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Koopsen T, van Osta N, van Loon T, Meiburg R, Huberts W, Beela AS, Kirkels FP, van Klarenbosch BR, Teske AJ, Cramer MJ, Bijvoet GP, van Stipdonk A, Vernooy K, Delhaas T, Lumens J. Parameter subset reduction for imaging-based digital twin generation of patients with left ventricular mechanical discoordination. Biomed Eng Online 2024; 23:46. [PMID: 38741182 DOI: 10.1186/s12938-024-01232-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 04/02/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Integration of a patient's non-invasive imaging data in a digital twin (DT) of the heart can provide valuable insight into the myocardial disease substrates underlying left ventricular (LV) mechanical discoordination. However, when generating a DT, model parameters should be identifiable to obtain robust parameter estimations. In this study, we used the CircAdapt model of the human heart and circulation to find a subset of parameters which were identifiable from LV cavity volume and regional strain measurements of patients with different substrates of left bundle branch block (LBBB) and myocardial infarction (MI). To this end, we included seven patients with heart failure with reduced ejection fraction (HFrEF) and LBBB (study ID: 2018-0863, registration date: 2019-10-07), of which four were non-ischemic (LBBB-only) and three had previous MI (LBBB-MI), and six narrow QRS patients with MI (MI-only) (study ID: NL45241.041.13, registration date: 2013-11-12). Morris screening method (MSM) was applied first to find parameters which were important for LV volume, regional strain, and strain rate indices. Second, this parameter subset was iteratively reduced based on parameter identifiability and reproducibility. Parameter identifiability was based on the diaphony calculated from quasi-Monte Carlo simulations and reproducibility was based on the intraclass correlation coefficient ( ICC ) obtained from repeated parameter estimation using dynamic multi-swarm particle swarm optimization. Goodness-of-fit was defined as the mean squared error (χ 2 ) of LV myocardial strain, strain rate, and cavity volume. RESULTS A subset of 270 parameters remained after MSM which produced high-quality DTs of all patients (χ 2 < 1.6), but minimum parameter reproducibility was poor (ICC min = 0.01). Iterative reduction yielded a reproducible (ICC min = 0.83) subset of 75 parameters, including cardiac output, global LV activation duration, regional mechanical activation delay, and regional LV myocardial constitutive properties. This reduced subset produced patient-resembling DTs (χ 2 < 2.2), while septal-to-lateral wall workload imbalance was higher for the LBBB-only DTs than for the MI-only DTs (p < 0.05). CONCLUSIONS By applying sensitivity and identifiability analysis, we successfully determined a parameter subset of the CircAdapt model which can be used to generate imaging-based DTs of patients with LV mechanical discoordination. Parameters were reproducibly estimated using particle swarm optimization, and derived LV myocardial work distribution was representative for the patient's underlying disease substrate. This DT technology enables patient-specific substrate characterization and can potentially be used to support clinical decision making.
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Affiliation(s)
- Tijmen Koopsen
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands.
| | - Nick van Osta
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Tim van Loon
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Roel Meiburg
- Group SIMBIOTX, Institut de Recherche en Informatique et en Automatique (INRIA), Paris, France
| | - Wouter Huberts
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Ahmed S Beela
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- Department of Cardiology, Suez Canal University, Ismailia, Egypt
| | - Feddo P Kirkels
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Bas R van Klarenbosch
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Arco J Teske
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Maarten J Cramer
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Geertruida P Bijvoet
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- Department of Cardiology, Maastricht University Medical Center (MUMC), Maastricht, The Netherlands
| | - Antonius van Stipdonk
- Department of Cardiology, Maastricht University Medical Center (MUMC), Maastricht, The Netherlands
| | - Kevin Vernooy
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- Department of Cardiology, Maastricht University Medical Center (MUMC), Maastricht, The Netherlands
- Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands.
