Literature DB >> 28474497

Uncertainty quantification of 2 models of cardiac electromechanics.

Daniel E Hurtado1,2, Sebastián Castro1, Pedro Madrid1.   

Abstract

Computational models of the heart have reached a maturity level that render them useful for in silico studies of arrhythmia and other cardiac diseases. However, the translation to the clinic of cardiac simulations critically depends on demonstrating the accuracy, robustness, and reliability of the underlying computational models under the presence of uncertainties. In this work, we study for the first time the effect of parameter uncertainty on 2 state-of-the-art coupled models of excitation-contraction of cardiac tissue. To this end, we perform forward uncertainty propagation and sensitivity analyses to understand how variability in key maximal conductances affect selected quantities of interest, such as the action potential duration (APD90 ), maximum intracellular calcium concentration, cardiac stretch, and stress. Our results suggest a strong linear relationship between selected maximal conductances and quantities of interest for a variability in parameters up to 25%, which justifies the construction of linear response surfaces that are used to compute the empirical probability density functions of all the quantities of interest under study. For both electromechanical models analyzed, uncertainty in the material parameters associated to the passive mechanical response of cardiac tissue does not affect the duration of action potentials, neither the amplitude of intracellular calcium concentrations. Our results confirm the poor mechanoelectric feedback that classical models of cardiac electromechanics have, even under the presence of parameter uncertainty.
Copyright © 2017 John Wiley & Sons, Ltd.

Entities:  

Keywords:  cardiac electromechanics; parameter sensitivity analysis; uncertainty propagation; uncertainty quantification

Mesh:

Year:  2017        PMID: 28474497     DOI: 10.1002/cnm.2894

Source DB:  PubMed          Journal:  Int J Numer Method Biomed Eng        ISSN: 2040-7939            Impact factor:   2.747


  8 in total

1.  Sensitivity analysis of an electrophysiology model for the left ventricle.

Authors:  Giulio Del Corso; Roberto Verzicco; Francesco Viola
Journal:  J R Soc Interface       Date:  2020-10-28       Impact factor: 4.118

2.  Semi-implicit Non-conforming Finite-Element Schemes for Cardiac Electrophysiology: A Framework for Mesh-Coarsening Heart Simulations.

Authors:  Javiera Jilberto; Daniel E Hurtado
Journal:  Front Physiol       Date:  2018-10-30       Impact factor: 4.566

Review 3.  An audit of uncertainty in multi-scale cardiac electrophysiology models.

Authors:  Richard H Clayton; Yasser Aboelkassem; Chris D Cantwell; Cesare Corrado; Tammo Delhaas; Wouter Huberts; Chon Lok Lei; Haibo Ni; Alexander V Panfilov; Caroline Roney; Rodrigo Weber Dos Santos
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2020-05-25       Impact factor: 4.226

4.  Uncertainty quantification and sensitivity analysis of left ventricular function during the full cardiac cycle.

Authors:  J O Campos; J Sundnes; R W Dos Santos; B M Rocha
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2020-05-25       Impact factor: 4.226

5.  Uncertainty in cardiac myofiber orientation and stiffnesses dominate the variability of left ventricle deformation response.

Authors:  Rocío Rodríguez-Cantano; Joakim Sundnes; Marie E Rognes
Journal:  Int J Numer Method Biomed Eng       Date:  2019-01-21       Impact factor: 2.747

6.  Whole-lung finite-element models for mechanical ventilation and respiratory research applications.

Authors:  Nibaldo Avilés-Rojas; Daniel E Hurtado
Journal:  Front Physiol       Date:  2022-10-04       Impact factor: 4.755

7.  Sensitivity analysis of a strongly-coupled human-based electromechanical cardiac model: Effect of mechanical parameters on physiologically relevant biomarkers.

Authors:  F Levrero-Florencio; F Margara; E Zacur; A Bueno-Orovio; Z J Wang; A Santiago; J Aguado-Sierra; G Houzeaux; V Grau; D Kay; M Vázquez; R Ruiz-Baier; B Rodriguez
Journal:  Comput Methods Appl Mech Eng       Date:  2020-04-01       Impact factor: 6.756

Review 8.  Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences.

Authors:  Mark Alber; Adrian Buganza Tepole; William R Cannon; Suvranu De; Salvador Dura-Bernal; Krishna Garikipati; George Karniadakis; William W Lytton; Paris Perdikaris; Linda Petzold; Ellen Kuhl
Journal:  NPJ Digit Med       Date:  2019-11-25
  8 in total

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