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

2020 ◽  
Vol 378 (2173) ◽  
pp. 20190347 ◽  
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’.

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

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
N Van Osta ◽  
F Kirkels ◽  
A Lyon ◽  
T Koopsen ◽  
TAM Van Loon ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Patient Specific ◽  
Myocardial Tissue ◽  
Free Wall ◽  
Longitudinal Deformation ◽  
Arrhythmogenic Cardiomyopathy ◽  
Tissue Properties ◽  
Specific Parameter ◽  
Deformation Patterns ◽  
Modelling Approach

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

The Uncertainty of Cardiovascular Disease Risk Calculation—What is the Best Risk Model for the Individual?

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Ronald S. LaFleur ◽  
Laura S. Goshko
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Clinical Decision Making ◽  
Risk Model ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Patient Specific ◽  
Model Parameters ◽  
Risk Models ◽  
The Individual

Cardiovascular disease (CVD) continues to be a leading cause of death. Accordingly, risk models attempt to predict an individual's probability of developing the disease. Risk models are incorporated into calculators to determine the risk for a number of clinical conditions, including the ten-year risk of developing CVD. There is significant variability in the published models in terms of how the clinical measurements are converted to risk factors as well as the specific population used to determine b-weights of these risk factors. Adding to model variability is the fact that numbers are an imperfect representation of a person's health status. Acknowledgment of uncertainty must be addressed for reliable clinical decision-making. This paper analyzes 35 published risk calculators and then generalizes them into one “Super Risk formula” to form a common basis for uncertainty calculations to determine the best risk model to use for an individual. Special error arithmetic, the duals method, is used to faithfully propagate error from model parameters, population averages and patient-specific clinical measures to one risk number and its relative uncertainty. A set of sample patients show that the “best model” is specific to the individual and no one model is appropriate for every patient.

scholarly journals Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David A. Hormuth ◽  
Karine A. Al Feghali ◽  
Andrew M. Elliott ◽  
Thomas E. Yankeelov ◽  
Caroline Chung
Keyword(s):  
Mathematical Models ◽  
Computational Models ◽  
Tumor Response ◽  
Treatment Resistance ◽  
High Grade Glioma ◽  
Information Criteria ◽  
Patient Specific ◽  
Model Parameters ◽  
High Grade ◽  
High Grade Gliomas

AbstractHigh-grade gliomas are an aggressive and invasive malignancy which are susceptible to treatment resistance due to heterogeneity in intratumoral properties such as cell proliferation and density and perfusion. Non-invasive imaging approaches can measure these properties, which can then be used to calibrate patient-specific mathematical models of tumor growth and response. We employed multiparametric magnetic resonance imaging (MRI) to identify tumor extent (via contrast-enhanced T1-weighted, and T2-FLAIR) and capture intratumoral heterogeneity in cell density (via diffusion-weighted imaging) to calibrate a family of mathematical models of chemoradiation response in nine patients with unresected or partially resected disease. The calibrated model parameters were used to forecast spatially-mapped individual tumor response at future imaging visits. We then employed the Akaike information criteria to select the most parsimonious member from the family, a novel two-species model describing the enhancing and non-enhancing components of the tumor. Using this model, we achieved low error in predictions of the enhancing volume (median: − 2.5%, interquartile range: 10.0%) and a strong correlation in total cell count (Kendall correlation coefficient 0.79) at 3-months post-treatment. These preliminary results demonstrate the plausibility of using multiparametric MRI data to inform spatially-informative, biologically-based predictive models of tumor response in the setting of clinical high-grade gliomas.

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

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.

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

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.

Childbirth Computational Models: Characteristics and Applications

2020 ◽  
Author(s):  
Sheng Chen ◽  
Michele J. Grimm
Keyword(s):  
Computational Models ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Patient Specific ◽  
Clinical Observations ◽  
Current State ◽  
Specific Assessment ◽  
Maternal Injuries ◽  
Complex Phenomena ◽  
Birth Processes

Abstract The biomechanical process of childbirth is necessary to usher in new lives – but it can also result in trauma. This physically intense process can put both the mother and the child at risk of injuries and complications that have life-long impact. Computational models, as a powerful tool to simulate and explore complex phenomena, have been used to improve our understanding of childbirth processes and related injuries since the 1990s. The goal of this paper is to review and summarize the breadth and current state of the computational models of childbirth in the literature – focusing on those that investigate the mechanical process and effects. We first summarize the state of critical characteristics that have been included in computational models of childbirth (i.e., maternal anatomy, fetal anatomy, cardinal movements, and maternal soft tissue mechanical behavior). We then delve into the findings of the past studies of birth processes and mechanical injuries in an effort to bridge the gap between the theoretical, numerical assessment and the empirical, clinical observations and practices. These findings are from applications of childbirth computational models in four areas: (1) the process of childbirth itself, (2) maternal injuries, (3) fetal injuries, and (4) protective measures employed by clinicians during delivery. Finally, we identify some of the challenges that computational models still face and suggest future directions through which more biofidelic simulations of childbirth might be achieved, with the goal that advancing models may provide more efficient and accurate, patient-specific assessment to support future clinical decision-making.

scholarly journals STUDY OF UNCERTAINTIES ON A 0D MODEL OF THE SYSTEMIC CIRCULATION

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Gilmar Ferreira Da Silva Filho ◽  
Rafael Alves Bonfim De Queiroz ◽  
Luis Paulo Da Silva Barra ◽  
Bernardo Martins Rocha
Keyword(s):  
Sensitivity Analysis ◽  
Computational Models ◽  
Clinical Decision Making ◽  
Systemic Circulation ◽  
Lumped Parameter Model ◽  
Patient Specific ◽  
Accurate Estimation ◽  
Clinical Patient ◽  
Lumped Parameter ◽  
Input Parameters

