A Lumped Two-Compartment Model for Simulation of Ventricular Pump and Tissue Mechanics in Ischemic Heart Disease

Koopsen, Tijmen; Osta, N Van; Loon, Tim van; Nieuwenhoven, Frans A. van; Prinzen, Frits W.; Klarenbosch, Bas R van; Kirkels, Feddo P.; Teske, Arco J.; Vernooy, Kevin; Delhaas, Tammo; Lumens, Joost

doi:10.3389/fphys.2022.782592

Cited by 7 publications

(14 citation statements)

References 42 publications

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“…Using a combination of echocardiography and sonomicrometry, Lyseggen et al showed that the viable LV myocardial pressure-strain curve switched from counter-clockwise to clockwise after 15 minutes of ischemia. The recent study by Koopsen et al15 reproduced similar plots using a two-compartment model of the ischemic ventricle and simulated the effects of reperfusion that parallel results reported by Lyseggen et al Our model framework, like Koopsen et al, illustrates…”

supporting

confidence: 80%

“…Witzenburg also accounted for compensatory changes in afterload parameters and showed that model predictions matched well with prior experimental (canine) studies. Koopsen et al 15 considered a similar, two compartment approach for simulating LV infarction; these authors included biventricular interaction and showed agreement with previously obtained canine data from Lyseggen et al 17 .…”

Section: Simulated Ischemiasupporting

confidence: 57%

“…We use a previously developed multiscale cardiovascular model 15,16 . The model components include 1) a modified Hill model of sarcomere shortening, 2) an empirical model of cardiomyocyte calcium handling, 3) four spherical cardiac chambers including biventricular interaction, and 4) a zero-dimensional (0D) hemodynamics model.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The cutting-edge reduced order model of ventricular interaction is the three-segment (“TriSeg”) model by Lumens et al, which represents the LV, RV, and S as thick walled, spherical chambers driven by myocyte dynamics 16 . Several authors have had success in using this framework to simulate disease, such as pulmonary hypertension 29 and LV ischemia 15 . These models contain numerous parameters, requiring a formal model analysis to determine which parameters are influential and identifiable given limited data 4 .…”

Section: Introductionmentioning

confidence: 99%

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Biventricular interaction during acute left ventricular ischemia in mice: a combined in-vivo and in-silico approach

Colebank

Taylor

Hacker

et al. 2023

Preprint

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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.

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supporting

confidence: 80%

Section: Simulated Ischemiasupporting

confidence: 57%

Section: Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Biventricular interaction during acute left ventricular ischemia in mice: a combined in-vivo and in-silico approach

Colebank

Taylor

Hacker

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These parameters are chosen because they are the most sensitive in determining ventricular strain patterns in patients with electromechanical dyssynchrony and myocardial infarction. The methodology to simulate virtual patients with myocardial infarction using a lumped two-compartment modeling approach integrated into the CircAdapt model is previously introduced in [17]. The resulting cohort is filtered with global hemodynamic parameter values and strain indices representative of a CRT cohort, found in literature, guidelines [18], and clinical data.…”

Section: A Circadapt Virtual Patient Datasetmentioning

confidence: 99%

Deep Learning for Multi-Level Detection and Localization of Myocardial Scars Based on Regional Strain Validated on Virtual Patients

et al. 2023

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How well the heart is functioning can be quantified through measurements of myocardial deformation via echocardiography. Clinical assessment of cardiac function is generally focused on global indices of relative shortening; however, segmental strain indices have been shown to be abnormal in regions of myocardial disease such as scarring. In this work, we propose a single framework to predict myocardial scars at global, territorial, and segmental levels using regional myocardial strain traces as input to a convolutional neural network (CNN). An anatomically meaningful representation of the input data from the clinically standard bullseye representation to a multi-channel 2D image is proposed, thus enabling the use of state-of-the-art neural network configurations. A Fully Convolutional Network (FCN) is trained to detect and localize myocardial scar from regional left ventricular (LV) strain traces. Simulated regional strain data from a controlled dataset of virtual patients with varying degrees and locations of myocardial scar is used for training and validation. The proposed method successfully detects and localizes the scars on 98% of the 5490 left ventricle (LV) segments of the 305 patients in the test set using strain traces only. Due to the sparse existence of scar in the dataset, only 10% of the LV segments are scarred. Taking the imbalance into account, the class balanced accuracy is calculated as 95%. The proposed method proves successful on the strain traces of the virtual cohort and offers the potential to solve the regional myocardial scar detection problem on the strain traces of the real patient cohorts.

show abstract

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

A Lumped Two-Compartment Model for Simulation of Ventricular Pump and Tissue Mechanics in Ischemic Heart Disease

Cited by 7 publications

References 42 publications

Biventricular interaction during acute left ventricular ischemia in mice: a combined in-vivo and in-silico approach

Biventricular interaction during acute left ventricular ischemia in mice: a combined in-vivo and in-silico approach

Deep Learning for Multi-Level Detection and Localization of Myocardial Scars Based on Regional Strain Validated on Virtual Patients

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

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