Effect of biomaterial stiffness on cardiac mechanics in a biventricular infarcted rat heart model with microstructural representation of in situ intramyocardial injectate.

Intramyocardial delivery of biomaterials is a promising concept for treating myocardial infarction. The delivered biomaterial provides mechanical support and attenuates wall thinning and elevated wall stress in the infarct region. This study aimed at developing a biventricular finite element model of an infarcted rat heart with a microstructural representation of an in situ biomaterial injectate, and a parametric investigation of the effect of the injectate stiffness on the cardiac mechanics. A three-dimensional subject-specific biventricular finite element model of a rat heart with left ventricular infarct and microstructurally dispersed biomaterial delivered 1 week after infarct induction was developed from ex vivo microcomputed tomography data. The volumetric mesh density varied between 303 mm-3 in the myocardium and 3852 mm-3 in the injectate region due to the microstructural intramyocardial dispersion. Parametric simulations were conducted with the injectate's elastic modulus varying from 4.1 to 405,900 kPa, and myocardial and injectate strains were recorded. With increasing injectate stiffness, the end-diastolic median myocardial fibre and cross-fibre strain decreased in magnitude from 3.6% to 1.1% and from -6.0% to -2.9%, respectively. At end-systole, the myocardial fibre and cross-fibre strain decreased in magnitude from -20.4% to -11.8% and from 6.5% to 4.6%, respectively. In the injectate, the maximum and minimum principal strains decreased in magnitude from 5.4% to 0.001% and from -5.4% to -0.001%, respectively, at end-diastole and from 38.5% to 0.06% and from -39.0% to -0.06%, respectively, at end-systole. With the microstructural injectate geometry, the developed subject-specific cardiac finite element model offers potential for extension to cellular injectates and in silico studies of mechanotransduction and therapeutic signalling in the infarcted heart with an infarct animal model extensively used in preclinical research.

Effect of contact force on pulsed field ablation lesions in porcine cardiac tissue.

INTRODUCTION: Contact force has been used to titrate lesion formation for radiofrequency ablation. Pulsed field ablation (PFA) is a field-based ablation technology for which limited evidence on the impact of contact force on lesion size is available. METHODS: Porcine hearts (n = 6) were perfused using a modified Langendorff set-up. A prototype focal PFA catheter attached to a force gauge was held perpendicular to the epicardium and lowered until contact was made. Contact force was recorded during each PFA delivery. Matured lesions were cross-sectioned, stained, and the lesion dimensions measured. RESULTS: A total of 82 lesions were evaluated with contact forces between 1.3 and 48.6 g. Mean lesion depth was 4.8 ± 0.9 mm (standard deviation), mean lesion width was 9.1 ± 1.3 mm, and mean lesion volume was 217.0 ± 96.6 mm3 . Linear regression curves showed an increase of only 0.01 mm in depth (depth = 0.01 × contact force + 4.41, R2 = 0.05), 0.03 mm in width (width = 0.03 × contact force + 8.26, R2 = 0.13) for each additional gram of contact force, and 2.20 mm3 in volume (volume = 2.20 × contact force + 162, R2 = 0.10). CONCLUSION: Increasing contact force using a bipolar, biphasic focal PFA system has minimal effects on acute lesion dimensions in an isolated porcine heart model and achieving tissue contact is more important than the force with which that contact is made.

Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics.

An understanding of left ventricle (LV) mechanics is fundamental for designing better preventive, diagnostic, and treatment strategies for improved heart function. Because of the costs of clinical and experimental studies to treat and understand heart function, respectively, in-silico models play an important role. Finite element (FE) models, which have been used to create in-silico LV models for different cardiac health and disease conditions, as well as cardiac device design, are time-consuming and require powerful computational resources, which limits their use when real-time results are needed. As an alternative, we sought to use deep learning (DL) for LV in-silico modeling. We used 80 four-chamber heart FE models for feed forward, as well as recurrent neural network (RNN) with long short-term memory (LSTM) models for LV pressure and volume. We used 120 LV-only FE models for training LV stress predictions. The active material properties of the myocardium and time were features for the LV pressure and volume training, and passive material properties and element centroid coordinates were features of the LV stress prediction models. For six test FE models, the DL error for LV volume was 1.599 ± 1.227 ml, and the error for pressure was 1.257 ± 0.488 mmHg; for 20 LV FE test examples, the mean absolute errors were, respectively, 0.179 ± 0.050 for myofiber, 0.049 ± 0.017 for cross-fiber, and 0.039 ± 0.011 kPa for shear stress. After training, the DL runtime was in the order of seconds whereas equivalent FE runtime was in the order of several hours (pressure and volume) or 20 min (stress). We conclude that using DL, LV in-silico simulations can be provided for applications requiring real-time results.

