A virtual sizing tool for mitral valve annuloplasty

Beyond Conventional Operations: Embracing the Era of Contemporary Minimally Invasive Cardiac Surgery

Over the past two decades, minimally invasive cardiac surgery (MICS) has gained a significant place due to the emergence of innovative tools and improvements in surgical techniques, offering comparable efficacy and safety to traditional surgical methods. This review provides an overview of the history of MICS, its current state, and its prospects and highlights its advantages and limitations. Additionally, we highlight the growing trends and potential pathways for the expansion of MICS, underscoring the crucial role of technological advancements in shaping the future of this field. Recognizing the challenges, we strive to pave the way for further breakthroughs in minimally invasive cardiac procedures.

Texas TriValve 1.0 : a reverse‑engineered, open model of the human tricuspid valve

Nearly 1.6 million Americans suffer from a leaking tricuspid heart valve. To make matters worse, current valve repair options are far from optimal leading to recurrence of leakage in up to 30% of patients. We submit that a critical step toward improving outcomes is to better understand the "forgotten" valve. High-fidelity computer models may help in this endeavour. However, the existing models are limited by averaged or idealized geometries, material properties, and boundary conditions. In our current work, we overcome the limitations of existing models by (reverse) engineering the tricuspid valve from a beating human heart in an organ preservation system. The resulting finite-element model faithfully captures the kinematics and kinetics of the native tricuspid valve as validated against echocardiographic data and others' previous work. To showcase the value of our model, we also use it to simulate disease-induced and repair-induced changes to valve geometry and mechanics. Specifically, we simulate and compare the effectiveness of tricuspid valve repair via surgical annuloplasty and via transcatheter edge-to-edge repair. Importantly, our model is openly available for others to use. Thus, our model will allow us and others to perform virtual experiments on the healthy, diseased, and repaired tricuspid valve to better understand the valve itself and to optimize tricuspid valve repair for better patient outcomes.

A Computationally Efficient Approach to Simulate Heart Rate Effects Using a Whole Human Heart Model

Computational modeling of the whole human heart has become a valuable tool to evaluate medical devices such as leadless pacemakers, annuloplasty rings and left ventricular assist devices, since it is often difficult to replicate the complex dynamic interactions between the device and human heart in bench-top and animal tests. The Dassault Systèmes Living Heart Human Model (LHHM) is a finite-element model of whole-human-heart electromechanics that has input parameters that were previously calibrated to generate physiological responses in a healthy heart beating at 60 beat/min (resting state). This study demonstrates that, by adjusting only six physiologically meaningful parameters, the LHHM can be recalibrated to generate physiological responses in a healthy heart beating at heart rates ranging from 90−160 beat/min. These parameters are as follows: the sinoatrial node firing period decreases from 0.67 s at 90 bpm to 0.38 s at 160 bpm, atrioventricular delay decreases from 0.122 s at 90 bpm to 0.057 s at 160 bpm, preload increases 3-fold from 90 bpm to 160 bpm, body resistance at 160 bpm is 80% of that at 90 bpm, arterial stiffness at 160 bpm is 3.9 times that at 90 bpm, and a parameter relating myofiber twitch force duration and sarcomere length decreases from 238 ms/mm at 90 bpm to 175 ms/mm at 160 bpm. In addition, this study demonstrates the feasibility of using the LHHM to conduct clinical investigations in AV delay optimization and hemodynamic differences between pacing and exercise. AV delays in the ranges of 40 ms to 250 ms were simulated and stroke volume and systolic blood pressure showed clear peaks at 120 ms for 90 bpm. For a heart during exercise, the increase in cardiac output continues to 160 bpm. However, for a heart during pacing, those physiological parameter adjustments are removed that are related to changes in body oxygen requirements (preload, arterial stiffness and body resistance). Consequently, cardiac output increases initially with heart rate; as the heart rate goes up (>100 bpm), the increasing rate of cardiac output slows down and approaches a plateau.

