Arrhythmogenic cardiomyopathy is characterized by apex-to-base heterogeneity of right ventricular myocardial contractility, stiffness, and mechanical delay: a patient-specific modeling study

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NWO-ZonMw, VIDI grant 016.176.340 Dutch Heart Foundation (2015T082) Introduction Arrhythmogenic Cardiomyopathy (AC) is an inherited cardiac disease, characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. Geno-positive subjects with and without symptoms may suffer from sudden cardiac death. Therefore, early disease detection and risk stratification is important. Right ventricular (RV) longitudinal deformation abnormalities in early stages of disease have been shown to be of prognostic value. We propose an imaging-based patient-specific computer modelling approach for non-invasive quantification of regional ventricular tissue abnormalities. Purpose To non-invasively reveal the individual patient’s myocardial tissue substrates underlying the regional RV deformation abnormalities in AC mutation carriers. Methods In 65 individuals carrying a plakophilin-2 or desmoglein-2 mutation and 20 control subjects, regional longitudinal deformation patterns of the RV free wall (RVfw), interventricular septum (IVS) and left ventricular free wall (LVfw) were obtained using speckle-tracking echocardiography (Figure: left). This cohort was subdivided into 3 consecutive clinical stages i.e. subclinical (concealed, n = 18) with no abnormalities, electrical stage (n = 13) with only electrocardiographic abnormalities, and structural stage (n = 34) with both electrical and structural abnormalities defined by the 2010 Task Force AC criteria. We developed and used a patient-specific parameter estimation protocol based on the multi-scale CircAdapt cardiovascular system model to create virtual AC subjects (Figure: middle). Using the individuals’ RVfw, IVS, and LVfw strain patterns as model input, this protocol automatically estimated regional RV and global IVS and LVfw tissue properties, such as myocardial contractility, stiffness, and activation delay. Results The computational model was able to reproduce the regional deformation patterns as measured clinically. Patient-specific parameter estimation results (Figure: right) revealed that clinical AC disease progression is characterized by a decrease in contractility and an increase in stiffness and mechanical delay of the RV myocardial tissue in the basal segment compared to the apex. The subclinical stage subjects showed tissue properties comparable to the control group, including a small apex-to-base heterogeneity in tissue properties. Conclusion Our patient-specific modelling approach is able to reveal individual myocardial substrates underlying the regional RV deformation abnormalities. Early abnormalities in RV longitudinal strain are most likely caused by increased heterogeneity in local tissue properties, such as an apex-to-base decrease of contractility, increased of myocardial stiffness, and time to peak stress. Abnormalities in tissue properties may be found already in the subclinical stage. In future studies, this artificial intelligence approach will be used to investigate how these abnormalities relate to disease progression and arrhythmogenic risk. Abstract Figure. Characterization of AC Disease Substrate

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Arrhythmogenic cardiomyopathy is characterized by apex-to-base heterogeneity of right ventricular myocardial contractility and stiffness: an imaging-based patient-specific modeling study

European Heart Journal ◽

10.1093/ehjci/ehaa946.0730 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

N Van Osta ◽

A Lyon ◽

F Kirkels ◽

T Koopsen ◽

T.A.M Van Loon ◽

...

Keyword(s):