Impact of Aortic Stenosis on Myofiber Stress: Translational Application of Left Ventricle-Aortic Coupling Simulation.

The severity of aortic stenosis (AS) has traditionally been graded by measuring hemodynamic parameters of transvalvular pressure gradient, ejection jet velocity, or estimating valve orifice area. Recent research has highlighted limitations of these criteria at effectively grading AS in presence of left ventricle (LV) dysfunction. We hypothesized that simulations coupling the aorta and LV could provide meaningful insight into myocardial biomechanical derangements that accompany AS. A realistic finite element model of the human heart with a coupled lumped-parameter circulatory system was used to simulate AS. Finite element analysis was performed with Abaqus FEA. An anisotropic hyperelastic model was assigned to LV passive properties, and a time-varying elastance function governed the LV active response. Global LV myofiber peak systolic stress (mean ± standard deviation) was 9.31 ± 10.33 kPa at baseline, 13.13 ± 10.29 kPa for moderate AS, and 16.18 ± 10.59 kPa for severe AS. Mean LV myofiber peak systolic strains were -22.40 ± 8.73%, -22.24 ± 8.91%, and -21.97 ± 9.18%, respectively. Stress was significantly elevated compared to baseline for moderate (p < 0.01) and severe AS (p < 0.001), and when compared to each other (p < 0.01). Ventricular regions that experienced the greatest systolic stress were (severe AS vs. baseline) basal inferior (39.87 vs. 30.02 kPa; p < 0.01), mid-anteroseptal (32.29 vs. 24.79 kPa; p < 0.001), and apex (27.99 vs. 23.52 kPa; p < 0.001). This data serves as a reference for future studies that will incorporate patient-specific ventricular geometries and material parameters, aiming to correlate LV biomechanics to AS severity.

In-silico study of the cardiac arrhythmogenic potential of biomaterial injection therapy.

Biomaterial injection is a novel therapy to treat ischemic heart failure (HF) that has shown to reduce remodeling and restore cardiac function in recent preclinical studies. While the effect of biomaterial injection in reducing mechanical wall stress has been recently demonstrated, the influence of biomaterials on the electrical behavior of treated hearts has not been elucidated. In this work, we developed computational models of swine hearts to study the electrophysiological vulnerability associated with biomaterial injection therapy. The propagation of action potentials on realistic biventricular geometries was simulated by numerically solving the monodomain electrophysiology equations on anatomically-detailed models of normal, HF untreated, and HF treated hearts. Heart geometries were constructed from high-resolution magnetic resonance images (MRI) where the healthy, peri-infarcted, infarcted and gel regions were identified, and the orientation of cardiac fibers was informed from diffusion-tensor MRI. Regional restitution properties in each case were evaluated by constructing a probability density function of the action potential duration (APD) at different cycle lengths. A comparative analysis of the ventricular fibrillation (VF) dynamics for every heart was carried out by measuring the number of filaments formed after wave braking. Our results suggest that biomaterial injection therapy does not affect the regional dispersion of repolarization when comparing untreated and treated failing hearts. Further, we found that the treated failing heart is more prone to sustain VF than the normal heart, and is at least as susceptible to sustained VF as the untreated failing heart. Moreover, we show that the main features of VF dynamics in a treated failing heart are not affected by the level of electrical conductivity of the biogel injectates. This work represents a novel proof-of-concept study demonstrating the feasibility of computer simulations of the heart in understanding the arrhythmic behavior in novel therapies for HF.

