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

Hemodynamic Optimization by Invasive Ramp Test in Patients Supported With HeartMate 3 Left Ventricular Assist Device

In patients supported by the HeartMate 3 left ventricular assist device (HM3 LVAD), pump speed adjustments may improve hemodynamics. We investigated the hemodynamic implications of speed adjustments in HM3 recipients undergoing hemodynamic ramp tests. Clinically stable HM3 recipients who underwent routine invasive hemodynamic ramp tests between 2015 and 2022 at our center were included. Filling pressure optimization, defined as central venous pressure (CVP) <12 mm Hg and pulmonary capillary wedge pressure (PCWP) <18 mm Hg, was assessed at baseline and final pump speeds. Patients with optimized pressures were compared to nonoptimized patients. Overall 60 HM3 recipients with a median age of 62 years (56, 71) and time from LVAD implantation of 187 days (124, 476) were included. Optimized filling pressures were found in 35 patients (58%) at baseline speed. Speed was adjusted in 84% of the nonoptimized patients. Consequently, 39 patients (65%) had optimized pressures at final speed. There were no significant differences in hemodynamic findings between baseline and final speeds ( p > 0.05 for all). Six and 12 month readmission-free rates were higher in optimized compared with nonoptimized patients ( p = 0.03 for both), predominantly due to lower cardiac readmission-free rates ( p = 0.052). In stable outpatients supported with HM3 who underwent routine ramp tests, optimized hemodynamics were achieved in only 2 of 3 of the patients. Patients with optimized pressures had lower all-cause readmission rates, primarily driven by fewer cardiac-related hospitalizations.

Preoperative passive venous pressure-driven cardiac function determines left ventricular assist device outcomes

BACKGROUND

Right heart output in heart failure can be compensated through increasing systemic venous pressure. We determined whether the magnitude of this "passive cardiac output" can predict LVAD outcomes.

METHODS

This was a retrospective review of 383 patients who received a continuous-flow LVAD at the University of Michigan between 2012 and 2021. Pre-LVAD cardiac output driven by venous pressure was determined by dividing right atrial pressure by mean pulmonary artery pressure, multiplied by total cardiac output. Normalization to body surface area led to the passive cardiac index (PasCI). The Youden J statistic was used to identify the PasCI threshold, which predicted LVAD death by 2 years.

RESULTS

Increased preoperative PasCI was associated with reduced survival (hazard ratio [HR], 2.27; P < .01), and increased risk of right ventricular failure (RVF) (HR, 3.46; P = .04). Youden analysis showed that a preoperative PasCI ≥0.5 (n = 226) predicted LVAD death (P = .10). Patients with PasCI ≥0.5 had poorer survival (P = .02), with a trend toward more heart failure readmission days (mean, 45.09 ± 67.64 vs 35.13 ± 45.02 days; P = .084) and increased gastrointestinal bleeding (29.2% vs 20.4%; P = .052). Additionally, of the 97 patients who experienced readmissions for heart failure, those with pre-LVAD implantation PasCI ≥0.5 were more likely to have more than 1 readmission (P = .05).

CONCLUSIONS

Although right heart output can be augmented by raising venous pressure, this negatively impacts end-organ function and increases heart failure readmission days. Patients with a pre-LVAD PasCI ≥0.5 have worse post-LVAD survival and increased RVF. Using the PasCI metric in isolation or incorporated into a predictive model may improve the management of LVAD candidates with RV dysfunction.

Design and Analysis of a Polymeric Left Ventricular Simulator via Computational Modelling

Preclinical testing of medical devices is an essential step in the product life cycle, whereas testing of cardiovascular implants requires specialised testbeds or numerical simulations using computer software Ansys 2016. Existing test setups used to evaluate physiological scenarios and test cardiac implants such as mock circulatory systems or isolated beating heart platforms are driven by sophisticated hardware which comes at a high cost or raises ethical concerns. On the other hand, computational methods used to simulate blood flow in the cardiovascular system may be simplified or computationally expensive. Therefore, there is a need for low-cost, relatively simple and efficient test beds that can provide realistic conditions to simulate physiological scenarios and evaluate cardiovascular devices. In this study, the concept design of a novel left ventricular simulator made of latex rubber and actuated by pneumatic artificial muscles is presented. The designed left ventricular simulator is geometrically similar to a native left ventricle, whereas the basal diameter and long axis length are within an anatomical range. Finite element simulations evaluating left ventricular twisting and shortening predicted that the designed left ventricular simulator rotates approximately 17 degrees at the apex and the long axis shortens around 11 mm. Experimental results showed that the twist angle is 18 degrees and the left ventricular simulator shortens 5 mm. Twist angles and long axis shortening as in a native left ventricle show it is capable of functioning like a native left ventricle and simulating a variety of scenarios, and therefore has the potential to be used as a test platform.

