An orifice shape-based reduced order model of patient-specific mitral valve regurgitation

Mitral valve regurgitation (MR) is one of the most prevalent valvular heart diseases. Its quantitative assessment is challenging but crucial for treatment decisions. Using computational fluid dynamics (CFD), we developed a reduced order model (ROM) describing the relationship between MR flow rates, transvalvular pressure differences, and the size and shape of the regurgitant valve orifice. Due to its low computational cost, this ROM could easily be implemented into clinical workflows to support the assessment of MR. We reconstructed mitral valves of 43 patients from 3D transesophageal echocardiographic images and estimated the 3D anatomic regurgitant orifice areas using a shrink-wrap algorithm. The orifice shapes were quantified with three dimensionless shape parameters. Steady-state CFD simulations in the reconstructed mitral valves were performed to analyse the relationship between the regurgitant orifice geometry and the regurgitant hemodynamics. Based on the results, three ROMs with increasing complexity were defined, all of which revealed very good agreement with CFD results with a mean bias below 3% for the MR flow rate. Classifying orifices into two shape groups and assigning group-specific flow coefficients in the ROM reduced the limit of agreement predicting regurgitant volumes from 9.0 ml to 5.7 ml at a mean regurgitant volume of 57 ml

Computational model of blood flow in the aorto-coronary bypass graft

BACKGROUND: Coronary artery bypass grafting surgery is an effective treatment modality for patients with severe coronary artery disease. The conduits used during the surgery include both the arterial and venous conduits. Long- term graft patency rate for the internal mammary arterial graft is superior, but the same is not true for the saphenous vein grafts. At 10 years, more than 50% of the vein grafts would have occluded and many of them are diseased. Why do the saphenous vein grafts fail the test of time? Many causes have been proposed for saphenous graft failure. Some are non-modifiable and the rest are modifiable. Non-modifiable causes include different histological structure of the vein compared to artery, size disparity between coronary artery and saphenous vein. However, researches are more interested in the modifiable causes, such as graft flow dynamics and wall shear stress distribution at the anastomotic sites. Formation of intimal hyperplasia at the anastomotic junction has been implicated as the root cause of long- term graft failure. Many researchers have analyzed the complex flow patterns in the distal sapheno-coronary anastomotic region, using various simulated model in an attempt to explain the site of preferential intimal hyperplasia based on the flow disturbances and differential wall stress distribution. In this paper, the geometrical bypass models (aorto-left coronary bypass graft model and aorto-right coronary bypass graft model) are based on real-life situations. In our models, the dimensions of the aorta, saphenous vein and the coronary artery simulate the actual dimensions at surgery. Both the proximal and distal anastomoses are considered at the same time, and we also take into the consideration the cross-sectional shape change of the venous conduit from circular to elliptical. Contrary to previous works, we have carried out computational fluid dynamics (CFD) study in the entire aorta-graft-perfused artery domain. The results reported here focus on (i) the complex flow patterns both at the proximal and distal anastomotic sites, and (ii) the wall shear stress distribution, which is an important factor that contributes to graft patency. METHODS: The three-dimensional coronary bypass models of the aorto-right coronary bypass and the aorto-left coronary bypass systems are constructed using computational fluid-dynamics software (Fluent 6.0.1). To have a better understanding of the flow dynamics at specific time instants of the cardiac cycle, quasi-steady flow simulations are performed, using a finite-volume approach. The data input to the models are the physiological measurements of flow-rates at (i) the aortic entrance, (ii) the ascending aorta, (iii) the left coronary artery, and (iv) the right coronary artery. RESULTS: The flow field and the wall shear stress are calculated throughout the cycle, but reported in this paper at two different instants of the cardiac cycle, one at the onset of ejection and the other during mid-diastole for both the right and left aorto-coronary bypass graft models. Plots of velocity-vector and the wall shear stress distributions are displayed in the aorto-graft-coronary arterial flow-field domain. We have shown (i) how the blocked coronary artery is being perfused in systole and diastole, (ii) the flow patterns at the two anastomotic junctions, proximal and distal anastomotic sites, and (iii) the shear stress distributions and their associations with arterial disease. CONCLUSION: The computed results have revealed that (i) maximum perfusion of the occluded artery occurs during mid-diastole, and (ii) the maximum wall shear-stress variation is observed around the distal anastomotic region. These results can enable the clinicians to have a better understanding of vein graft disease, and hopefully we can offer a solution to alleviate or delay the occurrence of vein graft disease

Simulation, Identification and Statistical Variation in Cardiovascular Analysis (SISCA) - a Software Framework for Multi-compartment Lumped Modeling