Disease Progression ◽

Patient Specific ◽

Funding Source ◽

Free Wall ◽

Longitudinal Deformation ◽

Arrhythmogenic Cardiomyopathy ◽

Tissue Properties ◽

Specific Parameter ◽

Structural Stage ◽

Modelling Approach

Abstract Introduction Arrhythmogenic Cardiomyopathy (AC) is an inherited cardiac disease, clinically characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. Geno-positive subjects with and without symptoms may suffer from sudden cardiac death. Therefore, early disease expression and risk stratification is important. It has been shown that right ventricular (RV) longitudinal deformation abnormalities in early stages is related to disease progression. We propose an inverse patient-specific computer modelling approach, combined with clinical imaging data, to non-invasively quantify regional ventricular tissue abnormalities in AC mutation carriers. Purpose To non-invasively reveal the individual myocardial substrate underlying the regional RV deformation abnormalities in AC mutation carriers. Methods In 74 individuals carrying a plakophilin-2 or desmoglein-2 mutation, regional longitudinal deformation patterns of the RV free wall (RVfw), interventricular septum (IVS) and left ventricular free wall (LVfw) were obtained using speckle-tracking echocardiography (Figure: left column). This cohort was subdivided into 3 consecutive clinical stages i.e. subclinical (concealed, n=19) with no abnormalities, electrical stage (n=13) with only electrocardiographic abnormalities, and structural stage (n=42) with both electrical and structural abnormalities defined by the 2010 Task Force AC criteria. We developed and used a patient-specific parameter estimation protocol based on the multi-scale CircAdapt cardiovascular system model to create virtual AC subjects (Figure: middle column). Using the individuals' RV strain patterns as model input, this protocol automatically estimated regional RV tissue properties, such as myocardial contractility and stiffness. Results The computational model was able to reproduce the deformation as clinically measured. Patient-specific parameter estimation results (Figure: right column) revealed that clinical AC disease progression is characterized by an increase of base-to-apex heterogeneity in contractility and stiffness of the RV myocardial tissue, with a decreased contractility and an increased stiffness in the basal segment compared to the apex. Although this heterogeneity was most severe in the structural stage group, it was already present in many of the subjects in the subclinical stage. No clear apex-to-base heterogeneity of mechanical activation delay was found in this cohort. Conclusion Our patient-specific modelling approach showed that early abnormalities in RV longitudinal strain are most likely caused by increased heterogeneity in local tissue properties. Strain abnormalities are predominantly caused by decreased basal tissue contractility and increased basal tissue stiffness. Abnormalities in tissue properties may be found already in the subclinical stage. Future studies will investigate how these abnormalities relate to disease progression and arrhythmogenic risk. Characterization of AC Disease Substrate Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): This work was funded by the Netherlands Organisation for Scientific Research and the Dutch Heart Foundation.

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Parameter subset reduction for patient-specific modelling of arrhythmogenic cardiomyopathy-related mutation carriers in the CircAdapt model

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2019.0347 ◽

2020 ◽

Vol 378 (2173) ◽

pp. 20190347 ◽

Cited By ~ 1

Author(s):

Nick van Osta ◽

Aurore Lyon ◽

Feddo Kirkels ◽

Tijmen Koopsen ◽

Tim van Loon ◽

...

Keyword(s):

Right Ventricular ◽

Computational Models ◽

Clinical Decision Making ◽

Early Stage ◽

Patient Specific ◽

Model Parameters ◽

Arrhythmogenic Cardiomyopathy ◽

Tissue Properties ◽

Myocardial Disease ◽

Deformation Patterns

Arrhythmogenic cardiomyopathy (AC) is an inherited cardiac disease, clinically characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. Patient-specific computational models could help understand the disease progression and may help in clinical decision-making. We propose an inverse modelling approach using the CircAdapt model to estimate patient-specific regional abnormalities in tissue properties in AC subjects. However, the number of parameters ( n = 110) and their complex interactions make personalized parameter estimation challenging. The goal of this study is to develop a framework for parameter reduction and estimation combining Morris screening, quasi-Monte Carlo (qMC) simulations and particle swarm optimization (PSO). This framework identifies the best subset of tissue properties based on clinical measurements allowing patient-specific identification of right ventricular tissue abnormalities. We applied this framework on 15 AC genotype-positive subjects with varying degrees of myocardial disease. Cohort studies have shown that atypical regional right ventricular (RV) deformation patterns reveal an early-stage AC disease. The CircAdapt model of cardiovascular mechanics and haemodynamics has already demonstrated its ability to capture typical deformation patterns of AC subjects. We, therefore, use clinically measured cardiac deformation patterns to estimate model parameters describing myocardial disease substrates underlying these AC-related RV deformation abnormalities. Morris screening reduced the subset to 48 parameters. qMC and PSO further reduced the subset to a final selection of 16 parameters, including regional tissue contractility, passive stiffness, activation delay and wall reference area. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

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Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty

International Journal for Numerical Methods in Biomedical Engineering ◽

10.1002/cnm.2799 ◽

2016 ◽

Vol 33 (3) ◽

pp. e02799 ◽

Cited By ~ 19

Author(s):

Daniele E. Schiavazzi ◽

Alessia Baretta ◽

Giancarlo Pennati ◽

Tain-Yen Hsia ◽

Alison L. Marsden

Keyword(s):