Intra-myocardial alginate hydrogel injection acts as a left ventricular mid-wall constraint in swine.

Despite positive initial outcomes emerging from preclinical and early clinical investigation of alginate hydrogel injection therapy as a treatment for heart failure, the lack of knowledge about the mechanism of action remains a major shortcoming that limits the efficacy of treatment design. To identify the mechanism of action, we examined previously unobtainable measurements of cardiac function from in vivo, ex vivo, and in silico states of clinically relevant heart failure (HF) in large animals. High-resolution ex vivo magnetic resonance imaging and histological data were used along with state-of-the-art subject-specific computational model simulations. Ex vivo data were incorporated in detailed geometric computational models for swine hearts in health (n = 5), ischemic HF (n = 5), and ischemic HF treated with alginate hydrogel injection therapy (n = 5). Hydrogel injection therapy mitigated elongation of sarcomere lengths (1.68 ± 0.10µm [treated] vs. 1.78 ± 0.15µm [untreated], p<0.001). Systolic contractility in treated animals improved substantially (ejection fraction = 43.9 ± 2.8% [treated] vs. 34.7 ± 2.7% [untreated], p<0.01). The in silico models realistically simulated in vivo function with >99% accuracy and predicted small myofiber strain in the vicinity of the solidified hydrogel that was sustained for up to 13 mm away from the implant. These findings suggest that the solidified alginate hydrogel material acts as an LV mid-wall constraint that significantly reduces adverse LV remodeling compared to untreated HF controls without causing negative secondary outcomes to cardiac function. STATEMENT OF SIGNIFICANCE: Heart failure is considered a growing epidemic and hence an important health problem in the US and worldwide. Its high prevalence (5.8 million and 23 million, respectively) is expected to increase by 25% in the US alone by 2030. Heart failure is associated with high morbidity and mortality, has a 5-year mortality rate of 50%, and contributes considerably to the overall cost of health care ($53.1 billion in the US by 2030). Despite positive initial outcomes emerging from preclinical and early clinical investigation of alginate hydrogel injection therapy as a treatment for heart failure, the lack of knowledge concerning the mechanism of action remains a major shortcoming that limits the efficacy of treatment design. To understand the mechanism of action, we combined high-resolution ex vivo magnetic resonance imaging and histological data in swine with state-of-the-art subject-specific computational model simulations. The in silico models realistically simulated in vivo function with >99% accuracy and predicted small myofiber strain in the vicinity of the solidified hydrogel that was sustained for up to 13 mm away from the implant. These findings suggest that the solidified alginate hydrogel material acts as a left ventricular mid-wall constraint that significantly reduces adverse LV remodeling compared to untreated heart failure controls without causing negative secondary outcomes to cardiac function. Moreover, if the hydrogel can be delivered percutaneously rather than via the currently used open-chest procedure, this therapy may become routine for heart failure treatment. A minimally invasive procedure would be in the best interest of this patient population; i.e., one that cannot tolerate general anesthesia and surgery, and it would be significantly more cost-effective than surgery.

Cell focal adhesion clustering leads to decreased and homogenized basal strains.

The subendothelial matrix of the artery is a complex mechanical environment where endothelial cells respond to and affect changes upon the underlying substrate. Our recent work has demonstrated that endothelial cell strain heterogeneity increases on a more heterogeneous underlying subendothelial matrix, and these cells display increased focal adhesion presence on stiffer substrate areas. However, the impact of these grouped focal adhesions on endothelial cell strains has not been explored. Here, we use finite element modeling to investigate the effects of microscale stiffness heterogeneities and focal adhesion location and stiffness on endothelial cell strains. Shear stress applied to the apical cell layer demonstrated a minimal effect on cell strain values, while substrate stretch had a greater effect on cell strain in the cell-substrate model. The addition of focal adhesions into the computational model (cell-FA-substrate model) predicted a decrease and homogenization of the cell strains. For simulations including focal adhesions, stiffer and more distributed adhesions caused increased and more heterogeneous endothelial cell strains. Overall, our data indicate that cells may group focal adhesions to minimize and homogenize their basal strains.