Numerical biomechanics modelling of indirect mitral annuloplasty treatments for functional mitral regurgitation

Mitral valve regurgitation (MR) is a common valvular heart disease where an improper closure leads to leakage from the left ventricle into the left atrium. There is a need for less-invasive treatments such as percutaneous repairs for a large inoperable patient population. The aim of this study is to compare several indirect mitral annuloplasty (IMA) percutaneous repair techniques by finite-element analyses. Two types of generic IMA devices were considered, based on coronary sinus vein shortening (IMA-CS) to reduce the annulus perimeter and based on shortening of the anterior-posterior diameter (IMA-AP). The disease, its treatments, and the heart function post-repair were modelled by modifying the living heart human model (Dassault Systèmes). A functional MR pathology that represents ischaemic MR was generated and the IMA treatments were simulated in it, followed by heart function simulations with the devices and leakage quantification from blood flow simulations. All treatments were able to reduce leakage, the IMA-AP device achieved better sealing, and there was a correlation between the IMA-CS device length and the reduction in leakage. The results of this study can help in bringing IMA-AP to market, expanding the use of IMA devices, and optimizing future designs of such devices.

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.

Numerical Biomechanics Models of the Interaction Between a Novel Transcatheter Mitral Valve Device and the Subvalvular Apparatus

Objective

Mitral valve regurgitation (MR) is a common valvular heart disease where improper closing causes leakage. Currently, no transcatheter mitral valve device is commercially available. Raanani (co-author) and colleagues have previously proposed a unique rotational implantation, ensuring anchoring by metallic arms that pull the chordae tendineae. This technique is now being implemented in a novel device design. The aim of this study is to quantify the rotational implantation effect on the mitral annulus kinematics and on the stresses in the chordae and papillary muscles.

Methods

Finite element analysis of the rotational step of the implantation in a whole heart model is employed to compare 5 arm designs with varying diameters (25.9 mm to 32.4 mm) and rotation angles (up to 140°). The arm rotation that grabs the chordae was modeled when the valve was in systolic configuration.

Results

An increase in the rotation angle results in reduced mitral annulus perimeters. Larger rotation angles led to higher chordae stresses with the 29.8 mm experiencing the maximum stresses. The calculated chordae stresses suggest that arm diameter should be <27.8 mm and the rotation angle <120°.

Conclusions

The upper limit of this diameter range is preferred in order to reduce the stresses in the papillary muscles while grabbing more chords. The findings of this study can help improving the design and performance of this unique device and procedural technique.

Computed Tomography-Derived 3D Modeling to Guide Sizing and Planning of Transcatheter Mitral Valve Interventions

A plethora of catheter-based strategies have been developed to treat mitral valve disease. Evolving 3-dimensional (3D) multidetector computed tomography (MDCT) technology can accurately reconstruct the mitral valve by means of 3-dimensional computational modeling (3DCM) to allow virtual implantation of catheter-based devices. 3D printing complements computational modeling and offers implanting physician teams the opportunity to evaluate devices in life-size replicas of patient-specific cardiac anatomy. MDCT-derived 3D computational and 3D-printed modeling provides unprecedented insights to facilitate hands-on procedural planning, device training, and retrospective procedural evaluation. This overview summarizes current concepts and provides insight into the application of MDCT-derived 3DCM and 3D printing for the planning of transcatheter mitral valve replacement and closure of paravalvular leaks. Additionally, future directions in the development of 3DCM will be discussed.

Right ventricular myocardial mechanics: Multi-modal deformation, microstructure, modeling, and comparison to the left ventricle

The right ventricular myocardium, much like the rest of the right side of the heart, has been consistently understudied. Presently, little is known about its mechanics, its microstructure, and its constitutive behavior. In this work, we set out to provide the first data on the mechanics of the mature right ventricular myocardium in both simple shear and uniaxial loading and to compare these data to the mechanics of the left ventricular myocardium. To this end, we tested ovine tissue samples of the right and left ventricle under a comprehensive mechanical testing protocol that consisted of six simple shear modes and three tension/compression modes. After mechanical testing, we conducted a histology-based microstructural analysis on each right ventricular sample that yielded high resolution fiber distribution maps across the entire samples. Equipped with this detailed mechanical and histological data, we employed an inverse finite element framework to determine the optimal form and parameters for microstructure-based constitutive models. The results of our study show that right ventricular myocardium is less stiff then the left ventricular myocardium in the fiber direction, but similarly exhibits non-linear, anisotropic, and tension/compression asymmetric behavior with direction-dependent Poynting effect. In addition, we found that right ventricular myocardial fibers change angles transmurally and are dispersed within the sheet plane and normal to it. Through our inverse finite element analysis, we found that the Holzapfel model successfully fits these data, even when selectively informed by rudimentary microstructural information. And, we found that the inclusion of higher-fidelity microstructural data improved the Holzapfel model's predictive ability. Looking forward, this investigation is a critical step towards understanding the fundamental mechanical behavior of right ventricular myocardium and lays the groundwork for future whole-organ mechanical simulations.