Modeling approaches that are suitable to cover a wide range of real world scenarios in cardiovascular physiology could not be obtained so far, because many of the system parameters are uncertain or even unknown at all. The natural variability and statistical variation of cardiovascular system parameters in healthy and diseased conditions is assumed to be one of the characteristic features to understand cardiovascular diseases in more detail. Within this paper, a novel software framework for statistical variation, system identification, patient-specific simulation in cardiovascular system modeling is described by a multi-model statistical ensemble approach using dimension reduced multi-compartment models. The data driven approach is applied to model pre- and post-surgery clinical data sets from a patient (13 years old, 148cm, female) diagnosed with coarctation of aorta (CoA). A patient specific structure was obtained by the remapping of geometric parameters according to available MRI and metadata. Characteristic periodic boundary conditions were generated from pressure measurements, the systemic resistances and mean flow conditions at the outlets were adjusted subject to a weighting scheme, while systemic compliance and viscous parameters were fitted by reweighting the elastic modulus to the peak flows and pressure levels of all compartments. Archived model parametrization reflects the post-treatment data set and was used as a prior for modeling the pathologic pre-treatment state. According to geometry data, the stenosis was implemented for CoA modeling, such that the pressure drop was reproduced. In both scenarios, the simulated flows and pressure amplitudes are correctly reproduced by the simulation and stenosis and stent treatment were adequately reflected. Furthermore, the pre-treatment cross stenosis phase shift of the pulse wave is fairly well reproduced by the simulation. However, significant decrease of unrealistic phase shifts within the measurements was observed in the post-treatment after stent implementation, which is assumed to raise from specific systemic or measurement conditions that occurred during surgery. While patient specific modeling heavily depends on an effective and highly versatile modeling process, the methods and results presented in this paper suggest that the conditioning and uncertainty management of routine clinical data sets have to be further improved to obtain reasonable results in patient-specific cardiovascular modeling

Treatment of the aortic coarctation: Prediction of the hemodynamic impact

Aortic coarctation (CoA) accounts for approximately 10% of congenital heart diseases 1. CoA causing high pressure gradient can be successfully treated surgical or catheter-based. Long-term results, however, revealed decreased life expectancy associated with abnormal hemodynamics 1. To develop a next-generation personalized diagnostic-prognostic tools allowing treatment optimization and thus to improve life expectance, the innovative combination of imaging science, biofluid mechanics, and computer modeling is necessary. Patient-specific computational fluid dynamics (CFD) models of the CoA based on MRI data were created to analyze pre- and post-treatment hemodynamics with a focus on pressure gradient

Impact of predictive medicine on therapeutic decision making: a randomized controlled trial in congenital heart disease

Computational modelling has made significant progress towards clinical application in recent years. In addition to providing detailed diagnostic data, these methods have the potential to simulate patient-specific interventions and to predict their outcome. Our objective was to evaluate to which extent patient-specific modelling influences treatment decisions in coarctation of the aorta (CoA), a common congenital heart disease. We selected three cases with CoA, two of which had borderline indications for intervention according to current clinical guidelines. The third case was not indicated for intervention according to guidelines. For each case, we generated two separate datasets. First dataset included conventional diagnostic parameters (echocardiography and magnetic resonance imaging). In the second, we added modelled parameters (pressure fields). For the two cases with borderline indications for intervention, the second dataset also included pressure fields after virtual stenting simulations. All parameters were computed by modelling methods that were previously validated. In an online-administered, invitation-only survey, we randomized 178 paediatric cardiologists to view either conventional (control) or add-on modelling (experimental) datasets. Primary endpoint was the proportion of participants recommending different therapeutic options: (1) surgery or catheter lab (collectively, “intervention”) or (2) no intervention (follow-up with or without medication). Availability of data from computational predictive modelling influenced therapeutic decision making in two of three cases. There was a statistically significant association between group assignment and the recommendation of an intervention for one borderline case and one non-borderline case: 94.3% vs. 72.2% (RR: 1.31, 95% CI: 1.14–1.50, p = 0.00) and 18.8% vs. 5.1% (RR: 3.09, 95% CI: 1.17–8.18, p = 0.01) of participants in the experimental and control groups respectively recommended an intervention. For the remaining case, there was no difference between the experimental and control group and the majority of participants recommended intervention. In sub-group analyses, findings were not affected by the experience level of participating cardiologists. Despite existing clinical guidelines, the therapy recommendations of the participating physicians were heterogeneous. Validated patient-specific computational modelling has the potential to influence treatment decisions. Future studies in broader areas are needed to evaluate whether differences in decisions result in improved outcomes (Trial Registration: NCT02700737)