Cardiovascular system is intensely researched to understand the intricate nature of the heart and blood circulation. Nowadays we have well evolved computational models which are useful in many ways for the understanding and analysis of physiological and pathophysiological conditions of the heart. However, the practical use of these models and their results for clinical decision making in specific patients is not straightforward. In this context, models predictions must be accurate and reliable, which can be assessed by quantification of uncertainties in the predictions and sensitivity analysis of the input parameters. Lumped parameter models for the cardiovascular physiology can provide useful data for clinical patient-specific applications. However, the accurate estimation of all parameters of these models is a difficult task, and therefore the determination of the most sensitive parameters is an important step towards the calibration of these models. We perform uncertainty quantification and sensitivity analysis based on generalised polynomial chaos expansion in a lumped parameter model for the systemic circulation. The objective of this work is to verify the effect of uncertainties from input parameters on the predictions of the models and to identify parameters that contribute significantly to relevant quantities of interest. Numerical experiments are performed and results indicate a set of the most relevant parameters in the context of these models.

A Public Repository of Image-Based Computational Models for Patient-Specific Blood Flow Simulation

2012 ◽  
Author(s):  
Nathan M. Wilson ◽  
Ana K. Ortiz ◽  
Allison B. Johnson ◽  
Frank R. Arko ◽  
Jeffrey A. Feinstein ◽  
...  
Keyword(s):  
Blood Flow ◽  
Computational Models ◽  
Clinical Decision Making ◽  
Flow Simulation ◽  
Clinical Decision ◽  
Arterial Disease ◽  
Patient Specific ◽  
Blood Flow Simulation ◽  
Coronary Arterial Disease ◽  
Public Data

Over the past two decades, significant progress has been made on increasing the realism and fidelity of image-based patient-specific blood flow simulation. A clear example of this progress is the first-of-a-kind multi-center clinical trial under way by Heartflow, Inc. (Redwood City, CA) attempting to utilize blood flow simulation in clinical decision making for coronary arterial disease. While recent applications of patient-specific blood flow simulation are impressive, numerous opportunities still exist for its application in advanced research in disease progression, design of better medical devices, and additional clinical applications for patient-specific interventional planning. Three core challenges face researchers in this space. First, state-of-the art techniques for patient-specific anatomic model construction and hemodynamic simulation require specialized, complex software. In recent years, open-source initiatives such as SimVascular and VMTK have addressed this need. Second, the access to clinical data has traditionally been limited to those with strong ties to research hospitals. Finally, public data for verification and validation of computational models for blood flow has also been limited.

scholarly journals Serial MRI-based right ventricular mechanical wall stress measurements and their association with right ventricle function in patients with repaired Tetralogy of Fallot

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
SCS Minderhoud ◽  
A Hirsch ◽  
F Marin ◽  
I Kardys ◽  
JW Roos-Hesselink ◽  
...  
Keyword(s):  
Right Ventricle ◽  
Right Ventricular ◽  
Computational Models ◽  
Wall Stress ◽  
Patient Specific ◽  
Apical Region ◽  
Free Wall ◽  
Time Points ◽  
Over Time

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Stichting Hartekind en Thorax Foundation Background Optimal timing of pulmonary valve replacement (PVR) in Tetralogy of Fallot (TOF) patients remains challenging. Wall stress is considered to be a possible early marker of right ventricular (RV) dysfunction. With patient-specific computational models, wall stress can be determined regionally and with high accuracy, especially in complex shaped ventricles such as in TOF patients. We aimed to 1) develop patient-specific computational models to assess RV diastolic wall stresses and 2) investigate the association of wall stresses and their change over time with functional parameters in TOF patients. Methods Repaired TOF patients with at least moderate pulmonary regurgitation (PR) and prior to PVR were included. MRI-based patient-specific computational ventricular models were created (figure). The ventricular geometry was created by stacking endo- and epicardial contours traced on short axis SSFP cine images. Pressure in the right ventricle was estimated from echocardiography. Mid-diastolic wall stress in the RV free wall was analysed globally and regionally (basal, mid, apical, anterior, lateral and posterior) at two time points. RV ejection fraction (RVEF), NT-proBNP and exercise tests (% maximum predicted workload) were used as outcomes for RV function. Associations between wall stresses and outcomes were investigated using linear mixed models adjusted for follow-up duration. Results Five males and five females were included with an age at baseline of 24 (IQR 16-28) years and RV end-diastolic volume of 140 (IQR 127-144) ml/m2. The period between the two time points was 7.0 (IQR 5.8-7.3) years. Global wall stress of the RV free wall combining both time points was 5.8 kPa (IQR 5.2-7.2). There was no statistical difference between baseline and follow-up global wall stress. The mean wall stresses in the mid region was 1.69 kPa (p < 0.01) higher than in the basal region and was 1.05 kPa (p = 0.03) higher than in the apical region cross-sectionally. The wall stress also increased more in the mid region compared to basal and apical region, corrected for duration of follow-up. Patients with more severe PR at baseline demonstrated a higher increase of global wall stress over time (p = 0.02), especially in lateral free wall. Higher global free wall stresses were cross-sectionally independently associated with lower RVEF, adjusted for LVEF and RVEDV (β=-1.29 % RVEF per kPa increase in wall stress, p = 0.01). This association was most prominent in the anterior, basal and mid part. No statistically significant association was found between wall stress, NT-proBNP, and exercise capacity. Conclusions This study generated a novel MRI-based method to calculate wall stress in geometrically complex ventricles. Wall stress associated negatively with RVEF in patients with TOF and PR. This promising tool for RV wall stress analysis can be used in future larger studies to validate these preliminary findings and to assess the predictive value of wall stress in TOF. Abstract Figure.

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