Literature DB >> 21920797

Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: a preliminary clinical validation.

M Sermesant1, R Chabiniok, P Chinchapatnam, T Mansi, F Billet, P Moireau, J M Peyrat, K Wong, J Relan, K Rhode, M Ginks, P Lambiase, H Delingette, M Sorine, C A Rinaldi, D Chapelle, R Razavi, N Ayache.   

Abstract

Cardiac resynchronisation therapy (CRT) is an effective treatment for patients with congestive heart failure and a wide QRS complex. However, up to 30% of patients are non-responders to therapy in terms of exercise capacity or left ventricular reverse remodelling. A number of controversies still remain surrounding patient selection, targeted lead implantation and optimisation of this important treatment. The development of biophysical models to predict the response to CRT represents a potential strategy to address these issues. In this article, we present how the personalisation of an electromechanical model of the myocardium can predict the acute haemodynamic changes associated with CRT. In order to introduce such an approach as a clinical application, we needed to design models that can be individualised from images and electrophysiological mapping of the left ventricle. In this paper the personalisation of the anatomy, the electrophysiology, the kinematics and the mechanics are described. The acute effects of pacing on pressure development were predicted with the in silico model for several pacing conditions on two patients, achieving good agreement with invasive haemodynamic measurements: the mean error on dP/dt(max) is 47.5±35mmHgs(-1), less than 5% error. These promising results demonstrate the potential of physiological models personalised from images and electrophysiology signals to improve patient selection and plan CRT.
Copyright © 2011 Elsevier B.V. All rights reserved.

Entities:  

Mesh:

Year:  2011        PMID: 21920797     DOI: 10.1016/j.media.2011.07.003

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  66 in total

1.  Patient-specific generation of the Purkinje network driven by clinical measurements of a normal propagation.

Authors:  Christian Vergara; Simone Palamara; Domenico Catanzariti; Fabio Nobile; Elena Faggiano; Cesarino Pangrazzi; Maurizio Centonze; Massimiliano Maines; Alfio Quarteroni; Giuseppe Vergara
Journal:  Med Biol Eng Comput       Date:  2014-08-24       Impact factor: 2.602

2.  DTI template-based estimation of cardiac fiber orientations from 3D ultrasound.

Authors:  Xulei Qin; Baowei Fei
Journal:  Med Phys       Date:  2015-06       Impact factor: 4.071

3.  Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology.

Authors:  Jwala Dhamala; Hermenegild J Arevalo; John Sapp; B Milan Horácek; Katherine C Wu; Natalia A Trayanova; Linwei Wang
Journal:  Med Image Anal       Date:  2018-05-17       Impact factor: 8.545

Review 4.  Generating anatomical models of the heart and the aorta from medical images for personalized physiological simulations.

Authors:  J Weese; A Groth; H Nickisch; H Barschdorf; F M Weber; J Velut; M Castro; C Toumoulin; J L Coatrieux; M De Craene; G Piella; C Tobón-Gomez; A F Frangi; D C Barber; I Valverde; Y Shi; C Staicu; A Brown; P Beerbaum; D R Hose
Journal:  Med Biol Eng Comput       Date:  2013-01-30       Impact factor: 2.602

5.  Towards an interactive electromechanical model of the heart.

Authors:  Hugo Talbot; Stéphanie Marchesseau; Christian Duriez; Maxime Sermesant; Stéphane Cotin; Hervé Delingette
Journal:  Interface Focus       Date:  2013-04-06       Impact factor: 3.906

6.  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

Review 7.  Imaging the Propagation of the Electromechanical Wave in Heart Failure Patients with Cardiac Resynchronization Therapy.

Authors:  Ethan Bunting; Litsa Lambrakos; Paul Kemper; William Whang; Hasan Garan; Elisa Konofagou
Journal:  Pacing Clin Electrophysiol       Date:  2016-12-02       Impact factor: 1.976

Review 8.  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

9.  Determining Cardiac Fiber Orientation Using FSL and Registered Ultrasound/DTI volumes.

Authors:  James Dormer; Xulei Qin; Ming Shen; Silun Wang; Xiaodong Zhang; Rong Jiang; Mary B Wagner; Baowei Fei
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2016-04-01

Review 10.  Using physiologically based models for clinical translation: predictive modelling, data interpretation or something in-between?

Authors:  Steven A Niederer; Nic P Smith
Journal:  J Physiol       Date:  2016-07-03       Impact factor: 5.182

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.