Ultraprotective versus apneic ventilation in acute respiratory distress syndrome patients with extracorporeal membrane oxygenation: a physiological study.

BACKGROUND: Even an ultraprotective ventilation strategy in severe acute respiratory distress syndrome (ARDS) patients treated with extracorporeal membrane oxygenation (ECMO) might induce ventilator-induced lung injury and apneic ventilation with the sole application of positive end-expiratory pressure may, therefore, be an alternative ventilation strategy. We, therefore, compared the effects of ultraprotective ventilation with apneic ventilation on oxygenation, oxygen delivery, respiratory system mechanics, hemodynamics, strain, air distribution and recruitment of the lung parenchyma in ARDS patients with ECMO. METHODS: In a prospective, monocentric physiological study, 24 patients with severe ARDS managed with ECMO were ventilated using ultraprotective ventilation (tidal volume 3 ml/kg of predicted body weight) with a fraction of inspired oxygen (FiO2) of 21%, 50% and 90%. Patients were then treated with apneic ventilation with analogous FiO2. The primary endpoint was the effect of the ventilation strategy on oxygenation and oxygen delivery. The secondary endpoints were mechanical power, stress, regional air distribution, lung recruitment and the resulting strain, evaluated by chest computed tomography, associated with the application of PEEP (apneic ventilation) and/or low VT (ultraprotective ventilation). RESULTS: Protective ventilation, compared to apneic ventilation, improved oxygenation (arterial partial pressure of oxygen, p < 0.001 with FiO2 of 50% and 90%) and reduced cardiac output. Both ventilation strategies preserved oxygen delivery independent of the FiO2. Protective ventilation increased driving pressure, stress, strain, mechanical power, as well as induced additional recruitment in the non-dependent lung compared to apneic ventilation. CONCLUSIONS: In patients with severe ARDS managed with ECMO, ultraprotective ventilation compared to apneic ventilation improved oxygenation, but increased stress, strain, and mechanical power. Apneic ventilation might be considered as one of the options in the initial phase of ECMO treatment in severe ARDS patients to facilitate lung rest and prevent ventilator-induced lung injury. TRIAL REGISTRATION: German Clinical Trials Register (DRKS00013967). Registered 02/09/2018. https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00013967 .

Probabilistic noninvasive prediction of wall properties of abdominal aortic aneurysms using Bayesian regression.

Multiple patient-specific parameters, such as wall thickness, wall strength, and constitutive properties, are required for the computational assessment of abdominal aortic aneurysm (AAA) rupture risk. Unfortunately, many of these quantities are not easily accessible and could only be determined by invasive procedures, rendering a computational rupture risk assessment obsolete. This study investigates two different approaches to predict these quantities using regression models in combination with a multitude of noninvasively accessible, explanatory variables. We have gathered a large dataset comprising tensile tests performed with AAA specimens and supplementary patient information based on blood analysis, the patients medical history, and geometric features of the AAAs. Using this unique database, we harness the capability of state-of-the-art Bayesian regression techniques to infer probabilistic models for multiple quantities of interest. After a brief presentation of our experimental results, we show that we can effectively reduce the predictive uncertainty in the assessment of several patient-specific parameters, most importantly in thickness and failure strength of the AAA wall. Thereby, the more elaborate Bayesian regression approach based on Gaussian processes consistently outperforms standard linear regression. Moreover, our study contains a comparison to a previously proposed model for the wall strength.

Material modeling of cardiac valve tissue: Experiments, constitutive analysis and numerical investigation.

A key element of the cardiac cycle of the human heart is the opening and closing of the four valves. However, the material properties of the leaflet tissues, which fundamentally contribute to determine the mechanical response of the valves, are still an open field of research. The main contribution of the present study is to provide a complete experimental data set for porcine heart valve samples spanning all valve and leaflet types under tensile loading. The tests show a fair degree of reproducibility and are clearly indicative of a number of fundamental tissue properties, including a progressively stiffening response with increasing elongation. We then propose a simple anisotropic constitutive model, which is fitted to the experimental data set, showing a reasonable interspecimen variability. Furthermore, we present a dynamic finite element analysis of the aortic valve to show the direct usability of the obtained material parameters in computational simulations.

