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Sel K, Osman D, Zare F, Masoumi Shahrbabak S, Brattain L, Hahn JO, Inan OT, Mukkamala R, Palmer J, Paydarfar D, Pettigrew RI, Quyyumi AA, Telfer B, Jafari R. Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact. J Am Heart Assoc 2024:e031981. [PMID: 39087582 DOI: 10.1161/jaha.123.031981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.
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Affiliation(s)
- Kaan Sel
- Laboratory for Information & Decision Systems (LIDS) Massachusetts Institute of Technology Cambridge MA USA
| | - Deen Osman
- Department of Electrical and Computer Engineering Texas A&M University College Station TX USA
| | - Fatemeh Zare
- Department of Electrical and Computer Engineering Texas A&M University College Station TX USA
| | | | - Laura Brattain
- Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA
| | - Jin-Oh Hahn
- Department of Mechanical Engineering University of Maryland College Park MD USA
| | - Omer T Inan
- School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA
| | - Ramakrishna Mukkamala
- Department of Bioengineering and Anesthesiology and Perioperative Medicine University of Pittsburgh Pittsburgh PA USA
| | - Jeffrey Palmer
- Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA
| | - David Paydarfar
- Department of Neurology The University of Texas at Austin Dell Medical School Austin TX USA
| | | | - Arshed A Quyyumi
- Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine Emory University School of Medicine Atlanta GA USA
| | - Brian Telfer
- Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA
| | - Roozbeh Jafari
- Laboratory for Information & Decision Systems (LIDS) Massachusetts Institute of Technology Cambridge MA USA
- Department of Electrical and Computer Engineering Texas A&M University College Station TX USA
- Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA
- School of Engineering Medicine Texas A&M University Houston TX USA
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2
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Colebank MJ, Oomen PA, Witzenburg CM, Grosberg A, Beard DA, Husmeier D, Olufsen MS, Chesler NC. Guidelines for mechanistic modeling and analysis in cardiovascular research. Am J Physiol Heart Circ Physiol 2024; 327:H473-H503. [PMID: 38904851 DOI: 10.1152/ajpheart.00766.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 06/07/2024] [Accepted: 06/16/2024] [Indexed: 06/22/2024]
Abstract
Computational, or in silico, models are an effective, noninvasive tool for investigating cardiovascular function. These models can be used in the analysis of experimental and clinical data to identify possible mechanisms of (ab)normal cardiovascular physiology. Recent advances in computing power and data management have led to innovative and complex modeling frameworks that simulate cardiovascular function across multiple scales. While commonly used in multiple disciplines, there is a lack of concise guidelines for the implementation of computer models in cardiovascular research. In line with recent calls for more reproducible research, it is imperative that scientists adhere to credible practices when developing and applying computational models to their research. The goal of this manuscript is to provide a consensus document that identifies best practices for in silico computational modeling in cardiovascular research. These guidelines provide the necessary methods for mechanistic model development, model analysis, and formal model calibration using fundamentals from statistics. We outline rigorous practices for computational, mechanistic modeling in cardiovascular research and discuss its synergistic value to experimental and clinical data.