Tricuspid Valve Regurgitation Decreases after MitraClip Implantation: Fluid Structure Interaction Simulation.

Untreated tricuspid valve regurgitation (TR) is associated with increased rates of mortality, morbidity, and hospitalization. Current pharmacological and surgical treatment options for TR are limited. MitraClip (MC), an edge-to-edge percutaneous intervention, has been reported to be effective for treatment of TR. The goal of this study was to examine the effects of MC position on TR, using a multiphysics fluid-structure-interaction (FSI) analysis. The computational set up included the tricuspid valve (TV), the chordae tendineae, the blood particles, and a tube that surrounded the leaflets and blood particles. The leaflets and chordae were modeled as hyperelastic materials, and blood was modeled using smoothed particle hydrodynamics. FSI analysis was conducted for blood flow through the closed valve for multiple simulations that account for normal, diseased, and treated conditions of the TV. To simulate the diseased TV, a group of chordae between septal and pulmonary leaflets were removed from the normal TV, which produced increased regurgitation. Four MC treated scenarios were considered: i) one MC near the annulus, ii) one MC approximately midway between the annulus and leaflet tip, iii) one MC near the leaflet tip, iv) two MCs: one approximately midway between the annulus and leaflet tip, and one close to the leaflet tip. The TR increased in diseased TV (7.5%) compared to normal TV (2.5%). All MC treated scenarios decreased TR. The MC located near the midway point between the annulus and leaflet tip led to largest decrease in TR (75.2% compared to the untreated condition). The MC located near the leaflet tip was associated with lowest reduction in TR (2.2% compared to the untreated condition). When two MCs were used, reduction in TR was relatively high (68.7%), but TR was not improved compared to the optimal single MC. MC caused high stresses in the vicinity of the clipping area in all conditions; the highest occurred when the MC was near the leaflet tips. Using a quantitative computational approach, we confirm previous clinical reports on the efficacy of MC for treatment of TR. The results of this study could lead to the design of more efficient MC interventions for TR.

Method for Calibration of Left Ventricle Material Properties Using Three-Dimensional Echocardiography Endocardial Strains.

We sought to calibrate mechanical properties of left ventricle (LV) based on three-dimensional (3D) speckle tracking echocardiographic imaging data recorded from 16 segments defined by American Heart Association (AHA). The in vivo data were used to create finite element (FE) LV and biventricular (BV) models. The orientation of the fibers in the LV model was rule based, but diffusion tensor magnetic resonance imaging (MRI) data were used for the fiber directions in the BV model. A nonlinear fiber-reinforced constitutive equation was used to describe the passive behavior of the myocardium, whereas the active tension was described by a model based on tissue contraction (Tmax). isight was used for optimization, which used abaqus as the forward solver (Simulia, Providence, RI). The calibration of passive properties based on the end diastolic pressure volume relation (EDPVR) curve resulted in relatively good agreement (mean error = -0.04 ml). The difference between the experimental and computational strains decreased after segmental strain metrics, rather than global metrics, were used for calibration: for the LV model, the mean difference reduced from 0.129 to 0.046 (circumferential) and from 0.076 to 0.059 (longitudinal); for the BV model, the mean difference nearly did not change in the circumferential direction (0.061) but reduced in the longitudinal direction from 0.076 to 0.055. The calibration of mechanical properties for myocardium can be improved using segmental strain metrics. The importance of realistic fiber orientation and geometry for modeling of the LV was shown.

Prediction of Left Ventricular Mechanics Using Machine Learning.