A perfectly imperfect engine: Utilizing the digital twin paradigm in pulmonary hypertension

Pulmonary hypertension (PH) is a severe medical condition with a number of treatment options, the majority of which are introduced without consideration of the underlying mechanisms driving it within an individual and thus a lack of tailored approach to treatment. The one exception is a patient presenting with apparent pulmonary arterial hypertension and shown to have vaso-responsive disease, whose clinical course and prognosis is significantly improved by high dose calcium channel blockers. PH is however characterized by a relative abundance of available data from patient cohorts, ranging from molecular data characterizing gene and protein expression in different tissues to physiological data at the organ level and clinical information. Integrating available data with mechanistic information at the different scales into computational models suggests an approach to a more personalized treatment of the disease using model-based optimization of interventions for individual patients. That is, constructing digital twins of the disease, customized to a patient, promises to be a key technology for personalized medicine, with the aim of optimizing use of existing treatments and developing novel interventions, such as new drugs. This article presents a perspective on this approach in the context of a review of existing computational models for different aspects of the disease, and it lays out a roadmap for a path to realizing it.

Right heart failure after durable left ventricular assist device implantation

INTRODUCTION

Right heart failure (RHF) is a well-known complication after left ventricular assist device (LVAD) implantation and portends increased morbidity and mortality. Understanding the mechanisms and predictors of RHF in this clinical setting may offer ideas for early identification and aggressive management to minimize poor outcomes. A variety of medical therapies and mechanical circulatory support options are currently available for the management of post-LVAD RHF.

AREAS COVERED

We reviewed the existing definitions of RHF including its potential mechanisms in the context of durable LVAD implantation and currently available medical and device therapies. We performed a literature search using PubMed (from 2010 to 2023).

EXPERT OPINION

RHF remains a common complication after LVAD implantation. However, existing knowledge gaps limit clinicians' ability to adequately address its consequences. Early identification and management are crucial to reducing the risk of poor outcomes, but existing risk stratification tools perform poorly and have limited clinical applicability. This is an area ripe for investigation with the potential for major improvements in identification and targeted therapy in an effort to improve outcomes.

Hemodynamic Evaluation of Asynchronous Speed Modulation of a Continuous-Flow Left Ventricular Assist Device in an Acute-Myocardial Injury Sheep Model

Asynchronous rotational-speed modulation of a continuous-flow left ventricular assist device (LVAD) can increase pulsatility; however, the feasibility of hemodynamic modification by asynchronous modulation of an LVAD has not been sufficiently verified. We evaluated the acute effect of an asynchronous-modulation mode under LVAD support and the accumulated effect of 6 consecutive hours of driving by the asynchronous-modulation mode on hemodynamics, including both ventricles, in a coronary microembolization-induced acute-myocardial injury sheep model. We evaluated 5-min LVAD-support hemodynamics, including biventricular parameters, by switching modes from constant-speed to asynchronous-modulation in the same animals ("acute-effect evaluation under LVAD support"). To determine the accumulated effect of a certain driving period, we evaluated hemodynamics including biventricular parameters after weaning from 6-hour (6 h) LVAD support by constant-speed or asynchronous-modulation mode ("6h-effect evaluation"). The acute-effect evaluation under LVAD support revealed that, compared to the constant-speed mode, the asynchronous-modulation mode increased vascular pulsatility but did not have significantly different effects on hemodynamics, including both ventricles. The 6 h-effect evaluation revealed that the hemodynamics did not differ significantly between the two groups except for some biventricular parameters which did not indicate negative effects of the asynchronous-modulation mode on both ventricles. The asynchronous-modulation mode could be feasible to increase vascular pulsatility without causing negative effects on hemodynamics including both ventricles. Compared to the constant-speed mode, the asynchronous-modulation mode increased pulsatility during LVAD support without negative effects on hemodynamics including both ventricles in the acute phase. Six hours of LVAD support with the asynchronous-modulation mode exerted no negative effects on hemodynamics, including both ventricles, after weaning from the LVAD.