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Affiliation(s)
- Mitchel J Colebank
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, Irvine, California, United States
| | - Pim A Oomen
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, Irvine, California, United States
| | - Colleen M Witzenburg
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States
| | - Anna Grosberg
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, Irvine, California, United States
| | - Daniel A Beard
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, United States
| | - Dirk Husmeier
- School of Mathematics and Statistics, University of Glasgow, Glasgow, United Kingdom
| | - Mette S Olufsen
- Department of Mathematics, North Carolina State University, Raleigh, North Carolina, United States
| | - Naomi C Chesler
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, Irvine, California, United States
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3
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Jack JT, Jensen M, Collins RT, Chan FP, Millett PC. Numerical study of hemodynamic flow in the aortic vessel of Williams syndrome patient with congenital heart disease. J Biomech 2024; 168:112124. [PMID: 38701696 DOI: 10.1016/j.jbiomech.2024.112124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/01/2024] [Accepted: 04/25/2024] [Indexed: 05/05/2024]
Abstract
Congenital arterial stenosis such as supravalvar aortic stenosis (SVAS) are highly prevalent in Williams syndrome (WS) and other arteriopathies pose a substantial health risk. Conventional tools for severity assessment, including clinical findings and pressure gradient estimations, often fall short due to their susceptibility to transient physiological changes and disease stage influences. Moreover, in the pediatric population, the severity of these and other congenital heart defects (CHDs) often restricts the applicability of invasive techniques for obtaining crucial physiological data. Conversely, evaluating CHDs and their progression requires a comprehensive understanding of intracardiac blood flow. Current imaging modalities, such as blood speckle imaging (BSI) and four-dimensional magnetic resonance imaging (4D MRI) face limitations in resolving flow data, especially in cases of elevated flow velocities. To address these challenges, we devised a computational framework employing zero-dimensional (0D) lumped parameter models coupled with patient-specific reconstructed geometries pre- and post-surgical intervention to execute computational fluid dynamic (CFD) simulations. This framework facilitates the analysis and visualization of intricate blood flow patterns, offering insights into geometry and flow dynamics alterations impacting cardiac function. In this study, we aim to assess the efficacy of surgical intervention in correcting an extreme aortic defect in a patient with WS, leading to reductions in wall shear stress (WSS), maximum velocity magnitude, pressure drop, and ultimately a decrease in cardiac workload.
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Affiliation(s)
- Justin T Jack
- University of Arkansas, Department of Mechanical Engineering, Fayetteville, AR, USA
| | - Morten Jensen
- University of Arkansas, Department of Biomedical Engineering, Fayetteville, AR, USA; University of Arkansas for Medical Sciences, Department of Surgery, Little Rock, AR, USA
| | - R Thomas Collins
- University of Kentucky, Department of Pediatrics, Division of Cardiology, Lexington, KY, USA
| | - Frandics Pak Chan
- Stanford University, Department of Radiology/Cardiovascular Imaging, Palo Alto, CA, USA
| | - Paul C Millett
- University of Arkansas, Department of Mechanical Engineering, Fayetteville, AR, USA.
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4
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de Azevedo FS, Almeida GDC, Alvares de Azevedo B, Ibanez Aguilar IF, Azevedo BN, Teixeira PS, Camargo GC, Correia MG, Nieckele AO, Oliveira GMM. Stress Load and Ascending Aortic Aneurysms: An Observational, Longitudinal, Single-Center Study Using Computational Fluid Dynamics. Bioengineering (Basel) 2024; 11:204. [PMID: 38534478 DOI: 10.3390/bioengineering11030204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/05/2024] [Accepted: 02/15/2024] [Indexed: 03/28/2024] Open
Abstract
Ascending aortic aneurysm (AAoA) is a silent disease with high mortality; however, the factors associated with a worse prognosis are not completely understood. The objective of this observational, longitudinal, single-center study was to identify the hemodynamic patterns and their influence on AAoA growth using computational fluid dynamics (CFD), focusing on the effects of geometrical variations on aortic hemodynamics. Personalized anatomic models were obtained from angiotomography scans of 30 patients in two different years (with intervals of one to three years between them), of which 16 (53%) showed aneurysm growth (defined as an increase in the ascending aorta volume by 5% or more). Numerically determined velocity and pressure fields were compared with the outcome of aneurysm growth. Through a statistical analysis, hemodynamic characteristics were found to be associated with aneurysm growth: average and maximum high pressure (superior to 100 Pa); average and maximum high wall shear stress (superior to 7 Pa) combined with high pressure (>100 Pa); and stress load over time (maximum pressure multiplied by the time interval between the exams). This study provides insights into a worse prognosis of this serious disease and may collaborate for the expansion of knowledge about mechanobiology in the progression of AAoA.
