Data integration for the numerical simulation of cardiac electrophysiology

Pagani, Stefano; Dedè, Luca; Manzoni, Andrea; Quarteroni, Alfio

doi:10.1111/pace.14198

Cited by 10 publications

(9 citation statements)

References 58 publications

(64 reference statements)

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“…0-D and 3D geometry/echocardiography; deformation elastodynamics Proof of concept in vivo porcine model ( n = 11) 4. Electrophysiology Pagani et al 2021 Italy 62 Reviews issues with integrating imaging, rhythm, and other clinical data into numerical models for patient-specific prediction in cardiac EP. n/a n/a Gillette et al 2021 Austria 63 Generating high-fidelity cardiac digital twins comprises both anatomical (from tomographic data) and functional (inferred from ECG) twinning stages.…”

Section: Resultsmentioning

confidence: 99%

The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field

et al. 2022

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Potential benefits of precision medicine in cardiovascular disease (CVD) include more accurate phenotyping of individual patients with the same condition or presentation, using multiple clinical, imaging, molecular and other variables to guide diagnosis and treatment. An approach to realising this potential is the digital twin concept, whereby a virtual representation of a patient is constructed and receives real-time updates of a range of data variables in order to predict disease and optimise treatment selection for the real-life patient. We explored the term digital twin, its defining concepts, the challenges as an emerging field, and potentially important applications in CVD. A mapping review was undertaken using a systematic search of peer-reviewed literature. Industry-based participants and patent applications were identified through web-based sources. Searches of Compendex, EMBASE, Medline, ProQuest and Scopus databases yielded 88 papers related to cardiovascular conditions (28%, n = 25), non-cardiovascular conditions (41%, n = 36), and general aspects of the health digital twin (31%, n = 27). Fifteen companies with a commercial interest in health digital twin or simulation modelling had products focused on CVD. The patent search identified 18 applications from 11 applicants, of which 73% were companies and 27% were universities. Three applicants had cardiac-related inventions. For CVD, digital twin research within industry and academia is recent, interdisciplinary, and established globally. Overall, the applications were numerical simulation models, although precursor models exist for the real-time cyber-physical system characteristic of a true digital twin. Implementation challenges include ethical constraints and clinical barriers to the adoption of decision tools derived from artificial intelligence systems.

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Section: Resultsmentioning

confidence: 99%

The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field

et al. 2022

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“…In this study, we not only quantitatively analyze the hemodynamic parameters, but also the advantages and disadvantages of the hemodynamic effects, combined with the movement of the left ventricular myocardium, and evaluate the effects of different pacing modes on cardiac function. Ventricle remodeling, disease development, tissue regeneration, patient recovery after surgery and many other cell biological activities are closely associated with ventricle mechanical conditions ( Pagani et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Optimization of Left Ventricle Pace Maker Location Using Echo-Based Fluid-Structure Interaction Models

et al. 2022

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IntroductionCardiac pacing has been an effective treatment in the management of patients with bradyarrhythmia and tachyarrhythmia. Different pacemaker location has different responses, and pacemaker effectiveness to each individual can also be different. A novel image-based ventricle animal modeling approach was proposed to optimize ventricular pacemaker site for better cardiac outcome.MethodOne health female adult pig (weight 42.5 kg) was used to make a pacing animal model with different ventricle pacing locations. Ventricle surface electric signal, blood pressure and echo image were acquired 15 min after the pacemaker was implanted. Echo-based left ventricle fluid-structure interaction models were constructed to perform ventricle function analysis and investigate impact of pacemaker location on cardiac outcome. With the measured electric signal map from the pig associated with the actual pacemaker site, electric potential conduction of myocardium was modeled by material stiffening and softening in our model, with stiffening simulating contraction and softening simulating relaxation. Ventricle model without pacemaker (NP model) and three ventricle models with the following pacemaker locations were simulated: right ventricular apex (RVA model), posterior interventricular septum (PIVS model) and right ventricular outflow tract (RVOT model). Since higher peak flow velocity, flow shear stress (FSS), ventricle stress and strain are linked to better cardiac function, those data were collected for model comparisons.ResultsAt the peak of filling, velocity magnitude, FSS, stress and strain for RVOT and PIVS models were 13%, 45%, 18%, 13% and 5%, 30%, 10%, 5% higher than NP model, respectively. At the peak of ejection, velocity magnitude, FSS, stress and strain for RVOT and PIVS models were 50%, 44%, 54%, 59% and 23%, 36%, 39%, 53% higher than NP model, respectively. RVA model had lower velocity, FSS, stress and strain than NP model. RVOT model had higher peak flow velocity and stress/strain than PIVS model. It indicated RVOT pacemaker site may be the best location.ConclusionThis preliminary study indicated that RVOT model had the best performance among the four models compared. This modeling approach could be used as “virtual surgery” to try various pacemaker locations and avoid risky and dangerous surgical experiments on real patients.

