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Peripheral venoarterial extracorporeal membrane oxygenation (VA-ECMO) is an advanced temporary life support system for patients with refractory cardiogenic shock or severe cardiopulmonary failure. However, the reperfusion of oxygenated blood into the arterial system via a peripheral artery will induce substantial hemodynamic changes that might contribute to the development of complications. In this study, we developed two types of computational models to quantify the hemodynamic changes induced by the peripheral VA-ECMO support for systolic heart failure (HF) of various severities. One was a lumped-parameter model used for exploring the optimal workload of extracorporeal membrane oxygenation (ECMO) for a specific severity of HF, whereas the other one was a geometrical multiscale model capable of simulating the detailed flow field in the aorta while accounting for the hemodynamic coupling of VA-ECMO with the cardiovascular system. Numerical results revealed that the retrograde transmission of ECMO-supplied blood flow toward the heart not only considerably inhibited cardiac output but also induced marked flow disturbance and regionally high or oscillatory wall shear stress (WSS) in the aorta that may increase the risk of thrombosis and vascular dysfunction. The major characteristics of flow disturbance and spatial distribution of abnormal WSS were codetermined by the cardiac function and workload of ECMO while less influenced by the morphology of aorta. These findings emphasized the importance of tuning the workload of ECMO based on patient-specific cardiac function to balance the amount of blood oxygenation support by ECMO against the risk of complications associated with hemodynamic abnormalities. [ABSTRACT FROM AUTHOR]

Clinical studies have extensively demonstrated that central aortic blood pressure (CABP) has greater clinical significance in comparison with peripheral blood pressure. Despite the existence of various techniques for noninvasively measuring CABP, the clinical applications of most techniques are hampered by the unsatisfactory accuracy or large variability in measurement errors. In this study, we proposed a new method for noninvasively estimating CABP with improved accuracy and reduced uncertain errors. The main idea was to optimize the estimation of the pulse wave transit time from the aorta to the occluded lumen of the brachial artery under a suprasystolic cuff by identifying and utilizing the characteristic information of the cuff oscillation wave, thereby improving the accuracy and stability of the CABP estimation algorithms under various physiological conditions. The method was firstly developed and verified based on large‐scale virtual subject data (n = 800) generated by a computational model of the cardiovascular system coupled to a brachial cuff, and then validated with small‐scale in vivo data (n = 34). The estimation errors for the aortic systolic pressure were −0.05 ± 0.63 mmHg in the test group of the virtual subjects and −1.09 ± 3.70 mmHg in the test group of the patients, both demonstrating a good performance. In particular, the estimation errors were found to be insensitive to variations in hemodynamic conditions and cardiovascular properties, manifesting the high robustness of the method. The method may have promising clinical applicability, although further validation studies with larger‐scale clinical data remain necessary. [ABSTRACT FROM AUTHOR]

Byline: Lijian. Xu, Yueqi. Zhu, Ruijian. Zhang, Tingzhun. Zhu, Jieqing. Wan, Fuyou. Liang, Bing. Zhao Background: Small intracranial aneurysms have a low risk of rupture. However, ruptured anterior communicating artery [...]

Aortic valve disease (AVD) often coexists with coronary artery disease (CAD), but whether and how the two diseases are correlated remains poorly understood. In this study, a zero–three dimensional (0-3D) multi-scale modeling method was developed to integrate coronary artery hemodynamics, aortic valve dynamics, coronary flow autoregulation mechanism, and systemic hemodynamics into a unique model system, thereby yielding a mathematical tool for quantifying the influences of aortic valve stenosis (AS) and aortic valve regurgitation (AR) on hemodynamics in large coronary arteries. The model was applied to simulate blood flows in six patient-specific left anterior descending coronary arteries (LADs) under various aortic valve conditions (i.e., control (free of AVD), AS, and AR). Obtained results showed that the space-averaged oscillatory shear index (SA-OSI) was significantly higher under the AS condition but lower under the AR condition in comparison with the control condition. Relatively, the overall magnitude of wall shear stress was less affected by AVD. Further data analysis revealed that AS induced the increase in OSI in LADs mainly through its role in augmenting the low-frequency components of coronary flow waveform. These findings imply that AS might increase the risk or progression of CAD by deteriorating the hemodynamic environment in coronary arteries. [ABSTRACT FROM AUTHOR]

In the present work, we construct a model of coronary flow, which utilizes both CT scans of large coronary arteries and coronary CT perfusion. The model describes pulsatile flow in the patient's network of coronary vessels and takes into account a number of physiological effects: myocardium contractions, stenoses, impairment of microvascular perfusion. The main novelty of this model is the new smooth boundary conditions that have not been used before in patient-specific simulations of coronary circulation. New boundary conditions use 0D lumped model approach and provide asymptotic convergence of the solution for the cases of one-to-one vascular connection and bifurcation with a very thin child vessel. The new boundary conditions make it possible to estimate the fractional flow margin more accurately. We also studied sensitivity of haemodynamic indices (fractional flow reserve, coronary flow reserve, instantaneous wave-free ratio) to the variations of microcirculation impairment. No substantial difference in sensitivity was observed between new model and original approach. The advantage of the presented approach is the availability of the required data in everyday clinical practice and, thus, improved personalization of the model. [ABSTRACT FROM AUTHOR]