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Koopsen T, Gerrits W, van Osta N, van Loon T, Wouters P, Prinzen FW, Vernooy K, Delhaas T, Teske AJ, Meine M, Cramer MJ, Lumens J. Virtual pacing of a patient's digital twin to predict left ventricular reverse remodelling after cardiac resynchronization therapy. Europace 2023; 26:euae009. [PMID: 38288616 PMCID: PMC10825733 DOI: 10.1093/europace/euae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 01/09/2024] [Indexed: 02/01/2024] Open
Abstract
AIMS Identifying heart failure (HF) patients who will benefit from cardiac resynchronization therapy (CRT) remains challenging. We evaluated whether virtual pacing in a digital twin (DT) of the patient's heart could be used to predict the degree of left ventricular (LV) reverse remodelling post-CRT. METHODS AND RESULTS Forty-five HF patients with wide QRS complex (≥130 ms) and reduced LV ejection fraction (≤35%) receiving CRT were retrospectively enrolled. Echocardiography was performed before (baseline) and 6 months after CRT implantation to obtain LV volumes and 18-segment longitudinal strain. A previously developed algorithm was used to generate 45 DTs by personalizing the CircAdapt model to each patient's baseline measurements. From each DT, baseline septal-to-lateral myocardial work difference (MWLW-S,DT) and maximum rate of LV systolic pressure rise (dP/dtmax,DT) were derived. Biventricular pacing was then simulated using patient-specific atrioventricular delay and lead location. Virtual pacing-induced changes ΔMWLW-S,DT and ΔdP/dtmax,DT were correlated with real-world LV end-systolic volume change at 6-month follow-up (ΔLVESV). The DT's baseline MWLW-S,DT and virtual pacing-induced ΔMWLW-S,DT were both significantly associated with the real patient's reverse remodelling ΔLVESV (r = -0.60, P < 0.001 and r = 0.62, P < 0.001, respectively), while correlation between ΔdP/dtmax,DT and ΔLVESV was considerably weaker (r = -0.34, P = 0.02). CONCLUSION Our results suggest that the reduction of septal-to-lateral work imbalance by virtual pacing in the DT can predict real-world post-CRT LV reverse remodelling. This DT approach could prove to be an additional tool in selecting HF patients for CRT and has the potential to provide valuable insights in optimization of CRT delivery.
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Affiliation(s)
- Tijmen Koopsen
- Department of Biomedical Engineering, CARIM Cardiovascular Research Institute Maastricht, Maastricht University, Universiteitssingel 40, 6200 MD, The Netherlands
| | - Willem Gerrits
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Nick van Osta
- Department of Biomedical Engineering, CARIM Cardiovascular Research Institute Maastricht, Maastricht University, Universiteitssingel 40, 6200 MD, The Netherlands
| | - Tim van Loon
- Department of Biomedical Engineering, CARIM Cardiovascular Research Institute Maastricht, Maastricht University, Universiteitssingel 40, 6200 MD, The Netherlands
| | - Philippe Wouters
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Frits W Prinzen
- Department of Physiology, CARIM Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Kevin Vernooy
- Department of Cardiology, CARIM Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
- Department of Cardiology, Maastricht University Medical Center (MUMC), Maastricht, The Netherlands
- Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, CARIM Cardiovascular Research Institute Maastricht, Maastricht University, Universiteitssingel 40, 6200 MD, The Netherlands
| | - Arco J Teske
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Mathias Meine
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Maarten J Cramer
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, CARIM Cardiovascular Research Institute Maastricht, Maastricht University, Universiteitssingel 40, 6200 MD, The Netherlands
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Kirkels FP, Rootwelt-Norberg C, Bosman LP, Aabel EW, Muller SA, Castrini AI, Taha K, van Osta N, Lie ØH, Asselbergs FW, Lumens J, te Riele ASJM, Hasselberg NE, Cramer MJ, Haugaa KH, Teske AJ. The added value of abnormal regional myocardial function for risk prediction in arrhythmogenic right ventricular cardiomyopathy. Eur Heart J Cardiovasc Imaging 2023; 24:1710-1718. [PMID: 37474315 PMCID: PMC10667035 DOI: 10.1093/ehjci/jead174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/13/2023] [Accepted: 07/13/2023] [Indexed: 07/22/2023] Open
Abstract
AIMS A risk calculator for individualized prediction of first-time sustained ventricular arrhythmia (VA) in arrhythmogenic right ventricular cardiomyopathy (ARVC) patients has recently been developed and validated (www.ARVCrisk.com). This study aimed to investigate whether regional functional abnormalities, measured by echocardiographic deformation imaging, can provide additional prognostic value. METHODS AND RESULTS From two referral centres, 150 consecutive patients with a definite ARVC diagnosis, no prior sustained VA, and an echocardiogram suitable for deformation analysis were included (aged 41 ± 17 years, 50% female). During a median follow-up of 6.3 (interquartile range 3.1-9.8) years, 37 (25%) experienced a first-time sustained VA. All tested left and right ventricular (LV and RV) deformation parameters were univariate predictors for