Electromechanical modeling of human ventricles with ischemic cardiomyopathy: numerical simulations in sinus rhythm and under arrhythmia

Salvador, Matteo; Fedele, Marco; Africa, Pasquale Claudio; Sung, Eric; Dedè, Luca; Prakosa, Adityo; Chrispin, Jonathan; Trayanova, Natalia A.; Quarteroni, Alfio

doi:10.1016/j.compbiomed.2021.104674

Cited by 39 publications

(25 citation statements)

References 68 publications

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“…Finally, our virtual-heart technology does not incorporate cardiac mechanics into the arrhythmia risk stratification, however we do not expect that including it will alter our prediction. Indeed, a recent paper examining VA in a patient-specific model with structural remodeling that incorporated mechanics Salvador et al, 2021 demonstrated that mechanics only modifies the stability of the arrhythmia, but does not alter its inducibility.…”

Section: Discussionmentioning

confidence: 99%

Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy

O’Hara

Binka²,

Prakosa

et al. 2022

eLife

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Hypertrophic cardiomyopathy (HCM) is associated with risk of sudden cardiac death (SCD) due to ventricular arrhythmias (VA) arising from the proliferation of fibrosis in the heart. Current clinical risk stratification criteria inadequately identify at-risk patients in need of primary prevention of VA. Here, we use mechanistic computational modeling of the heart to analyze how HCM-specific remodeling promotes arrhythmogenesis and to develop a personalized strategy to forecast risk of VAs in these patients. We combine contrast-enhanced cardiac magnetic-resonance (CMR) imaging and T1 mapping data to construct digital replicas of HCM patient hearts that represent the patient-specific distribution of focal and diffuse fibrosis and evaluate the substrate propensity to VA. Our analysis indicates that the presence of diffuse fibrosis, which is rarely assessed in these patients, increases arrhythmogenic propensity. In forecasting future VA events in HCM patients, the imaging-based computational heart approach achieved 84.6%, 76.9%, and 80.1% sensitivity, specificity, and accuracy, respectively, and significantly outperformed current clinical risk predictors. This novel VA risk assessment may have the potential to prevent SCD and help deploy primary prevention appropriately in HCM patients.

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Section: Discussionmentioning

confidence: 99%

Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy

O’Hara

Binka²,

Prakosa

et al. 2022

eLife

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“…Furthermore, the electro-mechanical coupling was disregarded in our study. For the inclusion of mechanical and hemodynamical models in the numerical simulations of arrhythmias, we refer to the recent paper (Salvador et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Role of Scar and Border Zone Geometry on the Genesis and Maintenance of Re-Entrant Ventricular Tachycardia in Patients With Previous Myocardial Infarction

et al. 2022

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In patients with healed myocardial infarction, the left ventricular ejection fraction is characterized by low sensitivity and specificity in the prediction of future malignant arrhythmias. Thus, there is the need for new parameters in daily practice to perform arrhythmic risk stratification. The aim of this study is to identify some features of proarrhythmic geometric configurations of scars and border zones (BZ), by means of numerical simulations based on left ventricular models derived from post myocardial infarction patients. Two patients with similar clinical characteristics were included in this study. Both patients exhibited left ventricular scars characterized by subendo- and subepicardial BZ and a transmural BZ isthmus. The scar of patient #1 was significantly larger than that of patient #2, whereas the transmural BZ isthmus and the subdendo- and subepicardial BZs of patient #2 were thicker than those of patient #1. Patient #1 was positive at electrophysiologic testing, whereas patient #2 was negative. Based on the cardiac magnetic resonance (CMR) data, we developed a geometric model of the left ventricles of the two patients, taking into account the position, extent, and topological features of scars and BZ. The numerical simulations were based on the anisotropic monodomain model of electrocardiology. In the model of patient #1, sustained ventricular tachycardia (VT) was inducible by an S2 stimulus delivered at any of the six stimulation sites considered, while in the model of patient #2 we were not able to induce sustained VT. In the model of patient #1, making the subendo- and subepicardial BZs as thick as those of patient #2 did not affect the inducibility and maintenance of VT. On the other hand, in the model of patient #2, making the subendo- and subepicardial BZs as thin as those of patient #1 yielded sustained VT. In conclusion, the results show that the numerical simulations have an effective predictive capability in discriminating patients at high arrhythmic risk. The extent of the infarct scar and the presence of transmural BZ isthmuses and thin subendo- and subepicardial BZs promote sustained VT.

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“…We now address the solution to the 3D-0D coupled problem in the eventuality that a portion of the cardiac tissue has been affected by myocardial ischemia, that is a reduction of blood supply to the myocardium that may lead to the death of cells in the affected area [58]. In this case, a reduced excitability of the cells and altered ionic currents are observed, as well as inhibited contractility of the tissue [59,60]. Reduced order models for cardiac electrophysiology in the case of ischemic necrosis have been formerly considered in, e.g., [61,62].…”

Section: Pathological Scenariomentioning

confidence: 99%

Efficient approximation of cardiac mechanics through reduced order modeling with deep learning-based operator approximation

Cicci¹,

Fresca²,

Manzoni³

et al. 2022

Preprint

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Reducing the computational time required by high-fidelity, full order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient-specific simulations into clinical practice. While FOMs, such as those based on the finite element method, provide valuable information of the cardiac mechanical function, up to hundreds of thousands degrees of freedom may be needed to obtain accurate numerical results. As a matter of fact, simulating even just a few heartbeats can require hours to days of CPU time even on powerful supercomputers. In addition, cardiac models depend on a set of input parameters that we could let vary in order to explore multiple virtual scenarios. To compute reliable solutions at a greatly reduced computational cost, we rely on a reduced basis method empowered with a new deep-learning based operator approximation, which we refer to as Deep-HyROMnet technique. Our strategy combines a projection-based POD-Galerkin method with deep neural networks for the approximation of (reduced) nonlinear operators, overcoming the typical computational bottleneck associated with standard hyper-reduction techniques. This method is shown to provide reliable approximations to cardiac mechanics problems outperforming classical projection-based ROMs in terms of computational speed-up of orders of magnitude, and enhancing forward uncertainty quantification analysis otherwise unaffordable.

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Electromechanical modeling of human ventricles with ischemic cardiomyopathy: numerical simulations in sinus rhythm and under arrhythmia

Cited by 39 publications

References 68 publications

Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy

Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy

Role of Scar and Border Zone Geometry on the Genesis and Maintenance of Re-Entrant Ventricular Tachycardia in Patients With Previous Myocardial Infarction

Efficient approximation of cardiac mechanics through reduced order modeling with deep learning-based operator approximation

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