Search

Impaired cardiac function has been described as a frequent complication of COVID-19-related pneumonia. To investigate possible underlying mechanisms, we represented the cardiovascular system by means of a lumped-parameter 0D mathematical model. The model was calibrated using clinical data, recorded in 58 patients hospitalized for COVID-19-related pneumonia, to make it patient-specific and to compute model outputs of clinical interest related to the cardiocirculatory system. We assessed, for each patient with a successful calibration, the statistical reliability of model outputs estimating the uncertainty intervals. Then, we performed a statistical analysis to compare healthy ranges and mean values (over patients) of reliable model outputs to determine which were significantly altered in COVID-19-related pneumonia. Our results showed significant increases in right ventricular systolic pressure, diastolic and mean pulmonary arterial pressure, and capillary wedge pressure. Instead, physical quantities related to the systemic circulation were not significantly altered. Remarkably, statistical analyses made on raw clinical data, without the support of a mathematical model, were unable to detect the effects of COVID-19-related pneumonia in pulmonary circulation, thus suggesting that the use of a calibrated 0D mathematical model to describe the cardiocirculatory system is an effective tool to investigate the impairments of the cardiocirculatory system associated with COVID-19. [ABSTRACT FROM AUTHOR]

In this paper, we present a mathematical model capable of simulating the human cardiac function. We review the basic equations of the model, their coupling, the numerical approach for the computer solution of this mathematical model, and a few examples of application to specific problems of clinical interest. [ABSTRACT FROM AUTHOR]

Transcatheter aortic valve implantation (TAVI) is a minimally invasive intervention for the treatment of severe aortic valve stenosis. The main cause of failure is the structural deterioration of the implanted prosthetic leaflets, possibly inducing a valvular re‐stenosis 5–10 years after the implantation. Based solely on pre‐implantation data, the aim of this work is to identify fluid‐dynamics and structural indices that may predict the possible valvular deterioration, in order to assist the clinicians in the decision‐making phase and in the intervention design. Patient‐specific, pre‐implantation geometries of the aortic root, the ascending aorta, and the native valvular calcifications were reconstructed from computed tomography images. The stent of the prosthesis was modeled as a hollow cylinder and virtually implanted in the reconstructed domain. The fluid‐structure interaction between the blood flow, the stent, and the residual native tissue surrounding the prosthesis was simulated by a computational solver with suitable boundary conditions. Hemodynamical and structural indicators were analyzed for five different patients that underwent TAVI – three with prosthetic valve degeneration and two without degeneration – and the comparison of the results showed a correlation between the leaflets' structural degeneration and the wall shear stress distribution on the proximal aortic wall. This investigation represents a first step towards computational predictive analysis of TAVI degeneration, based on pre‐implantation data and without requiring additional peri‐operative or follow‐up information. Indeed, being able to identify patients more likely to experience degeneration after TAVI may help to schedule a patient‐specific timing of follow‐up. [ABSTRACT FROM AUTHOR]

We present a new model of human cardiac electromechanics for the left ventricle where electrophysiology is described by a Reaction–Eikonal model and which enables an off-line resolution of the reaction model, thus entailing a big saving of computational time. Subcellular dynamics is coupled with a model of tissue mechanics, which is in turn coupled with a Windkessel model for blood circulation. Our numerical results show that the proposed model is able to provide a physiological response to changes in certain variables (end-diastolic volume, total peripheral resistance, contractility). We also show that our model is able to reproduce with high accuracy and with a considerably lower computational time the results that we would obtain if the monodomain model should be used in place of the Eikonal model. [ABSTRACT FROM AUTHOR]

The importance of myocardial perfusion at the outset of cardiac disease remains largely understudied. To address this topic we present a mathematical model that considers the systemic circulation, the coronary vessels, the myocardium, and the interactions among these components. The core of the whole model is the description of the myocardium as a multicompartment poromechanics system. A novel decomposition of the poroelastic Helmholtz potential involved in the poromechanics model allows for a quasi-incompressible model that adequately describes the physical interaction among all components in the porous medium. We further provide a rigorous mathematical analysis that gives guidelines for the choice of the Helmholtz potential. To reduce the computational cost of our integrated model we propose decoupling the deformation of the tissue and systemic circulation from the porous flow in the myocardium and coronary vessels, which allows us to apply the model also in combination with precomputed cardiac displacements, obtained form other models or medical imaging data. We test the methodology through the simulation of a heartbeat in healthy conditions that replicates the systolic impediment phenomenon, which is particularly challenging to capture as it arises from the interaction of several parts of the model. [ABSTRACT FROM AUTHOR]

Hypertrophic Cardiomyopathy (HCM) is a pathological condition characterized by an abnormal thickening of the myocardium. When affecting the medio-basal portion of the septum, it is named Hypertrophic Obstructive Cardiomyopathy (HOCM) because it induces a flow obstruction in the left ventricular outflow tract. In any type of HCM, the myocardial function can become compromised, possibly resulting in cardiac death. In this study, we investigated with computational analysis the hemodynamics of patients with different types of HCM. The aim was quantifying the effects of this pathology on the intraventricular blood flow and pressure gradients, and providing information potentially useful to guide the indication and the modality of the surgical treatment (septal myectomy). We employed an image-based computational approach, integrating fluid dynamics simulations with geometric and functional data, reconstructed from standard cardiac cine-MRI acquisitions. We showed that with our approach we can better understand the patho-physiological behavior of intraventricular blood flow dynamics due to the abnormal morphological and functional aspect of the left ventricle. The main results of our investigation are: (a) a detailed patient-specific analysis of the blood velocity, pressure and stress distribution associated to HCM; (b) a computation-based classification of patients affected by HCM that can complement the current clinical guidelines for the diagnosis and treatment of HOCM. [ABSTRACT FROM AUTHOR]

In this special issue, we collect some recent works in this field. In the recent years, there have been remarkable developments in the field of numerical simulations of the cardiovascular system. In particular, the works presented here cover both methodological and applicative issues in two fields of great interest, such as cardiac electrophysiology and hemodynamics. [Extracted from the article]

Pulmonary valve replacement (PVR) consists of substituting a patient's original valve with a prosthetic one, primarily addressing pulmonary valve insufficiency, which is crucially relevant in Tetralogy of Fallot repairment. While extensive clinical and computational literature on aortic and mitral valve replacements is available, PVR's post‐procedural haemodynamics in the pulmonary artery and the impact of prosthetic valve dynamics remain significantly understudied. Addressing this gap, we introduce a reduced Fluid–Structure Interaction (rFSI) model, applied for the first time to the pulmonary valve. This model couples a three‐dimensional computational representation of pulmonary artery haemodynamics with a one‐degree‐of‐freedom model to account for valve structural mechanics. Through this approach, we analyse patient‐specific haemodynamics pre and post PVR. Patient‐specific geometries, reconstructed from CT scans, are virtually equipped with a template valve geometry. Boundary conditions for the model are established using a lumped‐parameter model, fine‐tuned based on clinical patient data. Our model accurately reproduces patient‐specific haemodynamic changes across different scenarios: pre‐PVR, six months post‐PVR, and a follow‐up condition after a decade. It effectively demonstrates the impact of valve implantation on sustaining the diastolic pressure gradient across the valve. The numerical results indicate that our valve model is able to reproduce overall physiological and/or pathological conditions, as preliminary assessed on two different patients. This promising approach provides insights into post‐PVR haemodynamics and prosthetic valve effects, shedding light on potential implications for patient‐specific outcomes. [ABSTRACT FROM AUTHOR]