The goal of this paper was to provide a real-time left ventricular (LV) mechanics simulator using machine learning (ML). Finite element (FE) simulations were conducted for the LV with different material properties to obtain a training set. A hyperelastic fiber-reinforced material model was used to describe the passive behavior of the myocardium during diastole. The active behavior of the heart resulting from myofiber contractions was added to the passive tissue during systole. The active and passive properties govern the LV constitutive equation. These mechanical properties were altered using optimal Latin hypercube design of experiments to obtain training FE models with varied active properties (volume and pressure predictions) and varied passive properties (stress predictions). For prediction of LV pressures, we used eXtreme Gradient Boosting (XGboost) and Cubist, and XGBoost was used for predictions of LV pressures, volumes as well as LV stresses. The LV pressure and volume results obtained from ML were similar to FE computations. The ML results could capture the shape of LV pressure as well as LV pressure-volume loops. The results predicted by Cubist were smoother than those from XGBoost. The mean absolute errors were as follows: XGBoost volume: 1.734 ± 0.584 ml, XGBoost pressure: 1.544 ± 0.298 mmHg, Cubist volume: 1.495 ± 0.260 ml, Cubist pressure: 1.623 ± 0.191 mmHg, myofiber stress: 0.334 ± 0.228 kPa, cross myofiber stress: 0.075 ± 0.024 kPa, and shear stress: 0.050 ± 0.032 kPa. The simulation results show ML can predict LV mechanics much faster than the FE method. The ML model can be used as a tool to predict LV behavior. Training of our ML model based on a large group of subjects can improve its predictability for real world applications.

Intramyocardial Injections to De-Stiffen the Heart: A Subject-Specific in Silico Approach.

We hypothesized that minimally invasive injections of a softening agent at strategic locations in stiff myocardium could de-stiffen the left ventricle (LV) globally. Physics-based finite element models of the LV were created from LV echocardiography images and pressures recorded during experiments in four swine. Results confirmed animal models of LV softening by systemic agents. Regional de-stiffening of myocardium led to global de-stiffening of LV. The mathematical set up was used to design LV global de-stiffening by regional softening of myocardium. At an end diastolic pressure of 23 mmHg, when 8 ml of the free wall was covered by intramyocardial injections, end diastolic volume (EDV) increased by 15.0%, whereas an increase up to 11 ml due to intramyocardial injections in the septum and free wall led to a 26.0% increase in EDV. Although the endocardial intramyocardial injections occupied a lower LV wall volume, they led to an EDV (44 ml) that was equal compared to intramyocardial injections in the mid-wall (44 ml) and larger compared to intramyocardial injections in the epicardium (41 ml). Using an in silico set up, sites of regional myocardium de-stiffening could be planned in order to globally soften overly stiff LV in heart failure with preserved ejection fraction. This novel treatment is built on subject-specific data. Hypothesis-testing of these simulation findings in animal models is warranted.

Kinematic boundary conditions substantially impact in silico ventricular function.

Computational cardiac mechanical models, individualized to the patient, have the potential to elucidate the fundamentals of cardiac (patho-)physiology, enable non-invasive quantification of clinically significant metrics (eg, stiffness, active contraction, work), and anticipate the potential efficacy of therapeutic cardiovascular intervention. In a clinical setting, however, the available imaging resolution is often limited, which limits cardiac models to focus on the ventricles, without including the atria, valves, and proximal arteries and veins. In such models, the absence of surrounding structures needs to be accounted for by imposing realistic kinematic boundary conditions, which, for prognostic purposes, are preferably generic and thus non-image derived. Unfortunately, the literature on cardiac models shows no consistent approach to kinematically constrain the myocardium. The impact of different approaches (eg, fully constrained base, constrained epi-ring) on the predictive capacity of cardiac mechanical models has not been thoroughly studied. For that reason, this study first gives an overview of current approaches to kinematically constrain (bi) ventricular models. Next, we developed a patient-specific in silico biventricular model that compares well with literature and in vivo recorded strains. Alternative constraints were introduced to assess the influence of commonly used mechanical boundary conditions on both the predicted global functional behavior of the in-silico heart (cavity volumes, stroke volume, ejection fraction) and local strain distributions. Meaningful differences in global functioning were found between different kinematic anchoring strategies, which brought forward the importance of selecting appropriate boundary conditions for biventricular models that, in the near future, may inform clinical intervention. However, whilst statistically significant differences were also found in local strain distributions, these differences were minor and mostly confined to the region close to the applied boundary conditions.

Relationship of Transmural Variations in Myofiber Contractility to Left Ventricular Ejection Fraction: Implications for Modeling Heart Failure Phenotype With Preserved Ejection Fraction.