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Affiliation(s)
- Fabiula Schwartz de Azevedo
- Department of Cardiology, Federal University of Rio de Janeiro, Rio de Janeiro 21941-913, RJ, Brazil
- Research and Teaching Department, Instituto Nacional de Cardiologia, Rio de Janeiro 22240-006, RJ, Brazil
| | - Gabriela de Castro Almeida
- Department of Mechanical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, RJ, Brazil
| | - Bruno Alvares de Azevedo
- Department of Mechanical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, RJ, Brazil
| | - Ivan Fernney Ibanez Aguilar
- Department of Mechanical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, RJ, Brazil
| | - Bruno Nieckele Azevedo
- Department of Mechanical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, RJ, Brazil
| | | | - Gabriel Cordeiro Camargo
- Research and Teaching Department, Instituto Nacional de Cardiologia, Rio de Janeiro 22240-006, RJ, Brazil
| | - Marcelo Goulart Correia
- Research and Teaching Department, Instituto Nacional de Cardiologia, Rio de Janeiro 22240-006, RJ, Brazil
| | - Angela Ourivio Nieckele
- Department of Mechanical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, RJ, Brazil
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Ding CCA, Dokos S, Bakir AA, Zamberi NJ, Liew YM, Chan BT, Md Sari NA, Avolio A, Lim E. Simulating impaired left ventricular-arterial coupling in aging and disease: a systematic review. Biomed Eng Online 2024; 23:24. [PMID: 38388416 PMCID: PMC10885508 DOI: 10.1186/s12938-024-01206-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 01/11/2024] [Indexed: 02/24/2024] Open
Abstract
Aortic stenosis, hypertension, and left ventricular hypertrophy often coexist in the elderly, causing a detrimental mismatch in coupling between the heart and vasculature known as ventricular-vascular (VA) coupling. Impaired left VA coupling, a critical aspect of cardiovascular dysfunction in aging and disease, poses significant challenges for optimal cardiovascular performance. This systematic review aims to assess the impact of simulating and studying this coupling through computational models. By conducting a comprehensive analysis of 34 relevant articles obtained from esteemed databases such as Web of Science, Scopus, and PubMed until July 14, 2022, we explore various modeling techniques and simulation approaches employed to unravel the complex mechanisms underlying this impairment. Our review highlights the essential role of computational models in providing detailed insights beyond clinical observations, enabling a deeper understanding of the cardiovascular system. By elucidating the existing models of the heart (3D, 2D, and 0D), cardiac valves, and blood vessels (3D, 1D, and 0D), as well as discussing mechanical boundary conditions, model parameterization and validation, coupling approaches, computer resources and diverse applications, we establish a comprehensive overview of the field. The descriptions as well as the pros and cons on the choices of different dimensionality in heart, valve, and circulation are provided. Crucially, we emphasize the significance of evaluating heart-vessel interaction in pathological conditions and propose future research directions, such as the development of fully coupled personalized multidimensional models, integration of deep learning techniques, and comprehensive assessment of confounding effects on biomarkers.
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Affiliation(s)
- Corina Cheng Ai Ding
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
- Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Socrates Dokos
- Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Azam Ahmad Bakir
- University of Southampton Malaysia Campus, 79200, Iskandar Puteri, Johor, Malaysia
| | - Nurul Jannah Zamberi
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
- Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Yih Miin Liew
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Bee Ting Chan
- Department of Mechanical, Materials and Manufacturing Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, 43500, Selangor, Malaysia
| | - Nor Ashikin Md Sari
- Department of Medicine, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Alberto Avolio
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Einly Lim
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.