first-time VA. While LV function did not add predictive value in multivariate analysis, two RV deformation parameters did; RV free wall longitudinal strain and regional RV deformation patterns remained independent predictors after adjusting for the calculator-predicted risk [hazard ratio 1.07 (95% CI 1.02-1.11); P = 0.004 and 4.45 (95% CI 1.07-18.57); P = 0.040, respectively] and improved its discriminative value (from C-statistic 0.78 to 0.82 in both; Akaike information criterion change > 2). Importantly, all patients who experienced VA within 5 years from the echocardiographic assessment had abnormal regional RV deformation patterns at baseline. CONCLUSIONS This study showed that regional functional abnormalities measured by echocardiographic deformation imaging can further refine personalized arrhythmic risk prediction when added to the ARVC risk calculator. The excellent negative predictive value of normal RV deformation could support clinicians considering the timing of implantable cardioverter defibrillator implantation in patients with intermediate arrhythmic risk.
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Affiliation(s)
- Feddo P Kirkels
- Division of Heart and Lungs, Department of Cardiology, University Medical Centre Utrecht, Heidelberglaan 100, Utrecht 3582 CX, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- ProCardio Centre for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Christine Rootwelt-Norberg
- ProCardio Centre for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Laurens P Bosman
- Division of Heart and Lungs, Department of Cardiology, University Medical Centre Utrecht, Heidelberglaan 100, Utrecht 3582 CX, The Netherlands
| | - Eivind W Aabel
- ProCardio Centre for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Steven A Muller
- Division of Heart and Lungs, Department of Cardiology, University Medical Centre Utrecht, Heidelberglaan 100, Utrecht 3582 CX, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Anna I Castrini
- ProCardio Centre for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Karim Taha
- Division of Heart and Lungs, Department of Cardiology, University Medical Centre Utrecht, Heidelberglaan 100, Utrecht 3582 CX, The Netherlands
| | - Nick van Osta
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Øyvind H Lie
- ProCardio Centre for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Joost Lumens
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Anneline S J M te Riele
- Division of Heart and Lungs, Department of Cardiology, University Medical Centre Utrecht, Heidelberglaan 100, Utrecht 3582 CX, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Nina E Hasselberg
- ProCardio Centre for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Maarten J Cramer
- Division of Heart and Lungs, Department of Cardiology, University Medical Centre Utrecht, Heidelberglaan 100, Utrecht 3582 CX, The Netherlands
| | - Kristina H Haugaa
- ProCardio Centre for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Arco J Teske
- Division of Heart and Lungs, Department of Cardiology, University Medical Centre Utrecht, Heidelberglaan 100, Utrecht 3582 CX, The Netherlands
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Kirkels FP, van Osta N, Rootwelt-Norberg C, Chivulescu M, van Loon T, Aabel EW, Castrini AI, Lie ØH, Asselbergs FW, Delhaas T, Cramer MJ, Teske AJ, Haugaa KH, Lumens J. Monitoring of Myocardial Involvement in Early Arrhythmogenic Right Ventricular Cardiomyopathy Across the Age Spectrum. J Am Coll Cardiol 2023; 82:785-797. [PMID: 37612010 DOI: 10.1016/j.jacc.2023.05.065] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 05/19/2023] [Accepted: 05/31/2023] [Indexed: 08/25/2023]
Abstract
BACKGROUND Arrhythmogenic right ventricular cardiomyopathy (ARVC) is characterized by fibrofatty replacement of primarily the right ventricular myocardium, a substrate for life-threatening ventricular arrhythmias (VAs). Repeated cardiac imaging of at-risk relatives is important for early disease detection. However, it is not known whether screening should be age-tailored. OBJECTIVES The goal of this study was to assess the need for age-tailoring of follow-up protocols in early ARVC by evaluating myocardial disease progression in different age groups. METHODS We divided patients with early-stage ARVC and genotype-positive relatives without overt structural disease and VA at first evaluation into 3 groups: age <30 years, 30 to 50 years, and ≥50 years. Longitudinal biventricular deformation characteristics were used to monitor disease progression. To link deformation abnormalities to underlying myocardial disease substrates, Digital Twins were created using an imaging-based computational modeling framework. RESULTS We included 313 echocardiographic assessments from 82 subjects (57% female, age 39 ± 17 years, 10% probands) during 6.7 ± 3.3 years of follow-up. Left ventricular global longitudinal strain slightly deteriorated similarly in all age groups (0.1%-point per year [95% CI: 0.05-0.15]). Disease progression in all age groups was more pronounced in the right ventricular lateral wall, expressed by worsening in longitudinal strain (0.6%-point per year [95% CI: 0.46-0.70]) and local differences in myocardial contractility, compliance, and activation delay in the Digital Twin. Six patients experienced VA during follow-up. CONCLUSIONS Disease progression was similar in all age groups, and sustained VA also occurred in patients aged >50 years without overt ARVC phenotype at first evaluation. Unlike recommended by current guidelines, our study suggests that follow-up of ARVC patients and relatives should not stop at older age.
