is then adapted from the surface curvature to take into account the influence of the geometrical specificities. An anisotropic metric field is generated on the surface and in the volume in order to reduce the total number of degrees of freedom. The obtained mesh is considered more suitable for numerical computation. Secondly, fluid simulations were run using experimental data from each patient to set the boundary conditions on the model. The localization of hemodynamic indicators allows to determine areas of the geometry most susceptible to the development of strong cell proliferation. Realistic geometries are shown to have a strong influence on the flow patterns and therefore on the pathology progression.(FSA - Sciences de l) -- UCL, 201