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Tunedal K, Viola F, Garcia BC, Bolger A, Nyström FH, Östgren CJ, Engvall J, Lundberg P, Dyverfeldt P, Carlhäll CJ, Cedersund G, Ebbers T. Haemodynamic effects of hypertension and type 2 diabetes: Insights from a 4D flow MRI-based personalized cardiovascular mathematical model. J Physiol 2023; 601:3765-3787. [PMID: 37485733 DOI: 10.1113/jp284652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 06/29/2023] [Indexed: 07/25/2023] Open
Abstract
Type 2 diabetes (T2D) and hypertension increase the risk of cardiovascular diseases mediated by whole-body changes to metabolism, cardiovascular structure and haemodynamics. The haemodynamic changes related to hypertension and T2D are complex and subject-specific, however, and not fully understood. We aimed to investigate the haemodynamic mechanisms in T2D and hypertension by comparing the haemodynamics between healthy controls and subjects with T2D, hypertension, or both. For all subjects, we combined 4D flow magnetic resonance imaging data, brachial blood pressure and a cardiovascular mathematical model to create a comprehensive subject-specific analysis of central haemodynamics. When comparing the subject-specific haemodynamic parameters between the four groups, the predominant haemodynamic difference is impaired left ventricular relaxation in subjects with both T2D and hypertension compared to subjects with only T2D, only hypertension and controls. The impaired relaxation indicates that, in this cohort, the long-term changes in haemodynamic load of co-existing T2D and hypertension cause diastolic dysfunction demonstrable at rest, whereas either disease on its own does not. However, through subject-specific predictions of impaired relaxation, we show that altered relaxation alone is not enough to explain the subject-specific and group-related differences; instead, a combination of parameters is affected in T2D and hypertension. These results confirm previous studies that reported more adverse effects from the combination of T2D and hypertension compared to either disease on its own. Furthermore, this shows the potential of personalized cardiovascular models in providing haemodynamic mechanistic insights and subject-specific predictions that could aid in the understanding and treatment planning of patients with T2D and hypertension. KEY POINTS: The combination of 4D flow magnetic resonance imaging data and a cardiovascular mathematical model allows for a comprehensive analysis of subject-specific haemodynamic parameters that otherwise cannot be derived non-invasively. Using this combination, we show that diastolic dysfunction in subjects with both type 2 diabetes (T2D) and hypertension is the main group-level difference between controls, subjects with T2D, subjects with hypertension, and subjects with both T2D and hypertension. These results suggest that, in this relatively healthy population, the additional load of both hypertension and T2D affects the haemodynamic function of the left ventricle, whereas each disease on its own is not enough to cause significant effects under resting conditions. Finally, using the subject-specific model, we show that the haemodynamic effects of diastolic dysfunction alone are not sufficient to explain all the observed haemodynamic differences. Instead, additional subject-specific variations in cardiac and vascular function combine to explain the complex haemodynamics of subjects affected by hypertension and/or T2D.
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Affiliation(s)
- Kajsa Tunedal
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Federica Viola
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Belén Casas Garcia
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Ann Bolger
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Fredrik H Nyström
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Carl Johan Östgren
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Division of Prevention, Rehabilitation and Community Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Jan Engvall
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Department of Clinical Physiology in Linköping, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Peter Lundberg
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Radiation Physics, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Petter Dyverfeldt
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Carl-Johan Carlhäll
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Department of Clinical Physiology in Linköping, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Gunnar Cedersund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Tino Ebbers
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
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Evaluation of Different Cannulation Strategies for Aortic Arch Surgery Using a Cardiovascular Numerical Simulator. BIOENGINEERING (BASEL, SWITZERLAND) 2023; 10:bioengineering10010060. [PMID: 36671632 PMCID: PMC9854437 DOI: 10.3390/bioengineering10010060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/19/2022] [Accepted: 12/30/2022] [Indexed: 01/06/2023]
Abstract
Aortic disease has a significant impact on quality of life. The involvement of the aortic arch requires the preservation of blood supply to the brain during surgery. Deep hypothermic circulatory arrest is an established technique for this purpose, although neurological injury remains high. Additional techniques have been used to reduce risk, although controversy still remains. A three-way cannulation approach, including both carotid arteries and the femoral artery or the ascending aorta, has been used successfully for aortic arch replacement and redo procedures. We developed circuits of the circulation to simulate blood flow during this type of cannulation set up. The CARDIOSIM© cardiovascular simulation platform was used to analyse the effect on haemodynamic and energetic parameters and the benefit derived in terms of organ perfusion pressure and flow. Our simulation approach based on lumped-parameter modelling, pressure-volume analysis and modified time-varying elastance provides a theoretical background to a three-way cannulation strategy for aortic arch surgery with correlation to the observed clinical practice.
