Special articleCardiac computational modellingModelización computacional cardiaca
Section snippets
INTRODUCTION
Cardiovascular pathologies have a major social and economic impact in Spain, and in the rest of the world, in terms of morbidity, mortality and cost for the health care system. The diagnostic and therapeutic assessment of patients still depends on empirical studies in which the results are compared statistically between large groups of patients with similar pathology. The choice of the optimum treatment is difficult and treatment efficacy is limited because each patient has a unique disease
Anatomical modelling
There are a large number of available techniques to obtain a patient-specific 3D heart model from in vivo images acquired by MR or computed tomography10 (figure 1, geometry). Among them, it is worth mentioning those based on a-priori knowledge of the heart anatomy such as those based on statistical atlases,11 or more recent ones based on deep learning techniques.
In addition to the 3D geometry, every cardiac computational model has to include other properties: cardiac fiber orientation, or
Atrial arrhythmias
It is well known that atrial arrhythmias can be caused by various mechanisms, including single-circuit reentry, multiple-circuit reentry, rapid local ectopic activity, and rotors. Unravelling the mechanisms underlying atrial arrhythmias can have an important impact in tailoring treatment to individual patients or populations. One of the applications of computational models is in helping to understand the relationship between atrial activation patterns and the characteristics of electrograms
DISCUSSION
The technological progress during the last few years, including advanced high-computing infrastructures, open-source software and open-access medical databases, have brought biophysical models closer to clinical translation. They are currently being used in academia and industry for a better understanding of the physiology and for the optimization of medical devices and therapies. However, modelling-based tools are rarely employed in other clinical decisions such as diagnosis and treatment
FUNDING
This work was partially supported by: Acciones de Dinamización Redes de Excelencia 2016, Plan Estatal de Investigación Científica y Técnica y de Innovación, Ministerio de Economía y Competitividad (DPI2016-81873-REDT) and CompBioMed2, Grant agreement ID: 823712. The authors also thank the support of the European Research Council (ERC-StG 638284) and the Spanish Goverment through the following programmes: Retos I+D (TIN2014-59932-JIN, RTI2018-093416-B-I00, SAF2017-88019-C3-3R,
CONFLICTS OF INTEREST
The authors have nothing to disclose.
References (49)
- et al.
Alya: Multiphysics engineering simulation towards exascale
J Comput Sci.
(2016) - et al.
Modelling the mechanical properties of cardiac muscle
Prog Biophys Mol Biol.
(1998) - et al.
Rev Esp Cardiol.
(2019) - et al.
Reexcitation mechanisms in epicardial tissue: Role of Ito density heterogeneities and INa inactivation kinetics
J Theor Biol.
(2009) - et al.
Prediction of mortality benefit based on periodic repolarisation dynamics in patients undergoing prophylactic implantation of a defibrillator: a prospective, controlled, multicentre cohort study
Lancet.
(2019) - et al.
Computationally guided personalised targeted ablation of persistent atrial fibrillation
Nature biomedical engineering.
(2018) - et al.
Simulation of the undiseased human cardiac ventricular action potential: model formulation and experimental validation
PLoS Comput Biol.
(2011) - et al.
A three-dimensional human atrial model with fiber orientation. Electrograms and arrhythmic activation patterns relationship
PLoS ONE.
(2013) - et al.
Detailed anatomical and electrophysiological models of human atria and torso for the simulation of atrial activation
PLoS ONE.
(2015) - et al.
Three-dimensional cardiac computational modelling: methods, features and applications
Biomed Eng Online.
(2015)
Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps
PLoS ONE.
Fully coupled fluid-electro-mechanical model of the human heart for supercomputers
Int. J. Numer. Meth. Biomed. Engng.
Complex Congenital Heart Disease Associated With Disordered Myocardial Architecture in a Midtrimester Human Fetus
Circ Cardiovasc Imaging.
Computational models in cardiology
Nat Rev Cardiol.
A high-resolution atlas and statistical model of the human heart from multislice CT
IEEE Trans Med Imaging.
A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts
Int J Numer Method Biomed Eng.
Characterization and Modeling of the Peripheral Cardiac Conduction System
IEEE Trans Med Imaging.
Automatic Estimation of Purkinje-Myocardial Junction hot-spots from Noisy Endocardial Samples: A simulation study
Int J Numer Method Biomed Eng.
A quantitative description of membrane current and its application to conduction and excitation in nerve
J Physiol.
Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model
Am J Physiol Circ Physiol.
Mathematical models of the electrical action potential of Purkinje fibre cells
Philos Trans R Soc A Math Phys Eng Sci.
A Comparison of Monodomain and Bidomain Reaction-Diffusion Models for Action Potential. Propagation in the Human Heart
IEEE Trans Biomed Eng.
Ionic bases for electrophysiological distinctions among epicardial, midmyocardial, and endocardial myocytes from the free wall of the canine left ventricle
Circ Res.
Ca content fluctuations or SR refractoriness the key to atrial cardiac alternans?: insights from a human atrial model
Am J Physiol Heart Circ Physiol.
Cited by (0)
- ◊
All the authors, listed in alphabetical order, have contributed equally to this article.