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Affiliation(s)
- Feddo P Kirkels
- Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands; Netherlands Heart Institute, Utrecht, the Netherlands; Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands; ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway.
| | - Nick van Osta
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
| | - Christine Rootwelt-Norberg
- ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Monica Chivulescu
- ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Tim van Loon
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
| | - Eivind W Aabel
- ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Anna I Castrini
- ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Øyvind H Lie
- ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Folkert W Asselbergs
- Amsterdam University Medical Centers, Department of Cardiology, University of Amsterdam, Amsterdam, the Netherlands; Health Data Research UK and Institute of Health Informatics, University College London, London, United Kingdom
| | - Tammo Delhaas
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
| | - Maarten J Cramer
- Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Arco J Teske
- Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Kristina H Haugaa
- ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. https://twitter.com/KristinaHaugaa
| | - Joost Lumens
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands.
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van Osta N, Kirkels FP, van Loon T, Koopsen T, Lyon A, Meiburg R, Huberts W, Cramer MJ, Delhaas T, Haugaa KH, Teske AJ, Lumens J. Uncertainty Quantification of Regional Cardiac Tissue Properties in Arrhythmogenic Cardiomyopathy Using Adaptive Multiple Importance Sampling. Front Physiol 2021; 12:738926. [PMID: 34658923 PMCID: PMC8514656 DOI: 10.3389/fphys.2021.738926] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 08/31/2021] [Indexed: 11/13/2022] Open
Abstract
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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Affiliation(s)
- Nick van Osta
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Feddo P Kirkels
- Division Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tim van Loon
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Tijmen Koopsen
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Aurore Lyon
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Roel Meiburg
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Wouter Huberts
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Maarten J Cramer
- Division Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Kristina H Haugaa
- Department of Cardiology, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Arco J Teske
- Division Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
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van Osta N, Kirkels F, Lyon A, Koopsen T, van Loon T, Cramer MJ, Teske AJ, Delhaas T, Lumens J. Electromechanical substrate characterization in arrhythmogenic cardiomyopathy using imaging-based patient-specific computer simulations. Europace 2021; 23:i153-i160. [PMID: 33751081 PMCID: PMC7943356 DOI: 10.1093/europace/euaa407] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 12/14/2020] [Indexed: 01/11/2023] Open
Abstract
AIMS Arrhythmogenic cardiomyopathy (AC) is an inherited cardiac disease, characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. The aim of this study is to use computer simulations to non-invasively estimate the individual patient's myocardial tissue substrates underlying regional right ventricular (RV) deformation abnormalities in a cohort of AC mutation carriers. METHODS AND RESULTS In 68 AC mutation carriers 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. We developed and used a patient-specific parameter estimation protocol based on the multi-scale CircAdapt cardiovascular system model to create virtual AC subjects. Using the individual's deformation data as model input, this protocol automatically estimated regional RVfw and global IVS and LVfw tissue properties. The computational model was able to reproduce clinically measured regional deformation patterns for all subjects, with highly reproducible parameter estimations. Simulations revealed that regional RVfw heterogeneity of both contractile function and compliance were increased in subjects with clinically advanced disease compared to mutation carriers without clinically established disease (17 ± 13% vs. 8 ± 4%, P = 0.01 and 18 ± 11% vs. 10 ± 7%, P < 0.01, respectively). No significant difference in activation delay was found. CONCLUSION Regional RV deformation abnormalities in AC mutation carriers were related to reduced regional contractile function and tissue compliance. In clinically advanced disease stages, a characteristic apex-to-base heterogeneity of tissue abnormalities was present in the majority of the subjects, with most pronounced disease in the basal region of the RVfw.