Towards efficient uncertainty quantification in complex and large-scale biomechanical problems based on a Bayesian multi-fidelity scheme.

In simulation of cardiovascular processes and diseases patient-specific model parameters, such as constitutive properties, are usually not easy to obtain. Instead of using population mean values to perform "patient-specific" simulations, thereby neglecting the inter- and intra-patient variations present in these parameters, these uncertainties have to be considered in the computational assessment. However, due to limited computational resources and several shortcomings of traditional uncertainty quantification approaches, parametric uncertainties, modeled as random fields, have not yet been considered in patient-specific, nonlinear, large-scale, and complex biomechanical applications. Hence, the purpose of this study is twofold. First, we present an uncertainty quantification framework based on multi-fidelity sampling and Bayesian formulations. The key feature of the presented method is the ability to rigorously exploit and incorporate information from an approximate, low fidelity model. Most importantly, response statistics of the corresponding high fidelity model can be computed accurately even if the low fidelity model provides only a very poor approximation. The approach merely requires that the low fidelity model and the corresponding high fidelity model share a similar stochastic structure, i.e., dependence on the random input. This results in a tremendous flexibility in choice of the approximate model. The flexibility and capabilities of the framework are demonstrated by performing uncertainty quantification using two patient-specific, large-scale, nonlinear finite element models of abdominal aortic aneurysms. One constitutive parameter of the aneurysmatic arterial wall is modeled as a univariate three-dimensional, non-Gaussian random field, thereby taking into account inter-patient as well as intra-patient variations of this parameter. We use direct Monte Carlo to evaluate the proposed method and found excellent agreement with this reference solution. Additionally, the employed approach results in a tremendous reduction of computational costs, rendering uncertainty quantification with complex patient-specific nonlinear biomechanical models practical for the first time. Second, we also analyze the impact of the uncertainty in the input parameter on mechanical quantities typically related to abdominal aortic aneurysm rupture potential such as von Mises stress, von Mises strain and strain energy. Thus, providing first estimates on the variability of these mechanical quantities due to an uncertain constitutive parameter, and revealing the potential error made by assuming population averaged mean values in patient-specific simulations of abdominal aortic aneurysms. Moreover, the influence of correlation length of the random field is investigated in a parameter study using MC.

Biomechanics and gene expression in abdominal aortic aneurysm.

OBJECTIVE: The aim of the study was to detect inter-relations between the mechanical conditions and material properties of abdominal aortic aneurysm (AAA) wall and the underlying local gene expression of destabilizing inflammatory, proteolytic, and structural factors. METHODS: During open surgery, 51 tissue samples from 31 AAA patients were harvested. Gene expression of collagen types I and III, inflammatory factors CD45 and MSR1, proteolytic enzymes matrix metalloproteinases 2 and 9, and tissue inhibitor of matrix metalloproteinase 1 was analyzed by reverse transcription-polymerase chain reaction. Material properties of corresponding AAA tissue samples were assessed by cyclic sinusoidal and destructive testing. Local mechanical conditions of stress and strain were determined by advanced nonlinear finite element analysis based on patient-specific three-dimensional AAA models derived from preoperative computed tomography data. RESULTS: In the AAA wall, all parameters analyzed were significantly expressed at the messenger RNA level. With respect to mechanical properties of the aneurysmatic wall, expression of collagen III correlated with the stiffness parameter α (r = -0.348; P = .017), and matrix metalloprotease 2 correlated with the stiffness parameter ß and wall strength (r = -0.438 and -0.593; P = .005 and P < .001). Furthermore, significant relationships were observed between local AAA diameter and the expression of CD45, MSR1, and tissue inhibitor of matrix metalloproteinase 1 (r = 0.285, 0.551, 0.328; P < .05). However, we found no inter-relation of local calculated wall stresses and strains with gene expression. CONCLUSIONS: Our results show for the first time that gene expressions of destabilizing factors within AAA tissue might be correlated to geometric and mechanical properties of the AAA wall. However, we found no influence of local mechanical conditions on gene expression of these factors. Therefore, these preliminary results are still ambiguous.