The pathophysiological mechanisms underlying preserved left ventricular (LV) ejection fraction (EF) in patients with heart failure and preserved ejection fraction (HFpEF) remain incompletely understood. We hypothesized that transmural variations in myofiber contractility with existence of subendocardial dysfunction and compensatory increased subepicardial contractility may underlie preservation of LVEF in patients with HFpEF. We quantified alterations in myocardial function in a mathematical model of the human LV that is based on the finite element method. The fiber-reinforced material formulation of the myocardium included passive and active properties. The passive material properties were determined such that the diastolic pressure-volume behavior of the LV was similar to that shown in published clinical studies of pressure-volume curves. To examine changes in active properties, we considered six scenarios: (1) normal properties throughout the LV wall; (2) decreased myocardial contractility in the subendocardium; (3) increased myocardial contractility in the subepicardium; (4) myocardial contractility decreased equally in all layers, (5) myocardial contractility decreased in the midmyocardium and subepicardium, (6) myocardial contractility decreased in the subepicardium. Our results indicate that decreased subendocardial contractility reduced LVEF from 53.2 to 40.5%. Increased contractility in the subepicardium recovered LVEF from 40.5 to 53.2%. Decreased contractility transmurally reduced LVEF and could not be recovered if subepicardial and midmyocardial contractility remained depressed. The computational results simulating the effects of transmural alterations in the ventricular tissue replicate the phenotypic patterns of LV dysfunction observed in clinical practice. In particular, data for LVEF, strain and displacement are consistent with previous clinical observations in patients with HFpEF, and substantiate the hypothesis that increased subepicardial contractility may compensate for subendocardial dysfunction and play a vital role in maintaining LVEF.

Investigating the Role of Interventricular Interdependence in Development of Right Heart Dysfunction During LVAD Support: A Patient-Specific Methods-Based Approach.

Predictive computation models offer the potential to uncover the mechanisms of treatments whose actions cannot be easily determined by experimental or imaging techniques. This is particularly relevant for investigating left ventricular mechanical assistance, a therapy for end-stage heart failure, which is increasingly used as more than just a bridge-to-transplant therapy. The high incidence of right ventricular failure following left ventricular assistance reflects an undesired consequence of treatment, which has been hypothesized to be related to the mechanical interdependence between the two ventricles. To investigate the implication of this interdependence specifically in the setting of left ventricular assistance device (LVAD) support, we introduce a patient-specific finite-element model of dilated chronic heart failure. The model geometry and material parameters were calibrated using patient-specific clinical data, producing a mechanical surrogate of the failing in vivo heart that models its dynamic strain and stress throughout the cardiac cycle. The model of the heart was coupled to lumped-parameter circulatory systems to simulate realistic ventricular loading conditions. Finally, the impact of ventricular assistance was investigated by incorporating a pump with pressure-flow characteristics of an LVAD (HeartMate II™ operating between 8 and 12 k RPM) in parallel to the left ventricle. This allowed us to investigate the mechanical impact of acute left ventricular assistance at multiple operating-speeds on right ventricular mechanics and septal wall motion. Our findings show that left ventricular assistance reduces myofiber stress in the left ventricle and, to a lesser extent, right ventricle free wall, while increasing leftward septal-shift with increased operating-speeds. These effects were achieved with secondary, potentially negative effects on the interventricular septum which showed that support from LVADs, introduces unnatural bending of the septum and with it, increased localized stress regions. Left ventricular assistance unloads the left ventricle significantly and shifts the right ventricular pressure-volume-loop toward larger volumes and higher pressures; a consequence of left-to-right ventricular interactions and a leftward septal shift. The methods and results described in the present study are a meaningful advancement of computational efforts to investigate heart-failure therapies in silico and illustrate the potential of computational models to aid understanding of complex mechanical and hemodynamic effects of new therapies.

Construction and Validation of Subject-Specific Biventricular Finite-Element Models of Healthy and Failing Swine Hearts From High-Resolution DT-MRI.