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.

Tricuspid Annuloplasty Rings: A Quantitative Comparison of Size, Nonplanar Shape, and Stiffness

BACKGROUND

Functional tricuspid regurgitation due to annular and ventricular dilatation is increasingly recognized as a significant source of morbidity and mortality. To repair the annulus, surgeons implant one of many annuloplasty devices that differ in size, 3-dimensional (3D) shape, and stiffness. However, there have been no quantitative comparisons between various available devices.

METHODS

Three-dimensional scanning, micro-computed tomography imaging, analytical methods, and mechanical tests were used to compare 3 Edwards Lifesciences (Irvine, CA) and 3 Medtronic (Minneapolis, MN) annuloplasty devices of all available sizes. We measured in-plane metrics of maximum diameter, perimeter, area, height, as well as elevation and curvature profiles. Furthermore, we computed bending stiffness as well as the maximum and minimum axes of the bending stiffness.

RESULTS

Most annular prostheses differed little in their in-plane geometries but varied significantly in height. In-plane properties deviated significantly from measurements of healthy human tricuspid annuli. Height of the Edwards' MC3 and Medtronic's Contour 3D resembled healthy human tricuspid valve annuli, whereas the Edwards' Physio and Classic, and Medtronic's TriAd, did not. Additionally, the elevation profiles of the MC3 and Contour 3D and curvature profiles between all devices were consistent and matched those of healthy human annuli. The tested devices also differed in their bending stiffness, both in terms of absolute values and their maximum and minimum axes.

CONCLUSIONS

Contoured devices, such as Edwards' MC3 and Medtronic's Contour 3D, most accurately resembled the healthy human tricuspid annulus but differed significantly in bending stiffness. To what extent prosthesis properties and shape affect tricuspid valve function remains to be determined.

Structural Responses of Integrated Parametric Aortic Valve in an Electro-Mechanical Full Heart Model

The aortic valve (AV) is located between the left ventricle and the aorta and responsible for maintaining an outward unidirectional flow. Many AV hemodynamic and structural aspects of have been extensively studied, however, more sophisticated models are needed to better understand the AV biomechanical behavior. This study deals with integrating a new parametric AV structural model with the electro-mechanical Living Heart Human Model® (LHHM). The LHHM is a finite element model simulating human heart capable of realistic electro-mechanical simulations. Different geometric metrics of AV have been examined. New integrated structural AV model within the LHHM better predict local stresses during the cardiac cycle due to the realistic boundary condition derived from the LHHM. It was found that ellipticity index (EI), calculated as the ratio between the maximal (Max) and minimal (Min) aortic annulus (AA) diameters, well correlates with measured clinical data obtained from patients undergoing computed tomography (CT) while the annular perimeter (Perim) matches the same trend. This increases the confidence in the predicted kinematic behavior, leaflets coaptation, and the overall stresses. From the clinical aspect, the new proposed coupled and integrated AV modeling can serve as a platform for design and implementation of pre-transcatheter aortic valve replacement (TAVR) procedures.

New Insights into Valve Hemodynamics

Heart valve diseases are common disorders with five million annual diagnoses being made in the United States alone. All heart valve disorders alter cardiac hemodynamic performance; therefore, treatments aim to restore normal flow. This paper reviews the state-of-the-art clinical and engineering advancements in heart valve treatments with a focus on hemodynamics. We review engineering studies and clinical literature on the experience with devices for aortic valve treatment, as well as the latest advancements in mitral valve treatments and the pulmonic and tricuspid valves on the right side of the heart. Upcoming innovations will potentially revolutionize treatment of heart valve disorders. These advancements, and more gradual enhancements in the procedural techniques and imaging modalities, could improve the quality of life of patients suffering from valvular disease who currently cannot be treated.

Geometric description for the anatomy of the mitral valve: A review

The mitral valve is a complex anatomical structure whose physiological functioning relies on the biomechanical properties and structural integrity of its components. Their compromise can lead to mitral valve dysfunction, associated with morbidity and mortality. Therefore, a review on the morphometry of the mitral valve is crucial, more specifically on the importance of valve dimensions and shape for its function. This review initially provides a brief background on the anatomy and physiology of the mitral valve, followed by an analysis of the morphological information available. A characterisation of mathematical descriptions of several parts of the valve is performed and the impact of different dimensions and shape changes in disease is then outlined. Finally, a section regarding future directions and recommendations for the use of morphometric information in clinical analysis of the mitral valve is presented.

