Hemolysis at low blood flow rates: in-vitro and in-silico evaluation of a centrifugal blood pump

Background Treating severe forms of the acute respiratory distress syndrome and cardiac failure, extracorporeal membrane oxygenation (ECMO) has become an established therapeutic option. Neonatal or pediatric patients receiving ECMO, and patients undergoing extracorporeal CO2 removal (ECCO2R) represent low-flow applications of the technology, requiring lower blood flow than conventional ECMO. Centrifugal blood pumps as a core element of modern ECMO therapy present favorable operating characteristics in the high blood flow range (4 L/min–8 L/min). However, during low-flow applications in the range of 0.5 L/min–2 L/min, adverse events such as increased hemolysis, platelet activation and bleeding complications are reported frequently. Methods In this study, the hemolysis of the centrifugal pump DP3 is evaluated both in vitro and in silico, comparing the low-flow operation at 1 L/min to the high-flow operation at 4 L/min. Results Increased hemolysis occurs at low-flow, both in vitro and in silico. The in-vitro experiments present a sixfold higher relative increased hemolysis at low-flow. Compared to high-flow operation, a more than 3.5-fold increase in blood recirculation within the pump head can be observed in the low-flow range in silico. Conclusions This study highlights the underappreciated hemolysis in centrifugal pumps within the low-flow range, i.e. during pediatric ECMO or ECCO2R treatment. The in-vitro results of hemolysis and the in-silico computational fluid dynamic simulations of flow paths within the pumps raise awareness about blood damage that occurs when using centrifugal pumps at low-flow operating points. These findings underline the urgent need for a specific pump optimized for low-flow treatment. Due to the inherent problems of available centrifugal pumps in the low-flow range, clinicians should use the current centrifugal pumps with caution, alternatively other pumping principles such as positive displacement pumps may be discussed in the future

Effect of isolated intracranial hypertension on cerebral perfusion within the phase of primary disturbances after subarachnoid hemorrhage in rats

IntroductionElevated intracranial pressure (ICP) and blood components are the main trigger factors starting the complex pathophysiological cascade following subarachnoid hemorrhage (SAH). It is not clear whether they independently contribute to tissue damage or whether their impact cannot be differentiated from each other. We here aimed to establish a rat intracranial hypertension model that allows distinguishing the effects of these two factors and investigating the relationship between elevated ICP and hypoperfusion very early after SAH.MethodsBlood or four different types of fluids [gelofusine, silicone oil, artificial cerebrospinal fluid (aCSF), aCSF plus xanthan (CX)] were injected into the cisterna magna in anesthetized rats, respectively. Arterial blood pressure, ICP and cerebral blood flow (CBF) were continuously measured up to 6 h after injection. Enzyme-linked immunosorbent assays were performed to measure the pro-inflammatory cytokines interleukin 6 (IL-6) and tumor necrosis factor α (TNF-α) in brain cortex and peripheral blood.ResultsSilicone oil injection caused deaths of almost all animals. Compared to blood, gelofusine resulted in lower peak ICP and lower plateau phase. Artificial CSF reached a comparable ICP peak value but failed to reach the ICP plateau of blood injection. Injection of CX with comparable viscosity as blood reproduced the ICP course of the blood injection group. Compared with the CBF course after blood injection, CX induced a comparable early global ischemia within the first minutes which was followed by a prompt return to baseline level with no further hypoperfusion despite an equal ICP course. The inflammatory response within the tissue did not differ between blood or blood-substitute injection. The systemic inflammation was significantly more pronounced in the CX injection group compared with the other fluids including blood.DiscussionBy cisterna magna injection of blood substitution fluids, we established a subarachnoid space occupying rat model that exactly mimicked the course of ICP in the first 6 h following blood injection. Fluids lacking blood components did not induce the typical prolonged hypoperfusion occurring after blood-injection in this very early phase. Our study strongly suggests that blood components rather than elevated ICP play an important role for early hypoperfusion events in SAH

Design, manufacturing and testing of a green non-isocyanate polyurethane prosthetic heart valve.