Advancing clinical translation of cardiac biomechanics models: a comprehensive review, applications and future pathways

Cardiac mechanics models are developed to represent a high level of detail, including refined anatomies, accurate cell mechanics models, and platforms to link microscale physiology to whole-organ function. However, cardiac biomechanics models still have limited clinical translation. In this review, we provide a picture of cardiac mechanics models, focusing on their clinical translation. We review the main experimental and clinical data used in cardiac models, as well as the steps followed in the literature to generate anatomical meshes ready for simulations. We describe the main models in active and passive mechanics and the different lumped parameter models to represent the circulatory system. Lastly, we provide a summary of the state-of-the-art in terms of ventricular, atrial, and four-chamber cardiac biomechanics models. We discuss the steps that may facilitate clinical translation of the biomechanics models we describe. A well-established software to simulate cardiac biomechanics is lacking, with all available platforms involving different levels of documentation, learning curves, accessibility, and cost. Furthermore, there is no regulatory framework that clearly outlines the verification and validation requirements a model has to satisfy in order to be reliably used in applications. Finally, better integration with increasingly rich clinical and/or experimental datasets as well as machine learning techniques to reduce computational costs might increase model reliability at feasible resources. Cardiac biomechanics models provide excellent opportunities to be integrated into clinical workflows, but more refinement and careful validation against clinical data are needed to improve their credibility. In addition, in each context of use, model complexity must be balanced with the associated high computational cost of running these models.

Artificial Intelligence, Computational Simulations, and Extended Reality in Cardiovascular Interventions

Artificial intelligence, computational simulations, and extended reality, among other 21st century computational technologies, are changing the health care system. To collectively highlight the most recent advances and benefits of artificial intelligence, computational simulations, and extended reality in cardiovascular therapies, we coined the abbreviation AISER. The review particularly focuses on the following applications of AISER: 1) preprocedural planning and clinical decision making; 2) virtual clinical trials, and cardiovascular device research, development, and regulatory approval; and 3) education and training of interventional health care professionals and medical technology innovators. We also discuss the obstacles and constraints associated with the application of AISER technologies, as well as the proposed solutions. Interventional health care professionals, computer scientists, biomedical engineers, experts in bioinformatics and visualization, the device industry, ethics committees, and regulatory agencies are expected to streamline the use of AISER technologies in cardiovascular interventions and medicine in general.

Successful echocardiography-guided medical management of severe early post-implant right ventricular failure in a patient with left ventricular assist device support: a case report

BACKGROUND

Post-implant right heart failure (RHF) has been recognized as a crucial prognostic factor in patients receiving left ventricular assist devices (LVADs), and its management has long attracted attention from cardiologists and surgeons.

CASE PRESENTATION

This report described an 18-year-old female with acutely deteriorating heart failure due to dilated cardiomyopathy who underwent paracorporeal pulsatile-flow LVAD and developed early post-implant RHF. At postoperative day (POD) six, she was almost asymptomatic at rest on 2.5 mg/kg/min of dobutamine; however, the echocardiogram, performed as part of the daily postoperative care, revealed a severely enlarged right ventricle with a decompressed left ventricle, implying the development of post-implant RHF. Bolus infusion of saline and reduction of pump flow (6.0 L/min to 3.0 L/min) led to normalization of both ventricular shapes in 30 s, suggesting that RHF could be managed without surgical interventions. Milrinone was started on POD six, followed by sildenafil administration on POD seven. Fluid balance was strictly adjusted under the close observation of daily echocardiograms. Milrinone and dobutamine were discontinued on PODs 18 and 21, respectively. The patient was listed for a heart transplant on POD 40. Despite reduced right ventricular function (right ventricular stroke work index of 182.34 mmHg*ml/m- 2, body surface area 1.5 m2), she was successfully converted to implantable LVAD on POD 44 with no recurrence of post-implant RHF thereafter for four years.

CONCLUSIONS

In post-implant RHF management, early detection, together with proper and prompt medical management, is crucial to avoiding any surgical intervention. Close observation of daily echocardiograms might be helpful in detecting subclinical RHF and is useful for post-implant medical management.

Physical model of end-diastolic and end-systolic pressure-volume relationships of a heart

Left ventricular stiffness and contractility, characterized by the end-diastolic pressure-volume relationship (EDPVR) and the end-systolic pressure-volume relationship (ESPVR), are two important indicators of the performance of the human heart. Although much research has been conducted on EDPVR and ESPVR, no model with physically interpretable parameters combining both relationships has been presented, thereby impairing the understanding of cardiac physiology and pathology. Here, we present a model that evaluates both EDPVR and ESPVR with physical interpretations of the parameters in a unified framework. Our physics-based model fits the available experimental data and in silico results very well and outperforms existing models. With prescribed parameters, the new model is used to predict the pressure-volume relationships of the left ventricle. Our model provides a deeper understanding of cardiac mechanics and thus will have applications in cardiac research and clinical medicine.

