Towards Personalized Cardiology: Multi-Scale Modeling of the Failing Heart

Kayvanpour, Elham; Mansi, Tommaso; Sedaghat‐Hamedani, Farbod; Amr, Ali; Neumann, Dominik; Georgescu, Bogdan; Seegerer, Philipp; Kamen, Ali; Haas, Jan; Frese, Karen; Irawati, Maria; Wirsz, Emil; King, Vanessa M.; Buss, Sebastian J.; Mereles, Derliz; Zitron, Edgar; Keller, Andreas; Katus, Hugo A.; Comanicìu, Dorin; Meder, Benjamin

doi:10.1371/journal.pone.0134869

Cited by 74 publications

(70 citation statements)

References 52 publications

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“…The feasibility of using the presented cardiac model to capture cardiac systolic function in a clinical setting, in its strengths and limitations, has been previously reported [2]. In the present study, we aimed to examine how systolic and diastolic biomechanical parameters derived from the model, after completion of the fitting and personalization process, correspond to invasive and non-invasive clinical parameters of diastolic function.…”

Section: Resultsmentioning

confidence: 99%

Personalized Computer Simulation of Diastolic Function in Heart Failure

Amr

Kayvanpour

Sedaghat‐Hamedani

et al. 2016

Genomics, Proteomics &Amp; Bioinformatics

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The search for a parameter representing left ventricular relaxation from non-invasive and invasive diagnostic tools has been extensive, since heart failure (HF) with preserved ejection fraction (HF-pEF) is a global health problem. We explore here the feasibility using patient-specific cardiac computer modeling to capture diastolic parameters in patients suffering from different degrees of systolic HF. Fifty eight patients with idiopathic dilated cardiomyopathy have undergone thorough clinical evaluation, including cardiac magnetic resonance imaging (MRI), heart catheterization, echocardiography, and cardiac biomarker assessment. A previously-introduced framework for creating multi-scale patient-specific cardiac models has been applied on all these patients. Novel parameters, such as global stiffness factor and maximum left ventricular active stress, representing cardiac active and passive tissue properties have been computed for all patients. Invasive pressure measurements from heart catheterization were then used to evaluate ventricular relaxation using the time constant of isovolumic relaxation Tau (τ). Parameters from heart catheterization and the multi-scale model have been evaluated and compared to patient clinical presentation. The model parameter global stiffness factor, representing diastolic passive tissue properties, is correlated significantly across the patient population with τ. This study shows that multi-modal cardiac models can successfully capture diastolic (dys) function, a prerequisite for future clinical trials on HF-pEF.

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Section: Resultsmentioning

confidence: 99%

Personalized Computer Simulation of Diastolic Function in Heart Failure

Amr

Kayvanpour

Sedaghat‐Hamedani

et al. 2016

Genomics, Proteomics &Amp; Bioinformatics

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“…Multi-scale finite element (FE) models of cardiac mechanics are being investigated as novel tools for analyzing medical imaging data and assisting personalized diagnostics in cardiac resynchronization therapy and disease monitoring [1–4]. Despite recent advances, personalizing FE simulations of heart mechanics to a specific clinical case still constitutes an open research problem.…”