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Affiliation(s)
- Nick van Osta
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Universiteitssingel 50 (UNS50), 6229 ER Maastricht, The Netherlands
| | - Feddo Kirkels
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Universiteitssingel 50 (UNS50), 6229 ER Maastricht, The Netherlands
- Division Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Aurore Lyon
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Universiteitssingel 50 (UNS50), 6229 ER Maastricht, The Netherlands
| | - Tijmen Koopsen
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Universiteitssingel 50 (UNS50), 6229 ER Maastricht, The Netherlands
| | - Tim van Loon
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Universiteitssingel 50 (UNS50), 6229 ER Maastricht, The Netherlands
| | - Maarten-Jan Cramer
- Division Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Arco J Teske
- Division Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Universiteitssingel 50 (UNS50), 6229 ER Maastricht, The Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Universiteitssingel 50 (UNS50), 6229 ER Maastricht, The Netherlands
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van Osta N, Lyon A, Kirkels F, Koopsen T, van Loon T, Cramer MJ, Teske AJ, Delhaas T, Huberts W, Lumens J. Parameter subset reduction for patient-specific modelling of arrhythmogenic cardiomyopathy-related mutation carriers in the CircAdapt model. Philos Trans A Math Phys Eng Sci 2020; 378:20190347. [PMID: 32448061 PMCID: PMC7287326 DOI: 10.1098/rsta.2019.0347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
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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Affiliation(s)
- Nick van Osta
- Department of Biomedical Engineering, Maastricht University CARIM School for Cardiovascular Diseases, Maastricht, Limburg, The Netherlands
- e-mail:
| | - Aurore Lyon
- Department of Biomedical Engineering, Maastricht University CARIM School for Cardiovascular Diseases, Maastricht, Limburg, The Netherlands
| | - Feddo Kirkels
- Department of Cardiology, University Medical Center Utrecht, Utrecht, Utrecht, The Netherlands
| | - Tijmen Koopsen
- Department of Biomedical Engineering, Maastricht University CARIM School for Cardiovascular Diseases, Maastricht, Limburg, The Netherlands
| | - Tim van Loon
- Department of Biomedical Engineering, Maastricht University CARIM School for Cardiovascular Diseases, Maastricht, Limburg, The Netherlands
| | - Maarten J. Cramer
- Department of Cardiology, University Medical Center Utrecht, Utrecht, Utrecht, The Netherlands
| | - Arco J. Teske
- Department of Cardiology, University Medical Center Utrecht, Utrecht, Utrecht, The Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, Maastricht University CARIM School for Cardiovascular Diseases, Maastricht, Limburg, The Netherlands
| | - Wouter Huberts
- Department of Biomedical Engineering, Maastricht University CARIM School for Cardiovascular Diseases, Maastricht, Limburg, The Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, Maastricht University CARIM School for Cardiovascular Diseases, Maastricht, Limburg, The Netherlands
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