A relaxed growth modeling framework for controlling growth-induced residual stresses

BACKGROUND

Constitutive models of the mechanical response of soft tissues have been established and are widely accepted, but models of soft tissues remodeling are more controversial. Specifically for growth, one important question arises pertaining to residual stresses: existing growth models inevitably introduce residual stresses, but it is not entirely clear if this is physiological or merely an artifact of the modeling framework. As a consequence, in simulating growth, some authors have chosen to keep growth-induced residual stresses, and others have chosen to remove them.

METHODS

In this paper, we introduce a novel "relaxed growth" framework allowing for a fine control of the amount of residual stresses generated during tissue growth. It is a direct extension of the classical framework of the multiplicative decomposition of the transformation gradient, to which an additional sub-transformation is introduced in order to let the original unloaded configuration evolve, hence relieving some residual stresses. We provide multiple illustrations of the framework mechanical response, on time-driven constrained growth as well as the strain-driven growth problem of the artery under internal pressure, including the opening angle experiment.

FINDINGS

The novel relaxed growth modeling framework introduced in this paper allows for a better control of growth-induced residual stresses compared to standard growth models based on the multiplicative decomposition of the transformation gradient.

INTERPRETATION

Growth-induced residual stresses should be better handled in soft tissues biomechanical models, especially in patient-specific models of diseased organs that are aimed at augmented diagnosis and treatment optimization.

Using machine learning to characterize heart failure across the scales

Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the progression of heart failure and guide personalized treatment planning. Yet, the predictive potential of cardiac growth models remains poorly understood. Here, we quantify predictive power of a stretch-driven growth model using a chronic porcine heart failure model, subject-specific multiscale simulation, and machine learning techniques. We combine hierarchical modeling, Bayesian inference, and Gaussian process regression to quantify the uncertainty of our experimental measurements during an 8-week long study of volume overload in six pigs. We then propagate the experimental uncertainties from the organ scale through our computational growth model and quantify the agreement between experimentally measured and computationally predicted alterations on the cellular scale. Our study suggests that stretch is the major stimulus for myocyte lengthening and demonstrates that a stretch-driven growth model alone can explain [Formula: see text] of the observed changes in myocyte morphology. We anticipate that our approach will allow us to design, calibrate, and validate a new generation of multiscale cardiac growth models to explore the interplay of various subcellular-, cellular-, and organ-level contributors to heart failure. Using machine learning in heart failure research has the potential to combine information from different sources, subjects, and scales to provide a more holistic picture of the failing heart and point toward new treatment strategies.

Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification

Prolonged QT intervals are a major risk factor for ventricular arrhythmias and a leading cause of sudden cardiac death. Various drugs are known to trigger QT interval prolongation and increase the proarrhythmic potential. Yet, how precisely the action of drugs on the cellular level translates into QT interval prolongation on the whole organ level remains insufficiently understood. Here we use machine learning techniques to systematically characterize the effect of 30 common drugs on the QT interval. We combine information from high fidelity three-dimensional human heart simulations with low fidelity one-dimensional cable simulations to build a surrogate model for the QT interval using multi-fidelity Gaussian process regression. Once trained and cross-validated, we apply our surrogate model to perform sensitivity analysis and uncertainty quantification. Our sensitivity analysis suggests that compounds that block the rapid delayed rectifier potassium current I Kr have the greatest prolonging effect of the QT interval, and that blocking the L-type calcium current I CaL and late sodium current I NaL shortens the QT interval. Our uncertainty quantification allows us to propagate the experimental variability from individual block-concentration measurements into the QT interval and reveals that QT interval uncertainty is mainly driven by the variability in I Kr block. In a final validation study, we demonstrate an excellent agreement between our predicted QT interval changes and the changes observed in a randomized clinical trial for the drugs dofetilide, quinidine, ranolazine, and verapamil. We anticipate that both the machine learning methods and the results of this study will have great potential in the efficient development of safer drugs.

