Giorgia M. Bosi

Patient-specific simulations of transcatheter aortic valve stent implantation

Transcatheter aortic valve implantation (TAVI) enables treatment of aortic stenosis with no need for open heart surgery. According to current guidelines, only patients considered at high surgical risk can be treated with TAVI. In this study, patient-specific analyses were performed to explore the feasibility of TAVI in morphologies, which are currently borderline cases for a percutaneous approach. Five patients were recruited: four patients with failed bioprosthetic aortic valves (stenosis) and one patient with an incompetent, native aortic valve. Three-dimensional models of the implantation sites were reconstructed from computed tomography images. Within these realistic geometries, TAVI with an Edwards Sapien stent was simulated using finite element (FE) modelling. Engineering and clinical outcomes were assessed. In all patients, FE analysis proved that TAVI was morphologically feasible. After the implantation, stress distribution showed no risks of immediate device failure and geometric orifice areas increased with low risk of obstruction of the coronary arteries. Maximum principal stresses in the arterial walls were higher in the model with native outflow tract. FE analyses can both refine patient selection and characterise device mechanical performance in TAVI, overall impacting on procedural safety in the early introduction of percutaneous heart valve devices in new patient populations.

Computational modelling for congenital heart disease: how far are we from clinical translation?

Computational models of congenital heart disease (CHD) have become increasingly sophisticated over the last 20 years. They can provide an insight into complex flow phenomena, allow for testing devices into patient-specific anatomies (pre-CHD or post-CHD repair) and generate predictive data. This has been applied to different CHD scenarios, including patients with single ventricle, tetralogy of Fallot, aortic coarctation and transposition of the great arteries. Patient-specific simulations have been shown to be informative for preprocedural planning in complex cases, allowing for virtual stent deployment. Novel techniques such as statistical shape modelling can further aid in the morphological assessment of CHD, risk stratification of patients and possible identification of new ‘shape biomarkers’. Cardiovascular statistical shape models can provide valuable insights into phenomena such as ventricular growth in tetralogy of Fallot, or morphological aortic arch differences in repaired coarctation. In a constant move towards more realistic simulations, models can also account for multiscale phenomena (eg, thrombus formation) and importantly include measures of uncertainty (ie, CIs around simulation results). While their potential to aid understanding of CHD, surgical/procedural decision-making and personalisation of treatments is undeniable, important elements are still lacking prior to clinical translation of computational models in the field of CHD, that is, large validation studies, cost-effectiveness evaluation and establishing possible improvements in patient outcomes.

Patient‐specific finite element models to support clinical decisions: A lesson learnt from a case study of percutaneous pulmonary valve implantation

Patient‐specific finite element (FE) simulations were used to assess different transcatheter valve devices and help select the most appropriate treatment strategy for a patient (17‐year‐old male) with borderline dimensions for Melody® percutaneous pulmonary valve implantation (PPVI).

Comparison and calibration of a real-time virtual stenting algorithm using Finite Element Analysis and Genetic Algorithms

In this paper, we perform a comparative analysis between two computational methods for virtual stent deployment: a novel fast virtual stenting method, which is based on a spring–mass model, is compared with detailed finite element analysis in a sequence of in silico experiments. Given the results of the initial comparison, we present a way to optimise the fast method by calibrating a set of parameters with the help of a genetic algorithm, which utilises the outcomes of the finite element analysis as a learning reference. As a result of the calibration phase, we were able to substantially reduce the force measure discrepancy between the two methods and validate the fast stenting method by assessing the differences in the final device configurations.

Can finite element models of ballooning procedures yield mechanical response of the cardiovascular site to overexpansion

Patient-specific numerical models could aid the decision-making process for percutaneous valve selection; in order to be fully informative, they should include patient-specific data of both anatomy and mechanics of the implantation site. This information can be derived from routine clinical imaging during the cardiac cycle, but data on the implantation site mechanical response to device expansion are not routinely available. We aim to derive the implantation site response to overexpansion by monitoring pressure/dimensional changes during balloon sizing procedures and by applying a reverse engineering approach using a validated computational balloon model. This study presents the proof of concept for such computational framework tested in-vitro. A finite element (FE) model of a PTS-X405 sizing balloon (NuMed, Inc., USA) was created and validated against bench tests carried out on an ad hoc experimental apparatus: first on the balloon alone to replicate free expansion; second on the inflation of the balloon in a rapid prototyped cylinder with material deemed suitable for replicating pulmonary arteries in order to validate balloon/implantation site interaction algorithm. Finally, the balloon was inflated inside a compliant rapid prototyped patient-specific right ventricular outflow tract to test the validity of the approach. The corresponding FE simulation was set up to iteratively infer the mechanical response of the anatomical model. The test in this simplified condition confirmed the feasibility of the proposed approach and the potential for this methodology to provide patient-specific information on mechanical response of the implantation site when overexpanded, ultimately for more realistic computational simulations in patient-specific settings.

Patient-specific simulations for planning treatment in congenital heart disease

Patient-specific computational models have been extensively developed over the last decades and applied to investigate a wide range of cardiovascular problems. However, translation of these technologies into clinical applications, such as planning of medical procedures, has been limited to a few single case reports. Hence, the use of patient-specific models is still far from becoming a standard of care in clinical practice. The aim of this study is to describe our experience with a modelling framework that allows patient-specific simulations to be used for prediction of clinical outcomes. A cohort of 12 patients with congenital heart disease who were referred for percutaneous pulmonary valve implantation, stenting of aortic coarctation and surgical repair of double-outlet right ventricle was included in this study. Image data routinely acquired for clinical assessment were post-processed to set up patient-specific models and test device implantation and surgery. Finite-element and computational fluid dynamics analyses were run to assess feasibility of each intervention and provide some guidance. Results showed good agreement between simulations and clinical decision including feasibility, device choice and fluid-dynamic parameters. The promising results of this pilot study support translation of computer simulations as tools for personalization of cardiovascular treatments.