Prediction, prevention, and management of right ventricular failure after left ventricular assist device implantation: A comprehensive review

Left ventricular assist devices (LVADs) are increasingly common across the heart failure population. Right ventricular failure (RVF) is a feared complication that can occur in the early post-operative phase or during the outpatient follow-up. Multiple tools are available to the clinician to carefully estimate the individual risk of developing RVF after LVAD implantation. This review will provide a comprehensive overview of available tools for RVF prognostication, including patient-specific and right ventricle (RV)-specific echocardiographic and hemodynamic parameters, to provide guidance in patient selection during LVAD candidacy. We also offer a multidisciplinary approach to the management of early RVF, including indications and management of right ventricular assist devices in this setting to provide tools that help managing the failing RV.

Support Pressure Acting on the Epicardial Surface of a Rat Left Ventricle—A Computational Study

The present computational study investigates the effects of an epicardial support pressure mimicking a heart support system without direct blood contact. We chose restrictive cardiomyopathy as a model for a diseased heart. By changing one parameter representing the amount of fibrosis, this model allows us to investigate the impairment in a diseased left ventricle, both during diastole and systole. The aim of the study is to determine the temporal course and value of the support pressure that leads to a normalization of the cardiac parameters in diseased hearts. These are quantified via the end-diastolic pressure, end-diastolic volume, end-systolic volume, and ejection fraction. First, the amount of fibrosis is increased to model diseased hearts at different stages. Second, we determine the difference in the left ventricular pressure between a healthy and diseased heart during a cardiac cycle and apply for the epicardial support as the respective pressure difference. Third, an epicardial support pressure is applied in form of a piecewise constant step function. The support is provided only during diastole, only during systole, or during both phases. Finally, the support pressure is adjusted to reach the corresponding parameters in a healthy rat. Parameter normalization is not possible to achieve with solely diastolic or solely systolic support; for the modeled case with 50% fibrosis, the ejection fraction can be increased by 5% with purely diastolic support and 14% with purely systolic support. However, the ejection fraction reaches the value of the modeled healthy left ventricle (65.6%) using a combination of diastolic and systolic support. The end-diastolic pressure of 13.5 mmHg cannot be decreased with purely systolic support. However, the end-diastolic pressure reaches the value of the modeled healthy left ventricle (7.5 mmHg) with diastolic support as well as with the combination of the diastolic and systolic support. The resulting negative diastolic support pressure is −4.5 mmHg, and the positive systolic support pressure is 90 mmHg. We, thereby, conclude that ventricular support during both diastole and systole is beneficial for normalizing the left ventricular ejection fraction and the end-diastolic pressure, and thus it is a potentially interesting therapy for cardiac insufficiency.

Therapeutic augmentation of NO-sGC-cGMP signalling: lessons learned from pulmonary arterial hypertension and heart failure

The nitric oxide (NO)-guanylate cyclase (GC)-cyclic guanosine monophosphate (cGMP) pathway plays an important role in cardiovascular, pulmonary and renal function. Phosphodiesterase-5 inhibitors (PDE-5i) inhibit cGMP degradation, whereas both soluble guanylate cyclase (sGC) stimulators and sGC activators directly increase sGC. PDE-5i (e.g. sildenafil, tadalafil) and sGC stimulators (e.g. riociguat, vericiguat) have been extensively used in pulmonary artery hypertension (PAH) and heart failure (HF). PDE-5i have also been used in end-stage HF before and after left ventricular (LV) assist device (LVAD) implantation. Augmentation of NO-GC-cGMP signalling with PDE-5i causes selective pulmonary vasodilation, which is highly effective in PAH but may have controversial, potentially adverse effects in HF, including pre-LVAD implant due to device unmasking of PDE-5i-induced RV dysfunction. In contrast, retrospective analyses have demonstrated that PDE-5i have beneficial effects when initiated post LVAD implant due to the improved haemodynamics of the supported LV and the pleiotropic actions of these compounds. sGC stimulators, in turn, are effective both in PAH and in HF due to their balanced pulmonary and systemic vasodilation, and as such they are preferable to PDE-5i if the use of a pulmonary vasodilator is needed in HF patients, including those listed for LVAD implantation. Regarding the effectiveness of PDE-5i and sGC stimulators when initiated post LVAD implant, these two groups of compounds should be tested in a randomized control trial.