Multiscale characterization of heart failure

Dilated cardiomyopathy is a progressive irreversible disease associated with contractile dysfunction and heart failure. During dilated cardiomyopathy, elevated diastolic wall strains trigger mechanotransduction pathways that initiate the addition of sarcomeres in series and an overall increase in myocyte length. At the whole organ level, this results in a chronic dilation of the ventricles, an increase in end diastolic and end systolic volumes, and a decrease in ejection fraction. However, how exactly changes in sarcomere number translate into changes in myocyte morphology, and how these cellular changes translate into ventricular dilation remains incompletely understood. Here we combined a chronic animal study, continuum growth modeling, and machine learning to quantify correlations between sarcomere dynamics, myocyte morphology, and ventricular dilation. In an eight-week long volume overload study of six pigs, we found that the average sarcomere number increased by +3.8%/week, from 47 to 62, resulting in a myocyte lengthening of +3.3%/week, from 85 to 108 μm, while the sarcomere length and myocyte width remained unchanged. At the same time, the average end diastolic volume increased by +6.0%/week. Using continuum growth modeling and Bayesian inference, we correlated alterations on the subcellular, cellular, and organ scales and found that the serial sarcomere number explained 88% of myocyte lengthening, which, in turn, explained 54% of cardiac dilation. Our results demonstrate that sarcomere number and myocyte length are closely correlated and constitute the major determinants of dilated heart failure. We anticipate our study to be a starting point for more sophisticated multiscale models of heart failure. Our study suggests that altering sarcomere turnover-and with it myocyte morphology and ventricular dimensions-could be a potential therapeutic target to attenuate or reverse the progression of heart failure. STATEMENT OF SIGNIFICANCE: Heart failure is a significant global health problem that affects more than 25 million people worldwide and increases in prevalence as the population ages. Heart failure has been studied excessively at various scales; yet, there is no compelling concept to connect knowledge from the subcellular, cellular, and organ level across the scales. Here we combined a chronic animal study, continuum growth modeling, and machine learning to quantify correlations between sarcomere dynamics, myocyte morphology, and ventricular dilation. We found that the serial sarcomere number explained 88% of myocyte lengthening, which, in turn, explained 54% of cardiac dilation. Our results show that sarcomere number and myocyte length are closely correlated and constitute the major determinants of dilated heart failure. This suggests that altering the sarcomere turnover-and with it myocyte morphology and ventricular dimensions-could be a potential therapeutic target to attenuate or reverse heart failure.

Virtual downsizing for decision support in mitral valve repair

PURPOSE

Various options are available for the treatment of mitral valve insufficiency, including reconstructive approaches such as annulus correction through ring implants. The correct choice of general therapy and implant is relevant for an optimal outcome. Additional to guidelines, decision support systems (DSS) can provide decision aid by means of virtual intervention planning and predictive simulations. Our approach on virtual downsizing is one of the virtual intervention tools that are part of the DSS workflow. It allows for emulating a ring implantation based on patient-specific lumen geometry and vendor-specific implants.

METHODS

Our approach is fully automatic and relies on a lumen mask and an annulus contour as inputs. Both are acquired from previous DSS workflow steps. A virtual surface- and contour-based model of a vendor-specific ring design (26-40 mm) is generated. For each case, the ring geometry is positioned with respect to the original, patient-specific annulus and additional anatomical landmarks. The lumen mesh is parameterized to allow for a vertex-based deformation with respect to the user-defined annulus. Derived from post-interventional observations, specific deformation schemes are applied to atrium and ventricle and the lumen mesh is altered with respect to the ring location.

RESULTS

For quantitative evaluation, the surface distance between the deformed lumen mesh and segmented post-operative echo lumen close to the annulus was computed for 11 datasets. The results indicate a good agreement. An arbitrary subset of six datasets was used for a qualitative evaluation of the complete lumen. Two domain experts compared the deformed lumen mesh with post-interventional echo images. All deformations were deemed plausible.

CONCLUSION

Our approach on virtual downsizing allows for an automatic creation of plausible lumen deformations. As it takes only a few seconds to generate results, it can be added to a virtual intervention toolset without unnecessarily increasing the pipeline complexity.