peer reviewedThe sole effective treatment for most patients with heart valve disease is valve replacement by implantation of mechanical or biological prostheses. However, mechanical valves represent high risk of thromboembolism, and biological prostheses are prone to early degeneration. In this work, we aim to determine the potential of novel environmentally-friendly non-isocyanate polyurethanes (NIPUs) for manufacturing synthetic prosthetic heart valves. Polyhydroxyurethane (PHU) NIPUs are synthesized via an isocyanate-free route, tested in vitro, and used to produce aortic valves. PHU elastomers reinforced with a polyester mesh show mechanical properties similar to native valve leaflets. These NIPUs do not cause hemolysis. Interestingly, both platelet adhesion and contact activation-induced coagulation are strongly reduced on NIPU surfaces, indicating low thrombogenicity. Fibroblasts and endothelial cells maintain normal growth and shape after indirect contact with NIPUs. Fluid-structure interaction (FSI) allows modeling of the ideal valve design, with minimal shear stress on the leaflets. Injection-molded valves are tested in a pulse duplicator and show ISO-compliant hydrodynamic performance, comparable to clinically-used bioprostheses. Poly(tetrahydrofuran) (PTHF)-NIPU patches do not show any evidence of calcification over a period of 8 weeks. NIPUs are promising sustainable biomaterials for the manufacturing of improved prosthetic valves with low thrombogenicity

Ensemble-based stochastic permeability and flow simulation of a sparsely sampled hard-rock aquifer supported by high performance computing

Calibrating the heterogeneous permeability distribution of hard-rock aquifers based on sparse data is challenging but crucial for obtaining meaningful groundwater flow models. This study demonstrates the applicability of stochastic sampling of the prior permeability distribution and Metropolis sampling of the posterior permeability distribution using typical production data and measurements available in the context of groundwater abstraction. The case study is the Hastenrather Graben groundwater abstraction site near Aachen, Germany. A three-dimensional numerical flow model for the Carboniferous hard-rock aquifer is presented. Monte Carlo simulations are performed, for generating 1,000 realizations of the heterogeneous hard-rock permeability field, applying Sequential Gaussian Simulation based on nine log-permeability values for the geostatistical simulation. Forward simulation of flow during a production test for each realization results in the prior ensemble of model states verified by observation data in four wells. The computationally expensive ensemble simulations were performed in parallel with the simulation code SHEMAT-Suite on the high-performance computer JURECA. Applying a Metropolis sampler based on the misfit between drawdown simulations and observations results in a posterior ensemble comprising 251 realizations. The posterior mean log-permeability is −11.67 with an uncertainty of 0.83. The corresponding average posterior uncertainty of the drawdown simulation is 1.1 m. Even though some sources of uncertainty (e.g. scenario uncertainty) remain unquantified, this study is an important step towards an entire uncertainty quantification for a sparsely sampled hard-rock aquifer. Further, it provides a real-case application of stochastic hydrogeological approaches demonstrating how to accomplish uncertainty quantification of subsurface flow models in practice.Helmholtz-Gemeinschaft http://dx.doi.org/10.13039/50110000165

Ensemble-based stochastic permeability and flow simulation of a sparsely sampled hard-rock aquifer supported by high performance computing

Calibrating the heterogeneous permeability distribution of hard-rock aquifers based on sparse data is challenging but crucial for obtaining meaningful groundwater flow models. This study demonstrates the applicability of stochastic sampling of the prior permeability distribution and Metropolis sampling of the posterior permeability distribution using typical production data and measurements available in the context of groundwater abstraction. The case study is the Hastenrather Graben groundwater abstraction site near Aachen, Germany. A three-dimensional numerical flow model for the Carboniferous hard-rock aquifer is presented. Monte Carlo simulations are performed, for generating 1,000 realizations of the heterogeneous hard-rock permeability field, applying Sequential Gaussian Simulation based on nine log-permeability values for the geostatistical simulation. Forward simulation of flow during a production test for each realization results in the prior ensemble of model states verified by observation data in four wells. The computationally expensive ensemble simulations were performed in parallel with the simulation code SHEMAT-Suite on the high-performance computer JURECA. Applying a Metropolis sampler based on the misfit between drawdown simulations and observations results in a posterior ensemble comprising 251 realizations. The posterior mean log-permeability is −11.67 with an uncertainty of 0.83. The corresponding average posterior uncertainty of the drawdown simulation is 1.1 m. Even though some sources of uncertainty (e.g. scenario uncertainty) remain unquantified, this study is an important step towards an entire uncertainty quantification for a sparsely sampled hard-rock aquifer. Further, it provides a real-case application of stochastic hydrogeological approaches demonstrating how to accomplish uncertainty quantification of subsurface flow models in practice