Left Ventricle Biomechanics of Low-Flow, Low-Gradient Aortic Stenosis: A Patient-Specific Computational Model

This study aimed to create an imaging-derived patient-specific computational model of low-flow, low-gradient (LFLG) aortic stenosis (AS) to obtain biomechanics data about the left ventricle. LFLG AS is now a commonly recognized sub-type of aortic stenosis. There remains much controversy over its management, and investigation into ventricular biomechanics may elucidate pathophysiology and better identify patients for valve replacement. ECG-gated cardiac computed tomography images from a patient with LFLG AS were obtained to provide patient-specific geometry for the computational model. Surfaces of the left atrium, left ventricle (LV), and outflow track were segmented. A previously validated multi-scale, multi-physics computational human heart model was adapted to the patient-specific geometry, yielding a model consisting of 91,000 solid elements. This model was coupled to a virtual circulatory system and calibrated to clinically measured parameters from echocardiography and cardiac catheterization data. The simulation replicated key physiologic parameters within 10% of their clinically measured values. Global LV systolic myocardial stress was 7.1 ± 1.8 kPa. Mean stress of the basal, middle, and apical segments were 7.7 ± 1.8 kPa, 9.1 ± 3.8 kPa, and 6.4 ± 0.4 kPa, respectively. This is the first patient-specific computational model of LFLG AS based on clinical imaging. Low myocardial stress correlated with low ejection fraction and eccentric LV remodeling. Further studies are needed to understand how alterations in LV biomechanics correlates with clinical outcomes of AS.

The right ventricular involvement in dilated cardiomyopathy: prevalence and prognostic implications of the often-neglected child

Dilated cardiomyopathy (DCM) is a primary heart muscle disease characterized by left or biventricular systolic impairment. Historically, most of the clinical attention has been devoted to the evaluation of left ventricular function and morphology, while right ventricle (RV) has been for many years the forgotten chamber. Recently, progresses in cardiac imaging gave clinicians precious tools for the evaluation of RV, raising the awareness of the importance of biventricular assessment in DCM. Indeed, RV involvement is far from being uncommon in DCM, and the presence of right ventricular dysfunction (RVD) is one of the major negative prognostic determinants in DCM patients. However, some aspects such as the possible role of specific genetic mutations in determining the biventricular phenotype in DCM, or the lack of specific treatments able to primarily counteract RVD, still need research. In this review, we summarized the current knowledge on RV involvement in DCM, giving an overview on the epidemiology and pathogenetic mechanisms implicated in determining RVD. Furthermore, we discussed the imaging techniques to evaluate RV function and the role of RV failure in advanced heart failure.

In Vivo Validation of a Cardiovascular Simulation Model in Pigs

Many computer simulation models of the cardiovascular system, of varying complexity and objectives, have been proposed in physiological science. Every model needs to be parameterized and evaluated individually. We conducted a porcine animal model to parameterize and evaluate a computer simulation model, recently proposed by our group. The results of an animal model, on thirteen healthy pigs, were used to generate consistent parameterization data for the full heart computer simulation model. To evaluate the simulation model, differences between the resulting simulation output and original animal data were analysed. The input parameters of the animal model, used to individualize the computer simulation, showed high interindividual variability (range of coefficient of variation: 10.1–84.5%), which was well-reflected by the resulting haemodynamic output parameters of the simulation (range of coefficient of variation: 12.6–45.7%). The overall bias between the animal and simulation model was low (mean: −3.24%, range: from −26.5 to 20.1%). The simulation model used in this study was able to adapt to the high physiological variability in the animal model. Possible reasons for the remaining differences between the animal and simulation model might be a static measurement error, unconsidered inaccuracies within the model, or unconsidered physiological interactions.

A Multi-Domain Simulation Study of a Pulsatile-Flow Pump Device for Heart Failure With Preserved Ejection Fraction

Mechanical circulatory support (MCS) devices are currently under development to improve the physiology and hemodynamics of patients with heart failure with preserved ejection fraction (HFpEF). Most of these devices, however, are designed to provide continuous-flow support. While it has been shown that pulsatile support may overcome some of the complications hindering the clinical translation of these devices for other heart failure phenotypes, the effects that it may have on the HFpEF physiology are still unknown. Here, we present a multi-domain simulation study of a pulsatile pump device with left atrial cannulation for HFpEF that aims to alleviate left atrial pressure, commonly elevated in HFpEF. We leverage lumped-parameter modeling to optimize the design of the pulsatile pump, computational fluid dynamic simulations to characterize hydraulic and hemolytic performance, and finite element modeling on the Living Heart Model to evaluate effects on arterial, left atrial, and left ventricular hemodynamics and biomechanics. The findings reported in this study suggest that pulsatile-flow support can successfully reduce pressures and associated wall stresses in the left heart, while yielding more physiologic arterial hemodynamics compared to continuous-flow support. This work therefore supports further development and evaluation of pulsatile support MCS devices for HFpEF.