Computational Pre-surgical Planning of Arterial Patch Reconstruction: Parametric Limits and In Vitro Validation

Surgical treatment of congenital heart disease (CHD) involves complex vascular reconstructions utilizing artificial and native surgical materials. A successful surgical reconstruction achieves an optimal hemodynamic profile through the graft in spite of the complex post-operative vessel growth pattern and the altered pressure loading. This paper proposes a new in silico patient-specific pre-surgical planning framework for patch reconstruction and investigates its computational feasibility. The proposed protocol is applied to the patch repair of main pulmonary artery (MPA) stenosis in the Tetralogy of Fallot CHD template. The effects of stenosis grade, the three-dimensional (3D) shape of the surgical incision and material properties of the artificial patch are investigated. The release of residual stresses due to the surgical incision and the extra opening of the incision gap for patch implantation are simulated through a quasi-static finite-element vascular model with shell elements. Implantation of different unloaded patch shapes is simulated. The patched PA configuration is pressurized to the physiological post-operative blood pressure levels of 25 and 45 mmHg and the consequent post-operative stress distributions and patched artery shapes are computed. Stress–strain data obtained in-house, through the biaxial tensile tests for the mechanical properties of common surgical patch materials, Dacron, Polytetrafluoroethylene, human pericardium and porcine xenopericardium, are employed to represent the mechanical behavior of the patch material. Finite-element model is experimentally validated through the actual patch surgery reconstructions performed on the 3D printed anatomical stenosis replicas. The post-operative recovery of the initially narrowed lumen area and post-op tortuosity are quantified for all modeled cases. A computational fluid dynamics solver is used to evaluate post-operative pressure drop through the patch-reconstructed outflow tract. According to our findings, the shorter incisions made at the throat result in relatively low local peak stress values compared to other patch design alternatives. Longer cut and double patch cases are the most effective in repairing the initial stenosis level. After the patch insertion, the pressure drop in the artery due to blood flow decreases from 9.8 to 1.35 mmHg in the conventional surgical configuration. These results are in line with the clinical experience where a pressure gradient at or above 50 mmHg through the MPA can be an indication to intervene. The main strength of the proposed pre-surgical planning framework is its capability to predict the intra-operative and post-operative 3D vascular shape changes due to intramural pressure, cut length and configuration, for both artificial and native patch materials.

Predicting drug-induced arrhythmias by multiscale modeling

Drugs often have undesired side effects. In the heart, they can induce lethal arrhythmias such as torsades de pointes. The risk evaluation of a new compound is costly and can take a long time, which often hinders the development of new drugs. Here, we establish a high-resolution, multiscale computational model to quickly assess the cardiac toxicity of new and existing drugs. The input of the model is the drug-specific current block from single cell electrophysiology; the output is the spatio-temporal activation profile and the associated electrocardiogram. We demonstrate the potential of our model for a low-risk drug, ranolazine, and a high-risk drug, quinidine: For ranolazine, our model predicts a prolonged QT interval of 19.4% compared with baseline and a regular sinus rhythm at 60.15 beats per minute. For quinidine, our model predicts a prolonged QT interval of 78.4% and a spontaneous development of torsades de pointes both in the activation profile and in the electrocardiogram. Our model reveals the mechanisms by which electrophysiological abnormalities propagate across the spatio-temporal scales, from specific channel blockage, via altered single cell action potentials and prolonged QT intervals, to the spontaneous emergence of ventricular tachycardia in the form of torsades de pointes. Our model could have important implications for researchers, regulatory agencies, and pharmaceutical companies on rationalizing safe drug development and reducing the time-to-market of new drugs.

Predicting the cardiac toxicity of drugs using a novel multiscale exposure-response simulator

A common but serious side effect of many drugs is torsades de pointes, a rhythm disorder that can have fatal consequences. Torsadogenic risk has traditionally been associated with blockage of a specific potassium channel and an increased recovery period in the electrocardiogram. However, the mechanisms that trigger torsades de pointes remain incompletely understood. Here we establish a computational model to explore how drug-induced effects propagate from the single channel, via the single cell, to the whole heart level. Our mechanistic exposure-response simulator translates block-concentration characteristics for arbitrary drugs into three-dimensional excitation profiles and electrocardiogram recordings to rapidly assess torsadogenic risk. For the drug of dofetilide, we show that this risk is highly dose-dependent: at a concentration of 1x, QT prolongation is 55% but the heart maintains its regular sinus rhythm; at 5.7x, QT prolongation is 102% and the heart spontaneously transitions into torsades de points; at 30x, QT prolongation is 132% and the heart adapts a quasi-depolarized state with numerous rapidly flickering local excitations. Our simulations suggest that neither potassium channel blockage nor QT interval prolongation alone trigger torsades de pointes. The underlying mechanism predicted by our model is early afterdepolarization, which translates into pronounced U waves in the electrocardiogram, a signature that is correctly predicted by our model. Beyond the risk assessment of existing drugs, our exposure-response simulator can become a powerful tool to optimize the co-administration of drugs and, ultimately, guide the design of new drugs toward reducing life threatening drug-induced rhythm disorders in the heart.