Predictive computational modeling has revolutionized classical engineering disciplines and is in the process of transforming cardiovascular research. This is particularly relevant for investigating emergent therapies for heart failure, which remains a leading cause of death globally. The creation of subject-specific biventricular computational cardiac models has been a long-term endeavor within the biomedical engineering community. Using high resolution (0.3 × 0.3 × 0.8 mm) ex vivo data, we constructed a precise fully subject-specific biventricular finite-element model of healthy and failing swine hearts. Each model includes fully subject-specific geometries, myofiber architecture and, in the case of the failing heart, fibrotic tissue distribution. Passive and active material properties are prescribed using hyperelastic strain energy functions that define a nearly incompressible, orthotropic material capable of contractile function. These materials were calibrated using a sophisticated multistep approach to match orthotropic tri-axial shear data as well as subject-specific hemodynamic ventricular targets for pressure and volume to ensure realistic cardiac function. Each mechanically beating heart is coupled with a lumped-parameter representation of the circulatory system, allowing for a closed-loop definition of cardiovascular flow. The circulatory model incorporates unidirectional fluid exchanges driven by pressure gradients of the model, which in turn are driven by the mechanically beating heart. This creates a computationally meaningful representation of the dynamic beating of the heart coupled with the circulatory system. Each model was calibrated using subject-specific experimental data and compared with independent in vivo strain data obtained from echocardiography. Our methods produced highly detailed representations of swine hearts that function mechanically in a remarkably similar manner to the in vivo subject-specific strains on a global and regional comparison. The degree of subject-specificity included in the models represents a milestone for modeling efforts that captures realism of the whole heart. This study establishes a foundation for future computational studies that can apply these validated methods to advance cardiac mechanics research.

Partial LVAD restores ventricular outputs and normalizes LV but not RV stress distributions in the acutely failing heart in silico.

PURPOSE: Heart failure is a worldwide epidemic that is unlikely to change as the population ages and life expectancy increases. We sought to detail significant recent improvements to the Dassault Systèmes Living Heart Model (LHM) and use the LHM to compute left ventricular (LV) and right ventricular (RV) myofiber stress distributions under the following 4 conditions: (1) normal cardiac function; (2) acute left heart failure (ALHF); (3) ALHF treated using an LV assist device (LVAD) flow rate of 2 L/min; and (4) ALHF treated using an LVAD flow rate of 4.5 L/min. METHODS AND RESULTS: Incorporating improved systolic myocardial material properties in the LHM resulted in its ability to simulate the Frank-Starling law of the heart. We decreased myocardial contractility in the LV myocardium so that LV ejection fraction decreased from 56% to 28%. This caused mean LV end diastolic (ED) stress to increase to 508% of normal, mean LV end systolic (ES) stress to increase to 113% of normal, mean RV ED stress to decrease to 94% of normal and RV ES to increase to 570% of normal. When ALHF in the model was treated with an LVAD flow rate of 4.5 L/min, most stress results normalized. Mean LV ED stress became 85% of normal, mean LV ES stress became 109% of normal and mean RV ED stress became 95% of normal. However, mean RV ES stress improved less dramatically (to 342% of normal values). CONCLUSIONS: These simulations strongly suggest that an LVAD is effective in normalizing LV stresses but not RV stresses that become elevated as a result of ALHF.

Personalised computational cardiology: Patient-specific modelling in cardiac mechanics and biomaterial injection therapies for myocardial infarction.

Predictive computational modelling in biomedical research offers the potential to integrate diverse data, uncover biological mechanisms that are not easily accessible through experimental methods and expose gaps in knowledge requiring further research. Recent developments in computing and diagnostic technologies have initiated the advancement of computational models in terms of complexity and specificity. Consequently, computational modelling can increasingly be utilised as enabling and complementing modality in the clinic-with medical decisions and interventions being personalised. Myocardial infarction and heart failure are amongst the leading causes of death globally despite optimal modern treatment. The development of novel MI therapies is challenging and may be greatly facilitated through predictive modelling. Here, we review the advances in patient-specific modelling of cardiac mechanics, distinguishing specificity in cardiac geometry, myofibre architecture and mechanical tissue properties. Thereafter, the focus narrows to the mechanics of the infarcted heart and treatment of myocardial infarction with particular attention on intramyocardial biomaterial delivery.