Personalization of biomechanical simulations of the left ventricle by in-vivo cardiac DTI data: Impact of fiber interpolation methods

Simulations of cardiac electrophysiology and mechanics have been reported to be sensitive to the microstructural anisotropy of the myocardium. Consequently, a personalized representation of cardiac microstructure is a crucial component of accurate, personalized cardiac biomechanical models. In-vivo cardiac Diffusion Tensor Imaging (cDTI) is a non-invasive magnetic resonance imaging technique capable of probing the heart's microstructure. Being a rather novel technique, issues such as low resolution, signal-to noise ratio, and spatial coverage are currently limiting factors. We outline four interpolation techniques with varying degrees of data fidelity, different amounts of smoothing strength, and varying representation error to bridge the gap between the sparse in-vivo data and the model, requiring a 3D representation of microstructure across the myocardium. We provide a workflow to incorporate in-vivo myofiber orientation into a left ventricular model and demonstrate that personalized modelling based on fiber orientations from in-vivo cDTI data is feasible. The interpolation error is correlated with a trend in personalized parameters and simulated physiological parameters, strains, and ventricular twist. This trend in simulation results is consistent across material parameter settings and therefore corresponds to a bias introduced by the interpolation method. This study suggests that using a tensor interpolation approach to personalize microstructure with in-vivo cDTI data, reduces the fiber uncertainty and thereby the bias in the simulation results.

Physiology of Continuous-Flow Left Ventricular Assist Device Therapy

The expanding use of continuous-flow left ventricular assist devices (CF-LVADs) for end-stage heart failure warrants familiarity with the physiologic interaction of the device with the native circulation. Contemporary devices utilize predominantly centrifugal flow and, to a lesser extent, axial flow rotors that vary with respect to their intrinsic flow characteristics. Flow can be manipulated with adjustments to preload and afterload as in the native heart, and ascertainment of the predicted effects is provided by differential pressure-flow (H-Q) curves or loops. Valvular heart disease, especially aortic regurgitation, may significantly affect adequacy of mechanical support. In contrast, atrioventricular and ventriculoventricular timing is of less certain significance. Although beneficial effects of device therapy are typically seen due to enhanced distal perfusion, unloading of the left ventricle and atrium, and amelioration of secondary pulmonary hypertension, negative effects of CF-LVAD therapy on right ventricular filling and function, through right-sided loading and septal interaction, can make optimization challenging. Additionally, a lack of pulsatile energy provided by CF-LVAD therapy has physiologic consequences for end-organ function and may be responsible for a series of adverse effects. Rheological effects of intravascular pumps, especially shear stress exposure, result in platelet activation and hemolysis, which may result in both thrombotic and hemorrhagic consequences. Development of novel solutions for untoward device-circulatory interactions will facilitate hemodynamic support while mitigating adverse events. © 2021 American Physiological Society. Compr Physiol 12:1-37, 2021.

Patient-Specific Analysis of Ascending Thoracic Aortic Aneurysm with the Living Heart Human Model

In ascending thoracic aortic aneurysms (ATAAs), aneurysm kinematics are driven by ventricular traction occurring every heartbeat, increasing the stress level of dilated aortic wall. Aortic elongation due to heart motion and aortic length are emerging as potential indicators of adverse events in ATAAs; however, simulation of ATAA that takes into account the cardiac mechanics is technically challenging. The objective of this study was to adapt the realistic Living Heart Human Model (LHHM) to the anatomy and physiology of a patient with ATAA to assess the role of cardiac motion on aortic wall stress distribution. Patient-specific segmentation and material parameter estimation were done using preoperative computed tomography angiography (CTA) and ex vivo biaxial testing of the harvested tissue collected during surgery. The lumped-parameter model of systemic circulation implemented in the LHHM was refined using clinical and echocardiographic data. The results showed that the longitudinal stress was highest in the major curvature of the aneurysm, with specific aortic quadrants having stress levels change from tensile to compressive in a transmural direction. This study revealed the key role of heart motion that stretches the aortic root and increases ATAA wall tension. The ATAA LHHM is a realistic cardiovascular platform where patient-specific information can be easily integrated to assess the aneurysm biomechanics and potentially support the clinical management of patients with ATAAs.