Population-Specific Material Properties of the Implantation Site for Transcatheter Aortic Valve Replacement Finite Element Simulations

Patient-specific computational models are an established tool to support device development and test under clinically relevant boundary conditions. Potentially, such models could be used to aid the clinical decision-making process for percutaneous valve selection; however, their adoption in clinical practice is still limited to individual cases. To be fully informative, they should include patient-specific data on both anatomy and mechanics of the implantation site. In this work, fourteen patient-specific computational models for transcatheter aortic valve replacement (TAVR) with balloon-expandable Sapien XT devices were retrospectively developed to tune the material parameters of the implantation site mechanical model for the average TAVR population. Pre-procedural computed tomography (CT) images were post-processed to create the 3D patient-specific anatomy of the implantation site. Balloon valvuloplasty and device deployment were simulated with finite element (FE) analysis. Valve leaflets and aortic root were modelled as linear elastic materials, while calcification as elastoplastic. Material properties were initially selected from literature; then, a statistical analysis was designed to investigate the effect of each implantation site material parameter on the implanted stent diameter and thus identify the combination of material parameters for TAVR patients. These numerical models were validated against clinical data. The comparison between stent diameters measured from post-procedural fluoroscopy images and final computational results showed a mean difference of 2.5 ± 3.9%. Moreover, the numerical model detected the presence of paravalvular leakage (PVL) in 79% of cases, as assessed by post-TAVR echocardiographic examination. The final aim was to increase accuracy and reliability of such computational tools for prospective clinical applications.

Finite Element Analysis to Study Percutaneous Heart Valves

Percutaneous valve implantation is an innovative, successful alternative to open-heart surgery for the treatment of both pulmonary and aortic heart valve dysfunction (Bonhoeffer et al., 2000; Cribier et al., 2002; Leon et al., 2011; Lurz et al., 2008; McElhinney DB et al., 2010; Rodes-Cabau et al., 2010; Smith et al., 2011; Vahanian et al., 2008). However, this minimallyinvasive procedure still presents limitations related to device design: stent fracture, availability to a limited group of patients with very specific anatomy and conditions, and positioning and anchoring issues (Delgado et al., 2010; Nordmeyer et al., 2007; Schievano et al., 2007a). Computational simulations (Taylor & Figueroa, 2009), together with advanced cardiovascular imaging techniques can be used to help understand these limitations in order to guide the optimisation process for new device designs, to improve the success of percutaneous valve implantation, and ultimately to broaden the range of patients who could benefit from these procedures.

Computational Fluid Dynamic Analysis of the Left Atrial Appendage to Predict Thrombosis Risk

During Atrial Fibrillation (AF) more than 90% of the left atrial thrombi responsible for thromboembolic events originate in the left atrial appendage (LAA), a complex small sac protruding from the left atrium (LA). Current available treatments to prevent thromboembolic events are oral anticoagulation, surgical LAA exclusion, or percutaneous LAA occlusion. However, the mechanism behind thrombus formation in the LAA is poorly understood. The aim of this work is to analyse the hemodynamic behaviour in four typical LAA morphologies - “Chicken wing”, “Cactus”, “Windsock” and “Cauliflower” - to identify potential relationships between the different shapes and the risk of thrombotic events. Computerised tomography (CT) images from four patients with no LA pathology were segmented to derive the 3D anatomical shape of LAA and LA. Computational Fluid Dynamic (CFD) analyses based on the patient-specific anatomies were carried out imposing both healthy and AF flow conditions. Velocity and shear strain rate (SSR) were analysed for all cases. Residence time in the different LAA regions was estimated with a virtual contrast agent washing out. CFD results indicate that both velocity and SSR decrease along the LAA, from the ostium to the tip, at each instant in the cardiac cycle, thus making the LAA tip more prone to fluid stagnation, and therefore to thrombus formation. Velocity and SSR also decrease from normal to AF conditions. After four cardiac cycles, the lowest washout of contrast agent was observed for the Cauliflower morphology (3.27% of residual contrast in AF), and the highest for the Windsock (0.56% of residual contrast in AF). This suggests that the former is expected to be associated with a higher risk of thrombosis, in agreement with clinical reports in the literature. The presented computational models highlight the major role played by the LAA morphology on the hemodynamics, both in normal and AF conditions, revealing the potential support that numerical analyses can provide in the stratification of patients under risk of thrombus formation, towards personalised patient care.

Patient-Specific Simulations in Interventional Cardiology Practice: Early Results From a Clinical/Engineering Center

Patient-specific models have been recently applied to investigate a wide range of cardiovascular problems including cardiac mechanics, hemodynamic conditions and structural interaction with devices [1]. The development of dedicated computational tools which combined the advances in the field of image elaboration, finite element (FE) and computational fluid-dynamic (CFD) analyses has greatly supported not only the understanding of human physiology and pathology, but also the improvement of specific interventions taking into account realistic conditions [2, 3]. However, the translation of these technologies into clinical applications is still a major challenge for the engineering modeling community, which has to compromise between numerical accuracy and response time in order to meet the clinical needs [4]. Hence, the validation of in silico against in vivo results is crucial. Finally, if the development of novel tools has recently attracted big investments [5], it has not been similarly easy to dedicate funds and time to test the developed technologies on large numbers of patient cases.Copyright