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Structure (Epicardial Stenosis) and Function (Microvascular Dysfunction) That Influence Coronary Fractional Flow Reserve Estimation. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Background. The treatment of coronary stenosis is decided by performing high risk invasive surgery to generate the fractional flow reserve diagnostics index, a ratio of distal to proximal pressures in respect of coronary atherosclerotic plaques. Non-invasive methods are a need of the times that necessitate the use of mathematical models of coronary hemodynamic physiology. This study proposes an extensible mathematical description of the coronary vasculature that provides an estimate of coronary fractional flow reserve. Methods. By adapting an existing computational model of human coronary blood flow, the effects of large vessel stenosis and microvascular disease on fractional flow reserve were quantified. Several simulations generated flow and pressure information, which was used to compute fractional flow reserve under several conditions including focal stenosis, diffuse stenosis, and microvascular disease. Sensitivity analysis was used to uncover the influence of model parameters on fractional flow reserve. The model was simulated as coupled non-linear ordinary differential equations and numerically solved using our implicit higher order method. Results. Large vessel stenosis affected fractional flow reserve. The model predicts that the presence, rather than severity, of microvascular disease affects coronary flow deleteriously. Conclusions. The model provides a computationally inexpensive instrument for future in silico coronary blood flow investigations as well as clinical-imaging decision making. A combination of focal and diffuse stenosis appears to be essential to limit coronary flow. In addition to pressure measurements in the large epicardial vessels, diagnosis of microvascular disease is essential. The independence of the index with respect to heart rate suggests that computationally inexpensive steady state simulations may provide sufficient information to reliably compute the index.
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Khodaei S, Henstock A, Sadeghi R, Sellers S, Blanke P, Leipsic J, Emadi A, Keshavarz-Motamed Z. Personalized intervention cardiology with transcatheter aortic valve replacement made possible with a non-invasive monitoring and diagnostic framework. Sci Rep 2021; 11:10888. [PMID: 34035325 PMCID: PMC8149684 DOI: 10.1038/s41598-021-85500-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 02/12/2021] [Indexed: 02/04/2023] Open
Abstract
One of the most common acute and chronic cardiovascular disease conditions is aortic stenosis, a disease in which the aortic valve is damaged and can no longer function properly. Moreover, aortic stenosis commonly exists in combination with other conditions causing so many patients suffer from the most general and fundamentally challenging condition: complex valvular, ventricular and vascular disease (C3VD). Transcatheter aortic valve replacement (TAVR) is a new less invasive intervention and is a growing alternative for patients with aortic stenosis. Although blood flow quantification is critical for accurate and early diagnosis of C3VD in both pre and post-TAVR, proper diagnostic methods are still lacking because the fluid-dynamics methods that can be used as engines of new diagnostic tools are not well developed yet. Despite remarkable advances in medical imaging, imaging on its own is not enough to quantify the blood flow effectively. Moreover, understanding of C3VD in both pre and post-TAVR and its progression has been hindered by the absence of a proper non-invasive tool for the assessment of the cardiovascular function. To enable the development of new non-invasive diagnostic methods, we developed an innovative image-based patient-specific computational fluid dynamics framework for patients with C3VD who undergo TAVR to quantify metrics of: (1) global circulatory function; (2) global cardiac function as well as (3) local cardiac fluid dynamics. This framework is based on an innovative non-invasive Doppler-based patient-specific lumped-parameter algorithm and a 3-D strongly-coupled fluid-solid interaction. We validated the framework against clinical cardiac catheterization and Doppler echocardiographic measurements and demonstrated its diagnostic utility by providing novel analyses and interpretations of clinical data in eleven C3VD patients in pre and post-TAVR status. Our findings position this framework as a promising new non-invasive diagnostic tool that can provide blood flow metrics while posing no risk to the patient. The diagnostic information, that the framework can provide, is vitally needed to improve clinical outcomes, to assess patient risk and to plan treatment.