Parameter Estimation ◽

Single Ventricle ◽

Patient Specific ◽

Specific Parameter

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Patient–Specific Parameter Estimation for a Transversely Isotropic Active Strain Model of Left Ventricular Mechanics

Lecture Notes in Computer Science - Statistical Atlases and Computational Models of the Heart - Imaging and Modelling Challenges ◽

10.1007/978-3-319-14678-2_10 ◽

2015 ◽

pp. 93-104 ◽

Cited By ~ 2

Author(s):

Sjur Gjerald ◽

Johan Hake ◽

Simone Pezzuto ◽

Joakim Sundnes ◽

Samuel T. Wall

Keyword(s):

Parameter Estimation ◽

Transversely Isotropic ◽

Left Ventricular ◽

Patient Specific ◽

Active Strain ◽

Ventricular Mechanics ◽

Specific Parameter ◽

Left Ventricular Mechanics ◽

Strain Model

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Patient-specific parameter estimation: coupling a heart model and experimental data

Journal of Theoretical Biology ◽

10.1016/j.jtbi.2021.110791 ◽

2021 ◽

pp. 110791

Author(s):

Andrei A. Domogo ◽

Johnny T. Ottesen

Keyword(s):

Experimental Data ◽

Parameter Estimation ◽

Patient Specific ◽

Heart Model ◽

Specific Parameter

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Uncertainty Quantification of Regional Cardiac Tissue Properties in Arrhythmogenic Cardiomyopathy Using Adaptive Multiple Importance Sampling

Frontiers in Physiology ◽

10.3389/fphys.2021.738926 ◽

2021 ◽

Vol 12 ◽

Author(s):

Nick van Osta ◽

Feddo P. Kirkels ◽

Tim van Loon ◽

Tijmen Koopsen ◽

Aurore Lyon ◽

...

Keyword(s):

Uncertainty Quantification ◽

Importance Sampling ◽

Left Ventricular ◽

Patient Specific ◽

Intraobserver Variability ◽

Posterior Distributions ◽

Arrhythmogenic Cardiomyopathy ◽

True Parameter ◽

Tissue Properties ◽

Multiple Importance Sampling

Introduction: Computational models of the cardiovascular system are widely used to simulate cardiac (dys)function. Personalization of such models for patient-specific simulation of cardiac function remains challenging. Measurement uncertainty affects accuracy of parameter estimations. In this study, we present a methodology for patient-specific estimation and uncertainty quantification of parameters in the closed-loop CircAdapt model of the human heart and circulation using echocardiographic deformation imaging. Based on patient-specific estimated parameters we aim to reveal the mechanical substrate underlying deformation abnormalities in patients with arrhythmogenic cardiomyopathy (AC).Methods: We used adaptive multiple importance sampling to estimate the posterior distribution of regional myocardial tissue properties. This methodology is implemented in the CircAdapt cardiovascular modeling platform and applied to estimate active and passive tissue properties underlying regional deformation patterns, left ventricular volumes, and right ventricular diameter. First, we tested the accuracy of this method and its inter- and intraobserver variability using nine datasets obtained in AC patients. Second, we tested the trueness of the estimation using nine in silico generated virtual patient datasets representative for various stages of AC. Finally, we applied this method to two longitudinal series of echocardiograms of two pathogenic mutation carriers without established myocardial disease at baseline.Results: Tissue characteristics of virtual patients were accurately estimated with a highest density interval containing the true parameter value of 9% (95% CI [0–79]). Variances of estimated posterior distributions in patient data and virtual data were comparable, supporting the reliability of the patient estimations. Estimations were highly reproducible with an overlap in posterior distributions of 89.9% (95% CI [60.1–95.9]). Clinically measured deformation, ejection fraction, and end-diastolic volume were accurately simulated. In presence of worsening of deformation over time, estimated tissue properties also revealed functional deterioration.Conclusion: This method facilitates patient-specific simulation-based estimation of regional ventricular tissue properties from non-invasive imaging data, taking into account both measurement and model uncertainties. Two proof-of-principle case studies suggested that this cardiac digital twin technology enables quantitative monitoring of AC disease progression in early stages of disease.

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