An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline

Parameterised patient-specific models of the heart enable quantitative analysis of cardiac function as well as estimation of regional stress and intrinsic tissue stiffness. However, the development of personalised models and subsequent simulations have often required lengthy manual setup, from image labelling through to generating the finite element model and assigning boundary conditions. Recently, rapid patient-specific finite element modelling has been made possible through the use of machine learning techniques. In this paper, utilising multiple neural networks for image labelling and detection of valve landmarks, together with streamlined data integration, a pipeline for generating patient-specific biventricular models is applied to clinically-acquired data from a diverse cohort of individuals, including hypertrophic and dilated cardiomyopathy patients and healthy volunteers. Valve motion from tracked landmarks as well as cavity volumes measured from labelled images are used to drive realistic motion and estimate passive tissue stiffness values. The neural networks are shown to accurately label cardiac regions and features for these diverse morphologies. Furthermore, differences in global intrinsic parameters, such as tissue anisotropy and normalised active tension, between groups illustrate respective underlying changes in tissue composition and/or structure as a result of pathology. This study shows the successful application of a generic pipeline for biventricular modelling, incorporating artificial intelligence solutions, within a diverse cohort.

Right Ventricular Pressure-Volume Analysis During Left Ventricular Assist Device Speed Optimization Studies: Insights Into Interventricular Interactions and Right Ventricular Failure

BACKGROUND

Interventricular interaction, which refers to the impact of left ventricular (LV) function on right ventricular (RV) function and vice versa, has been implicated in the pathogenesis of RV failure in LV assist device (LVAD) recipients. We sought to understand more about interventricular interaction by quantifying changes in the RV systolic and diastolic function with varying LVAD speeds.

METHODS AND RESULTS

Four patients (ages 22-69 years, 75% male, and 25% with ischemic cardiomyopathy) underwent a protocolized hemodynamic ramp test within 12 months of LVAD implantation where RV pressure-volume loops were recorded with a conductance catheter. The end-systolic PV relationship and end-diastolic PV relationship were compared using the V₂₀ and V₁₀ indices (volumes at which end-systolic PV relationship and end-diastolic PV relationship reach a pressure of 20 and 10 mm Hg, respectively). The ∆V₂₀ and ∆V₁₀ refer to the change in V₂₀ and V₁₀ from the minimum to maximum LVAD speeds. RV PV loops demonstrated variable changes in systolic and diastolic function with increasing LVAD speed. The end-systolic PV relationship changed in 1 patient (patient 2, ∆V₂₀ = 23.5 mL), reflecting a decrease in systolic function with increased speed, and was unchanged in 3 patients (average ∆V₂₀ = 7.4 mL). The end-diastolic PV relationship changed with increasing speed in 3 of 4 patients (average ∆V₁₀ = 12.5 mL), indicating an increase in ventricular compliance, and remained unchanged in one participant (patient 1; ∆V₁₀ = 4.0 mL).

CONCLUSIONS

Interventricular interaction can improve RV compliance and impair systolic function, but the overall effect on RV performance in this pilot investigation is heterogeneous. Further research is required to understand which patient characteristics and hemodynamic parameters influence the net impact of interventricular interaction.

Establishing the Biofidelity of a Multiphysics Finite Element Model of the Human Heart

PURPOSE

Accelerating development of new therapeutic cardiac devices remains a clinical and technical priority. High-performance computing and the emergence of functional and complex in silico models of human anatomy can be an engine to accelerate the commercialization of innovative, safe, and effective devices.

METHODS

An existing three-dimensional, nonlinear model of a human heart with flow boundary conditions was evaluated. Its muscular tissues were exercised using electrophysiological boundary conditions, creating a dynamic, electro-mechanical simulation of the kinetics of the human heart. Anatomic metrics were selected to characterize the functional biofidelity of the model based on their significance to the design of cardiac devices. The model output was queried through the cardiac cycle and compared to in vivo literature values.

RESULTS

For the kinematics of mitral and aortic valves and curvature of coronary vessels, the model's performance was at or above the 95th percentile range of the in vivo data from large patient cohorts. One exception was the kinematics of the tricuspid valve. The model's mechanical use environment would subject devices to generally conservative use conditions.

CONCLUSIONS

This conservative simulated use environment for heart-based medical devices, and its judicious application in the evaluation of medical devices is justified, but careful interpretation of the results is encouraged.