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Affiliation(s)
- Seyedvahid Khodaei
- grid.25073.330000 0004 1936 8227Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L7 Canada
| | - Alison Henstock
- grid.25073.330000 0004 1936 8227Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L7 Canada
| | - Reza Sadeghi
- grid.25073.330000 0004 1936 8227Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L7 Canada
| | - Stephanie Sellers
- grid.416553.00000 0000 8589 2327St. Paul’s Hospital, Vancouver, BC Canada ,grid.17091.3e0000 0001 2288 9830Department of Radiology, University of British Columbia, Vancouver, BC Canada
| | - Philipp Blanke
- grid.416553.00000 0000 8589 2327St. Paul’s Hospital, Vancouver, BC Canada ,grid.17091.3e0000 0001 2288 9830Department of Radiology, University of British Columbia, Vancouver, BC Canada
| | - Jonathon Leipsic
- grid.416553.00000 0000 8589 2327St. Paul’s Hospital, Vancouver, BC Canada ,grid.17091.3e0000 0001 2288 9830Department of Radiology, University of British Columbia, Vancouver, BC Canada
| | - Ali Emadi
- grid.25073.330000 0004 1936 8227Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L7 Canada ,grid.25073.330000 0004 1936 8227Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON Canada
| | - Zahra Keshavarz-Motamed
- grid.25073.330000 0004 1936 8227Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L7 Canada ,grid.25073.330000 0004 1936 8227School of Biomedical Engineering, McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227School of Computational Science and Engineering, McMaster University, Hamilton, ON Canada
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10
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Civilla L, Sbrollini A, Burattini L, Morettini M. An integrated lumped-parameter model of the cardiovascular system for the simulation of acute ischemic stroke: description of instantaneous changes in hemodynamics. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:3993-4010. [PMID: 34198422 DOI: 10.3934/mbe.2021200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Acute Ischemic Stroke (AIS) is defined as the acute condition of occlusion of a cerebral artery and is often caused by a Hypertensive Condition (HC). Due to its sudden occurrence, AIS is not observable the right moment it occurs, thus information about instantaneous changes in hemodynamics is limited. This study aimed to propose an integrated Lumped Parameter (LP) model of the cardiovascular system to simulate an AIS and describe instantaneous changes in hemodynamics. In the integrated LP model of the cardiovascular system, heart chambers have been modelled with elastance systems with controlled pressure inputs; heart valves have been modelled with static open/closed pressure-controlled valves; eventually, the vasculature has been modelled with resistor-inductor-capacitor (RLC) direct circuits and have been linked to the rest of the system through a series connection. After simulating physiological conditions, HC has been simulated by changing pressure inputs and constant RLC parameters. Then, AIS occurring in arteries of different sizes have been simulated by considering time-dependent RLC parameters due to the elimination from the model of the occluding artery; instantaneous changes in hemodynamics have been evaluated by Systemic Arteriolar Flow (Qa) and Systemic Arteriolar Pressure (Pa) drop with respect to those measured in HC. Occlusion of arteries of different sizes leaded to an average Qa drop of 0.38 ml/s per cardiac cycle (with minimum and maximum values of 0.04 ml/s and 1.93 ml/s) and average Pa drop of 0.39 mmHg, (with minimum and maximum values of 0.04 mmHg and 1.98 mmHg). In conclusion, hemodynamic variations due to AIS are very small with respect to HC. A direct relation between the inverse of the length of the artery in which the occlusion occurs and the hemodynamic variations has been highlighted; this may allow to link the severity of AIS to the length of the interested artery.
