Special article
Cardiac computational modellingModelización computacional cardiaca

https://doi.org/10.1016/j.rec.2020.05.024Get rights and content

Abstract

Cardiovascular diseases currently have a major social and economic impact, constituting one of the leading causes of mortality and morbidity. Personalized computational models of the heart are demonstrating their usefulness both to help understand the mechanisms underlying cardiac disease, and to optimize their treatment and predict the patient's response. Within this framework, the Spanish Research Network for Cardiac Computational Modelling (VHeart-SN) has been launched. The general objective of the VHeart-SN network is the development of an integrated, modular and multiscale multiphysical computational model of the heart. This general objective is addressed through the following specific objectives: a) to integrate the different numerical methods and models taking into account the specificity of patients; b) to assist in advancing knowledge of the mechanisms associated with cardiac and vascular diseases; and c) to support the application of different personalized therapies. This article presents the current state of cardiac computational modelling and different scientific works conducted by the members of the network to gain greater understanding of the characteristics and usefulness of these models.

Resumen

Las enfermedades cardiovasculares tienen en la actualidad un gran impacto social y económico y constituyen una de las principales causas de mortalidad y morbilidad. Los modelos computacionales personalizados del corazón están demostrando ser útiles tanto para ayudar a comprender los mecanismos subyacentes a las patologías cardiacas como para optimizar su tratamiento prediciendo la respuesta del paciente. En este contexto, se ha puesto en marcha la Red Española de Investigación en Modelización Computacional Cardiaca (V-Heart SN). El objetivo general de V-Heart SN es el desarrollo de un modelo computacional multifísico y multiescala integrado del corazón. Este objetivo general se aborda a través de los siguientes objetivos específicos: a) integrar los diferentes modelos numéricos teniendo en cuenta la especificidad de los pacientes; b) ayudar a avanzar en el conocimiento de los mecanismos asociados a las diferentes patologías cardiacas y vasculares; y c) apoyar la aplicación de terapias personalizadas. Este artículo presenta el estado actual de la modelización computacional cardiaca y los diferentes trabajos científicos desarrollados por los miembros de la red para favorecer una mayor comprensión de las características y utilidad de los modelos.

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)

  • A. Ferrer-Albero et al.

    Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps

    PLoS ONE.

    (2017)
  • A. Santiago et al.

    Fully coupled fluid-electro-mechanical model of the human heart for supercomputers

    Int. J. Numer. Meth. Biomed. Engng.

    (2018)
  • P. Garcia-Canadilla et al.

    Complex Congenital Heart Disease Associated With Disordered Myocardial Architecture in a Midtrimester Human Fetus

    Circ Cardiovasc Imaging.

    (2018)
  • S.A. Niederer et al.

    Computational models in cardiology

    Nat Rev Cardiol.

    (2019)
  • C. Hoogendoorn et al.

    A high-resolution atlas and statistical model of the human heart from multislice CT

    IEEE Trans Med Imaging.

    (2013)
  • R. Doste et al.

    A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts

    Int J Numer Method Biomed Eng.

    (2019)
  • R. Sebastian et al.

    Characterization and Modeling of the Peripheral Cardiac Conduction System

    IEEE Trans Med Imaging.

    (2013)
  • F. Barber et al.

    Automatic Estimation of Purkinje-Myocardial Junction hot-spots from Noisy Endocardial Samples: A simulation study

    Int J Numer Method Biomed Eng.

    (2018)
  • A.L. Hodgkin et al.

    A quantitative description of membrane current and its application to conduction and excitation in nerve

    J Physiol.

    (1952)
  • M. Courtemanche et al.

    Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model

    Am J Physiol Circ Physiol.

    (1998)
  • P. Stewart et al.

    Mathematical models of the electrical action potential of Purkinje fibre cells

    Philos Trans R Soc A Math Phys Eng Sci.

    (2009)
  • M. Potse et al.

    A Comparison of Monodomain and Bidomain Reaction-Diffusion Models for Action Potential. Propagation in the Human Heart

    IEEE Trans Biomed Eng.

    (2006)
  • D.W. Liu et al.

    Ionic bases for electrophysiological distinctions among epicardial, midmyocardial, and endocardial myocytes from the free wall of the canine left ventricle

    Circ Res.

    (1993)
  • C.A. Lugo et al.

    Ca content fluctuations or SR refractoriness the key to atrial cardiac alternans?: insights from a human atrial model

    Am J Physiol Heart Circ Physiol.

    (2014)

    • Cited by (0)

    ◊

    All the authors, listed in alphabetical order, have contributed equally to this article.

    View full text
    © 2020 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.