Precision medicine in human heart modeling : Perspectives, challenges, and opportunities

Precision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.

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.

Towards the simulation of active cardiac mechanics using a smoothed finite element method

In the last decades, various computational models have been developed to simulate cardiac electromechanics. The most common numerical tool is the finite element method (FEM). However, this method crucially depends on the mesh quality. For complex geometries such as cardiac structures, it is convenient to use tetrahedral discretisations which can be generated automatically. On the other hand, such automatic meshing with tetrahedrons together with large deformations often lead to elements distortion and volumetric locking. To overcome these difficulties, different smoothed finite element methods (S-FEMs) have been proposed in the recent years. They are known to be volumetric locking free, less sensitive to mesh distortion and so far have been used e.g. in simulation of passive cardiac mechanics. In this work, we extend for the first time node-based S-FEM (NS-FEM) towards active cardiac mechanics. Firstly, the sensitivity to mesh distortion is tested and compared to that of FEM. Secondly, an active contraction in circumferentially aligned fibre direction is modelled in the healthy and the infarcted case. We show, that the proposed method is more robust with respect to mesh distortion and computationally more efficient than standard FEM. Being furthermore free of volumetric locking problems makes S-FEM a promising alternative in modelling of active cardiac mechanics, respectively electromechanics.

Ultrasound-based prediction of interventricular septum positioning during left ventricular support-an experimental study

The implantation of left ventricular assist devices (LVADs) is often complicated by arrhythmias and right ventricular failure (RVF). Today, the pump speed is titrated to optimize device support using single observations of interventricular septum (IVS) positioning with echocardiographic ultrasound (US). The study demonstrates the applicability of three integrated US transducers in the LVAD cannula to monitor IVS positioning continuously and robustly in real time. In vitro, the predictor of the IVS shift shows an overall prediction error for all volume states of less than 20% and provides a continuous assessment for 99% of cases in four differently sized heart phantoms. The prediction of IVS shift depending on the cannula position is robust for azimuthal and polar deviations of ± 20° and ± 8°, respectively. This intracardiac US concept results in a viable predictor for IVS positioning and represents a promising approach to continuously monitor the IVS and ventricular loading in LVAD patients. Graphical abstract.

Continuous Heart Volume Monitoring by Fully Implantable Soft Strain Sensor

Cardiothoracic open-heart surgery has revolutionized the treatment of cardiovascular disease, the leading cause of death worldwide. After the surgery, hemodynamic and volume management can be complicated, for example in case of vasoplegia after endocarditis. Timely treatment is crucial for outcomes. Currently, treatment decisions are made based on heart volume, which needs to be measured manually by the clinician each time using ultrasound. Alternatively, implantable sensors offer a real-time window into the dynamic function of our body. Here it is shown that a soft flexible sensor, made with biocompatible materials, implanted on the surface of the heart, can provide continuous information of the heart volume after surgery. The sensor works robustly for a period of two days on a tensile machine. The accuracy of measuring heart volume is improved compared to the clinical gold standard in vivo, with an error of 7.1 mL for the strain sensor versus impedance and 14.0 mL versus ultrasound. Implanting such a sensor would provide essential, continuous information on heart volume in the critical time following the surgery, allowing early identification of complications, facilitating treatment, and hence potentially improving patient outcome.

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.

Bionic women and men - Part 3: Right ventricular dysfunction in patients implanted with left ventricular assist devices

NEW FINDINGS

What is the topic of this review? Right heart dysfunction remains a major adverse event in patients with end stage heart failure undergoing left ventricular assist device placement. This article reviews the pathophysiology and clinical considerations of right heart failure in this patient population. What advances does it highlight? This review highlights the anatomic and physiological peculiarities of the interplay between left and right heart function in patients undergoing LVAD therapy. These would allow us to further advance our understanding of right ventricular function.

ABSTRACT

The adaptation of the right ventricular (RV) output to a left ventricular assist device (LVAD) often determines the fate of patients with pulmonary hypertension secondary to left heart failure. Pre-existing right heart dysfunction in patients with advanced left heart failure is the consequence of increased (arterial) afterload and not simply the consequence of myocardial disease. If unaccounted for, it has the potential of accelerating into clinical right heart failure after LVAD, leading to significant morbidity and mortality. After LVAD implantation, the RV has to face increased flow generated by the LVAD, cardiac arrhythmias and exaggerated functional interactions between both ventricles. Understanding the key physiological mechanisms of RV dysfunction in patients with end-stage heart failure will allow us to predict and therefore prevent RV failure after LVAD implantation.

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.

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.