Human biventricular electromechanical simulations on the progression of electrocardiographic and mechanical abnormalities in post-myocardial infarction.
TL;DR: In this article, a human-based multiscale modelling and simulation framework enables mechanistic investigations into patho-physiological electrophysiological and mechanical behaviour and can serve as testbed to guide the optimization of pharmacological and electrical therapies.
Abstract: AIMS Develop, calibrate and evaluate with clinical data a human electromechanical modelling and simulation framework for multiscale, mechanistic investigations in healthy and post-myocardial infarction (MI) conditions, from ionic to clinical biomarkers. METHODS AND RESULTS Human healthy and post-MI electromechanical simulations were conducted with a novel biventricular model, calibrated and evaluated with experimental and clinical data, including torso/biventricular anatomy from clinical magnetic resonance, state-of-the-art human-based membrane kinetics, excitation-contraction and active tension models, and orthotropic electromechanical coupling. Electromechanical remodelling of the infarct/ischaemic region and the border zone were simulated for ischaemic, acute, and chronic states in a fully transmural anterior infarct and a subendocardial anterior infarct. The results were compared with clinical electrocardiogram and left ventricular ejection fraction (LVEF) data at similar states. Healthy model simulations show LVEF 63%, with 11% peak systolic wall thickening, QRS duration and QT interval of 100 ms and 330 ms. LVEF in ischaemic, acute, and chronic post-MI states were 56%, 51%, and 52%, respectively. In linking the three post-MI simulations, it was apparent that elevated resting potential due to hyperkalaemia in the infarcted region led to ST-segment elevation, while a large repolarization gradient corresponded to T-wave inversion. Mechanically, the chronic stiffening of the infarct region had the benefit of improving systolic function by reducing infarct bulging at the expense of reducing diastolic function by inhibiting inflation. CONCLUSION Our human-based multiscale modelling and simulation framework enables mechanistic investigations into patho-physiological electrophysiological and mechanical behaviour and can serve as testbed to guide the optimization of pharmacological and electrical therapies.
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TL;DR: In this article , the authors proposed a mathematical and numerical model for cardiac electromechanics, wherein biophysically detailed core models describe the different physical processes concurring to the cardiac function.
40 citations
TL;DR: In this paper, a universal feature-complete cardiac electro-mechanics (EM) modeling framework is proposed for predicting acute outcomes of EM therapies. But, the model is not suitable for the analysis of clinical data.
20 citations
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TL;DR: In this paper, a 3D biventricular electromechanical model coupled with a 0D closed-loop model of the whole cardiovascular system is presented to account for the interaction between the heart and the circulatory system that determines pressures and volumes loads in the heart chambers.
Abstract: Two crucial factors for accurate numerical simulations of cardiac electromechanics, which are also essential to reproduce the synchronous activity of the heart, are: i) accounting for the interaction between the heart and the circulatory system that determines pressures and volumes loads in the heart chambers; ii) reconstructing the muscular fiber architecture that drives the electrophysiology signal and the myocardium contraction. In this work, we present a 3D biventricular electromechanical model coupled with a 0D closed-loop model of the whole cardiovascular system that addresses the two former crucial factors. With this aim, we introduce a boundary condition for the mechanical problem that accounts for the neglected part of the domain located on top of the biventricular basal plane and that is consistent with the principles of momentum and energy conservation. We also discuss in detail the coupling conditions that stand behind the 3D and the 0D models. We perform electromechanical simulations in physiological conditions using the 3D-0D model and we show that our results match the experimental data of relevant mechanical biomarkers available in literature. Furthermore, we investigate different arrangements in cross-fibers active contraction. We prove that an active tension along the sheet direction counteracts the myofiber contraction, while the one along the sheet-normal direction enhances the cardiac work. Finally, several myofiber architectures are analysed. We show that a different fiber field in the septal area and in the transmural wall effect the pumping functionality of the left ventricle.
14 citations
28 Jul 2021
TL;DR: The main existing multiscales frameworks to equip cellular models of cardiac electrophysiology with a β-ARS response are described and various applications of these multiscale frameworks in the study of cardiac pathology are outlined.
Abstract: β-adrenergic receptor stimulation (β-ARS) is a physiological mechanism that regulates cardiovascular function under stress conditions or physical exercise. Triggered during the so-called “fight-or-flight” response, the activation of the β-adrenergic receptors located on the cardiomyocyte membrane initiates a phosphorylation cascade of multiple ion channel targets that regulate both cellular excitability and recovery and of different proteins involved in intracellular calcium handling. As a result, β-ARS impacts both the electrophysiological and the mechanical response of the cardiomyocyte. β-ARS also plays a crucial role in several cardiac pathologies, greatly modifying cardiac output and potentially causing arrhythmogenic events. Mathematical patient-specific models are nowadays envisioned as an important tool for the personalised study of cardiac disease, the design of tailored treatments, or to inform risk assessment. Despite that, only a reduced number of computational studies of heart disease have incorporated β-ARS modelling. In this review, we describe the main existing multiscale frameworks to equip cellular models of cardiac electrophysiology with a β-ARS response. We also outline various applications of these multiscale frameworks in the study of cardiac pathology. We end with a discussion of the main current limitations and the future steps that need to be taken to adapt these models to a clinical environment and to incorporate them in organ-level simulations.
10 citations
TL;DR: In this article, 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.
Abstract: 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.
7 citations
References
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TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Abstract: —The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of He...
11,407 citations
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Abstract: ACC
: American College of Cardiology
ACE
: angiotensin-converting enzyme
ACS
: acute coronary syndrome
AF
: atrial fibrillation
AGNES
: Arrhythmia Genetics in the Netherlands
AHA
: American Heart Association
AMIOVIRT
: AMIOdarone Versus Implantable cardioverter-defibrillator:
2,830 citations
TL;DR: In this article, the authors proposed AMIOdarone versus implantable cardioverter-defibrillator (ICD-DV) for the treatment of atrial fibrillation.
Abstract: ACC
: American College of Cardiology
ACE
: angiotensin-converting enzyme
ACS
: acute coronary syndrome
AF
: atrial fibrillation
AGNES
: Arrhythmia Genetics in the Netherlands
AHA
: American Heart Association
AMIOVIRT
: AMIOdarone Versus Implantable cardioverter-defibrillator:
1,513 citations
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901 citations
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762 citations