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      Biventricular Interaction During Acute Left Ventricular Ischemia in Mice: A Combined In-Vivo and In-Silico Approach

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          Abstract

          Computational models provide an efficient paradigm for integrating and linking multiple spatial and temporal scales. However, these models are difficult to parameterize and match to experimental data. Recent advances in both data collection and model analyses have helped overcome this limitation. Here, we combine a multiscale, biventricular interaction model with mouse data before and after left ventricular (LV) ischemia. Sensitivity analyses are used to identify the most influential parameters on pressure and volume predictions. The subset of influential model parameters are calibrated to biventricular pressure–volume loop data ( n = 3) at baseline. Each mouse underwent left anterior descending coronary artery ligation, during which changes in fractional shortening and RV pressure–volume dynamics were recorded. Using the calibrated model, we simulate acute LV ischemia and contrast outputs at baseline and in simulated ischemia. Our baseline simulations align with the LV and RV data, and our predictions during ischemia complement recorded RV data and prior studies on LV function during myocardial infarction. We show that a model with both biventricular mechanical interaction and systems-level cardiovascular dynamics can quantitatively reproduce in-vivo data and qualitatively match prior findings from animal studies on LV ischemia.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s10439-023-03293-z.

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          Most cited references36

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          Myocardial strain imaging: how useful is it in clinical decision making?

          Myocardial strain is a principle for quantification of left ventricular (LV) function which is now feasible with speckle-tracking echocardiography. The best evaluated strain parameter is global longitudinal strain (GLS) which is more sensitive than left ventricular ejection fraction (LVEF) as a measure of systolic function, and may be used to identify sub-clinical LV dysfunction in cardiomyopathies. Furthermore, GLS is recommended as routine measurement in patients undergoing chemotherapy to detect reduction in LV function prior to fall in LVEF. Intersegmental variability in timing of peak myocardial strain has been proposed as predictor of risk of ventricular arrhythmias. Strain imaging may be applied to guide placement of the LV pacing lead in patients receiving cardiac resynchronization therapy. Strain may also be used to diagnose myocardial ischaemia, but the technology is not sufficiently standardized to be recommended as a general tool for this purpose. Peak systolic left atrial strain is a promising supplementary index of LV filling pressure. The strain imaging methodology is still undergoing development, and further clinical trials are needed to determine if clinical decisions based on strain imaging result in better outcome. With this important limitation in mind, strain may be applied clinically as a supplementary diagnostic method.
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            The importance of the pericardium for cardiac biomechanics: from physiology to computational modeling

            The human heart is enclosed in the pericardial cavity. The pericardium consists of a layered thin sac and is separated from the myocardium by a thin film of fluid. It provides a fixture in space and frictionless sliding of the myocardium. The influence of the pericardium is essential for predictive mechanical simulations of the heart. However, there is no consensus on physiologically correct and computationally tractable pericardial boundary conditions. Here, we propose to model the pericardial influence as a parallel spring and dashpot acting in normal direction to the epicardium. Using a four-chamber geometry, we compare a model with pericardial boundary conditions to a model with fixated apex. The influence of pericardial stiffness is demonstrated in a parametric study. Comparing simulation results to measurements from cine magnetic resonance imaging reveals that adding pericardial boundary conditions yields a better approximation with respect to atrioventricular plane displacement, atrial filling, and overall spatial approximation error. We demonstrate that this simple model of pericardial-myocardial interaction can correctly predict the pumping mechanisms of the heart as previously assessed in clinical studies. Utilizing a pericardial model not only can provide much more realistic cardiac mechanics simulations but also allows new insights into pericardial-myocardial interaction which cannot be assessed in clinical measurements yet.
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              Fast Simulation of Mechanical Heterogeneity in the Electrically Asynchronous Heart Using the MultiPatch Module

              Cardiac electrical asynchrony occurs as a result of cardiac pacing or conduction disorders such as left bundle-branch block (LBBB). Electrically asynchronous activation causes myocardial contraction heterogeneity that can be detrimental for cardiac function. Computational models provide a tool for understanding pathological consequences of dyssynchronous contraction. Simulations of mechanical dyssynchrony within the heart are typically performed using the finite element method, whose computational intensity may present an obstacle to clinical deployment of patient-specific models. We present an alternative based on the CircAdapt lumped-parameter model of the heart and circulatory system, called the MultiPatch module. Cardiac walls are subdivided into an arbitrary number of patches of homogeneous tissue. Tissue properties and activation time can differ between patches. All patches within a wall share a common wall tension and curvature. Consequently, spatial location within the wall is not required to calculate deformation in a patch. We test the hypothesis that activation time is more important than tissue location for determining mechanical deformation in asynchronous hearts. We perform simulations representing an experimental study of myocardial deformation induced by ventricular pacing, and a patient with LBBB and heart failure using endocardial recordings of electrical activation, wall volumes, and end-diastolic volumes. Direct comparison between simulated and experimental strain patterns shows both qualitative and quantitative agreement between model fibre strain and experimental circumferential strain in terms of shortening and rebound stretch during ejection. Local myofibre strain in the patient simulation shows qualitative agreement with circumferential strain patterns observed in the patient using tagged MRI. We conclude that the MultiPatch module produces realistic regional deformation patterns in the asynchronous heart and that activation time is more important than tissue location within a wall for determining myocardial deformation. The CircAdapt model is therefore capable of fast and realistic simulations of dyssynchronous myocardial deformation embedded within the closed-loop cardiovascular system.
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                Author and article information

                Contributors
                nchesler@uci.edu
                Journal
                Ann Biomed Eng
                Ann Biomed Eng
                Annals of Biomedical Engineering
                Springer International Publishing (Cham )
                0090-6964
                1573-9686
                15 July 2023
                15 July 2023
                2023
                : 51
                : 11
                : 2528-2543
                Affiliations
                [1 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, and Department of Biomedical Engineering, , University of California, Irvine, ; Irvine, CA USA
                [2 ]Cardiovascular Research Center, University of Wisconsin-Madison, ( https://ror.org/01y2jtd41) Madison, WI USA
                Author notes

                Associate Editor Umberto Morbiducci oversaw the review of this article.

                Author information
                http://orcid.org/0000-0002-2294-9124
                http://orcid.org/0000-0002-7612-5796
                Article
                3293
                10.1007/s10439-023-03293-z
                10598180
                37453977
                9ce09e2c-1729-4ef5-a4e8-decd42f4e915
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 27 January 2023
                : 17 June 2023
                Funding
                Funded by: National Institutes of Health
                Award ID: TL1 TR001415
                Award ID: R01 HL147590
                Award ID: R01HL154624
                Award Recipient :
                Categories
                Original Article
                Custom metadata
                © Biomedical Engineering Society 2023

                Biomedical engineering
                computational model,parameter estimation,myocardial infarction,biventricular interaction,sensitivity analysis,multiscale modeling

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