Engineering Analysis of Tricuspid Annular Dynamics in the Beating Ovine Heart

Functional tricuspid regurgitation is a significant source of morbidity and mortality in the US. Furthermore, treatment of functional tricuspid regurgitation is suboptimal with significant recurrence rates, which may, at least in part, be due to our limited knowledge of the relationship between valvular shape and function. Here we study the dynamics of the healthy in vivo ovine tricuspid annulus to improve our understanding of normal annular deformations throughout the cardiac cycle. To this end, we determine both clinical as well as engineering metrics of in vivo annular dynamics based on sonomicrometry crystals surgically attached to the annulus. We confirm that the tricuspid annulus undergoes large dynamic changes in area, perimeter, height, and eccentricity throughout the cardiac cycle. This deformation may be described as asymmetric in-plane motion of the annulus with minor out-of-plane motion. In addition, we employ strain and curvature to provide mechanistic insight into the origin of this deformation. Specifically, we find that strain and curvature vary considerable across the annulus with highly localized minima and maxima resulting in aforementioned configurational changes throughout the cardiac cycle. It is our hope that these data provide valuable information for clinicians and engineers alike and ultimately help us improve treatment of functional tricuspid regurgitation.

Modelling mitral valvular dynamics-current trend and future directions

Dysfunction of mitral valve causes morbidity and premature mortality and remains a leading medical problem worldwide. Computational modelling aims to understand the biomechanics of human mitral valve and could lead to the development of new treatment, prevention and diagnosis of mitral valve diseases. Compared with the aortic valve, the mitral valve has been much less studied owing to its highly complex structure and strong interaction with the blood flow and the ventricles. However, the interest in mitral valve modelling is growing, and the sophistication level is increasing with the advanced development of computational technology and imaging tools. This review summarises the state-of-the-art modelling of the mitral valve, including static and dynamics models, models with fluid-structure interaction, and models with the left ventricle interaction. Challenges and future directions are also discussed.

A coupled mitral valve-left ventricle model with fluid-structure interaction

Understanding the interaction between the valves and walls of the heart is important in assessing and subsequently treating heart dysfunction. This study presents an integrated model of the mitral valve (MV) coupled to the left ventricle (LV), with the geometry derived from in vivo clinical magnetic resonance images. Numerical simulations using this coupled MV-LV model are developed using an immersed boundary/finite element method. The model incorporates detailed valvular features, left ventricular contraction, nonlinear soft tissue mechanics, and fluid-mediated interactions between the MV and LV wall. We use the model to simulate cardiac function from diastole to systole. Numerically predicted LV pump function agrees well with in vivo data of the imaged healthy volunteer, including the peak aortic flow rate, the systolic ejection duration, and the LV ejection fraction. In vivo MV dynamics are qualitatively captured. We further demonstrate that the diastolic filling pressure increases significantly with impaired myocardial active relaxation to maintain a normal cardiac output. This is consistent with clinical observations. The coupled model has the potential to advance our fundamental knowledge of mechanisms underlying MV-LV interaction, and help in risk stratification and optimisation of therapies for heart diseases.

An augmented iterative method for identifying a stress-free reference configuration in image-based biomechanical modeling

Continued advances in computational power and methods have enabled image-based biomechanical modeling to become an important tool in basic science, diagnostic and therapeutic medicine, and medical device design. One of the many challenges of this approach, however, is identification of a stress-free reference configuration based on in vivo images of loaded and often prestrained or residually stressed soft tissues and organs. Fortunately, iterative methods have been proposed to solve this inverse problem, among them Sellier's method. This method is particularly appealing because it is easy to implement, convergences reasonably fast, and can be coupled to nearly any finite element package. By means of several practical examples, however, we demonstrate that in its original formulation Sellier's method is not optimally fast and may not converge for problems with large deformations. Fortunately, we can also show that a simple, inexpensive augmentation of Sellier's method based on Aitken's delta-squared process can not only ensure convergence but also significantly accelerate the method.