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“…The ionic model used in this paper is the Ten Tusscher 2006 model, 31 which is commonly used in cardiac electrophysiology studies 32,33 . This model consists of 12 subsystem currents

I_{i}

where

i \in Γ

, which is given by the set

Γ = \{Na, K 1, to, Kr, Ks, CaL, NaCa, NaK, pCa, pK, bCa, bNa\} .

These currents, in turn, are dependent on a set of 12 gating variables and six non‐gating variables.…”

Section: Mathematical Modelsmentioning

confidence: 99%

“…The ionic model used in this paper is the Ten Tusscher 2006 model, 31 which is commonly used in cardiac electrophysiology studies. 32,33 This model consists of 12 subsystem currents I i where i Γ, which is given by the set Γ ¼ Na, K1, to, Kr, Ks, CaL, NaCa, NaK, pCa, pK, bCa, bNa f g :…”

Section: Monodomain Equationsmentioning

confidence: 99%

An inner‐outer subcycling algorithm for parallel cardiac electrophysiology simulations

Laudenschlager

Cai

2023

Numer Methods Biomed Eng

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This paper explores cardiac electrophysiological simulations of the monodomain equations and introduces a novel subcycling time integration algorithm to exploit the structure of the ionic model. The aim of this work is to improve upon the efficiency of parallel cardiac monodomain simulations by using our subcycling algorithm in the computation of the ionic model to handle the local sharp changes of the solution. This will reduce the turnaround time for the simulation of basic cardiac electrical function on both idealized and patient-specific geometry. Numerical experiments show that the proposed approach is accurate and also has close to linear parallel scalability on a computer with more than 1000 processor cores. Ultimately, the reduction in simulation time can be beneficial in clinical applications, where multiple simulations are often required to tune a model to match clinical measurements.cardiac electrophysiology, finite element on unstructured meshes, parallel processing, patient-specific cardiac geometry, time integration with subcycling | INTRODUCTIONThe prevalence of cardiovascular disease 1 gives cardiac research a sense of great importance, because advancements in cardiac research could ultimately lead to a reduction in deaths related to cardiovascular disease. One crucial area of cardiac research is cardiac electrophysiology, which investigates the electrical propagation that drives a heartbeat in the human heart. In a clinical setting, this can be studied, for example, with magnetic resonance imaging (MRI) and electrocardiograms (ECG) in order to monitor and detect irregularities in the electrical activity. A different approach is to simulate the electrical activity in the heart using cardiac models. [2][3][4][5] Cardiac electrophysiology simulations at the tissue level can often be modeled by either the bidomain or the monodomain equations, and incorporate a cellular model to deal with the intracellular currents. In this paper, we utilize the monodomain equations. This is a tradeoff between complexity and accuracy, as the bidomain equations more accurately describe cardiac electrical activity, but are also more expensive to solve computationally. 6,7 The monodomain equations are solved via the finite element method (FEM) on a variety of meshes, ranging from simple slabs for benchmarking to meshes generated from actual MRI data. In order to generate patient-specific meshes from MRI data, we developed a semi-automatic mesh generation pipeline, which utilizes user-guided segmentation and automatic model creation from segmentation. This pipeline uses a variety of open source software, and allows for flexibility in mesh generation, including mesh size and quality.

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Data integration for the numerical simulation of cardiac electrophysiology

Cited by 10 publications

References 58 publications

The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field

The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field

Optimization of Left Ventricle Pace Maker Location Using Echo-Based Fluid-Structure Interaction Models

An inner‐outer subcycling algorithm for parallel cardiac electrophysiology simulations

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