Section: Introductionmentioning

confidence: 99%

Model order reduction for left ventricular mechanics via congruency training

Achille

Parikh

Khamzin

et al. 2019

Preprint

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Computational models of the cardiovascular system and heart function are currently being investigated as analytic tools to assist medical practice and clinical trials. Recent technological advances allow for finite element models of heart ventricles and atria to be customized to medical images and to assimilate electrical and hemodynamic measurements.Optimizing model parameters to physiological data is, however, challenging due to the computational complexity of finite element models. Metaheuristic algorithms and other optimization strategies typically require sampling hundreds of points in the model parameter space before converging to optimal solutions. Similarly, resolving uncertainty of model outputs to input assumptions is difficult for finite element models due to their computational cost. In this paper, we present a novel, multifidelity strategy for model order reduction of 3-D finite element models of ventricular mechanics. Our approach is centered around well established findings on the similarity between contraction of an isolated muscle and the whole ventricle. Specifically, we demonstrate that simple linear transformations between sarcomere strain (tension) and ventricular volume (pressure) are sufficient to reproduce global pressure-volume outputs of 3-D finite element models even by a reduced model with just a single myocyte unit. We further develop a procedure for congruency training of a surrogate low-order model from multi-scale finite elements, and we construct an example of parameter optimization based on medical images. We discuss how the presented approach might be employed to process large datasets of medical images as well as databases of echocardiographic reports, paving the way towards application of heart mechanics models in the clinical practice.Multi-scale finite element (FE) models of cardiac mechanics are being investigated as novel tools for analyzing medical 2 imaging data and assisting personalized diagnostics in cardiac resynchronization therapy and disease monitoring [1][2][3][4]. 3 Despite recent advances, personalizing FE simulations of heart mechanics to a specific clinical case still constitutes an 4 open research problem. While most state-of-the-art models can capture the anatomical details of atria and ventricles from 5 3-D medical images (e.g., from CT or from the more costly MRI [5, 6]), significant computational resources are necessary 6 to inversely estimate, when at all possible, the many unknown model parameters. Applying FE models in large scale 7analyses of clinical trials is, therefore, currently intractable. At the same time, it is widely accepted that a more 8 mechanistic understanding of trial results could be greatly beneficial. For example, direct applications of models could 9 1/26 provide illustrations of certain biophysical correlates of trial trends, thus aiding the interpretation of complex drug effects, 10 such as intropes on cardiac function in heart failure patients [7][8][9]. 11Clinical evaluations often rely on echocardiography as their main source ...

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“…Personalised cardiac models are of increasing interest for clinical applications. () To that end, parameter values of a cardiac model are estimated to get a personalised simulation that reproduces the available measurements for a clinical case. Then the personalised simulations can be used for advanced analysis of pathologies.…”

Section: Introductionmentioning

confidence: 99%

Population‐based priors in cardiac model personalisation for consistent parameter estimation in heterogeneous databases

Molléro

Pennec

Delingette

et al. 2018

Numer Methods Biomed Eng

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Personalised cardiac models are a virtual representation of the patient heart, with parameter values for which the simulation fits the available clinical measurements. Models usually have a large number of parameters while the available data for a given patient are typically limited to a small set of measurements; thus, the parameters cannot be estimated uniquely. This is a practical obstacle for clinical applications, where accurate parameter values can be important.Here, we explore an original approach based on an algorithm called Iteratively Updated Priors (IUP), in which we perform successive personalisations of a full database through maximum a posteriori (MAP) estimation, where the prior probability at an iteration is set from the distribution of personalised parameters in the database at the previous iteration. At the convergence of the algorithm, estimated parameters of the population lie on a linear subspace of reduced (and possibly sufficient) dimension in which for each case of the database, there is a (possibly unique) parameter value for which the simulation fits the measurements. We first show how this property can help the modeller select a relevant parameter subspace for personalisation. In addition, since the resulting priors in this subspace represent the population statistics in this subspace, they can be used to perform consistent parameter estimation for cases where measurements are possibly different or missing in the database, which we illustrate with the personalisation of a heterogeneous database of 811 cases. KEYWORDScardiac electromechanical modelling, parameter estimation, parameter selection, personalised modelling INTRODUCTIONPersonalised cardiac models are of increasing interest for clinical applications. [1][2][3] To that end, parameter values of a cardiac model are estimated to get a personalised simulation that reproduces the available measurements for a clinical case. Then the personalised simulations can be used for advanced analysis of pathologies. In particular, recent works have been successful in predicting haemodynamic changes in cardiac resynchronization therapy, 4 ventricular tachycardia inducibility and dynamics, 5 and in detecting and localising infarcts 6 using 3D personalised models. Int J Numer Meth Biomed Engng. 2019;35:e3158. wileyonlinelibrary.com/journal/cnm

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Towards Personalized Cardiology: Multi-Scale Modeling of the Failing Heart

Cited by 74 publications

References 52 publications

Personalized Computer Simulation of Diastolic Function in Heart Failure

Personalized Computer Simulation of Diastolic Function in Heart Failure

Model order reduction for left ventricular mechanics via congruency training

Population‐based priors in cardiac model personalisation for consistent parameter estimation in heterogeneous databases

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