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Affiliation(s)
- Lorenzo Civilla
- Department of Information Engineering, UniversitȤ Politecnica delle Marche, Ancona 60131, Italy
| | - Agnese Sbrollini
- Department of Information Engineering, UniversitȤ Politecnica delle Marche, Ancona 60131, Italy
| | - Laura Burattini
- Department of Information Engineering, UniversitȤ Politecnica delle Marche, Ancona 60131, Italy
| | - Micaela Morettini
- Department of Information Engineering, UniversitȤ Politecnica delle Marche, Ancona 60131, Italy
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11
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Herrgårdh T, Madai VI, Kelleher JD, Magnusson R, Gustafsson M, Milani L, Gennemark P, Cedersund G. Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios. Neuroimage Clin 2021; 31:102694. [PMID: 34000646 PMCID: PMC8141769 DOI: 10.1016/j.nicl.2021.102694] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/27/2021] [Accepted: 05/04/2021] [Indexed: 11/28/2022]
Abstract
Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose a roadmap for hybrid modelling in the case of stroke care. We focus on two main tasks needed for the clinical setting: a) For stroke risk calculation, we propose a new two-step approach, where non-linear mixed effects models and bioinformatic network models yield biomarkers which are used as input to a machine learning model and b) For simulation of care scenarios, we propose a new four-step approach, which revolves around iterations between simulations of the mechanistic models and imputations of non-modelled or non-measured variables. We illustrate and discuss the different approaches in the context of Precision Medicine for stroke.
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Affiliation(s)
- Tilda Herrgårdh
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, 58185 Linköping, Sweden
| | - Vince I Madai
- Charité Lab for Artificial Intelligence in Medicine - CLAIM, Charité University Medicine Berlin, Germany; School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham, UK
| | - John D Kelleher
- ADAPT Research Centre, Technological University Dublin, Ireland
| | - Rasmus Magnusson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Sweden
| | - Mika Gustafsson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Sweden
| | - Lili Milani
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Peter Gennemark
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, 58185 Linköping, Sweden; Drug Metabolism and Pharmacokinetics, Early Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Gunnar Cedersund
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, 58185 Linköping, Sweden.
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12
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Rosalia L, Ozturk C, Van Story D, Horvath MA, Roche ET. Object‐Oriented Lumped‐Parameter Modeling of the Cardiovascular System for Physiological and Pathophysiological Conditions. ADVANCED THEORY AND SIMULATIONS 2021. [DOI: 10.1002/adts.202000216] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Luca Rosalia
- Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02139 USA
- Harvard‐MIT Program in Health Sciences and Technology Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Caglar Ozturk
- Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - David Van Story
- Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Markus A. Horvath
- Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02139 USA
- Harvard‐MIT Program in Health Sciences and Technology Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Ellen T. Roche
- Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02139 USA
- Harvard‐MIT Program in Health Sciences and Technology Massachusetts Institute of Technology Cambridge MA 02139 USA
- Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
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13
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Lantz J, Bäck S, Carlhäll CJ, Bolger A, Persson A, Karlsson M, Ebbers T. Impact of prosthetic mitral valve orientation on the ventricular flow field: Comparison using patient-specific computational fluid dynamics. J Biomech 2020; 116:110209. [PMID: 33422725 DOI: 10.1016/j.jbiomech.2020.110209] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 12/10/2020] [Accepted: 12/14/2020] [Indexed: 11/29/2022]
Abstract
Significant mitral valve regurgitation creates progressive adverse remodeling of the left ventricle (LV). Replacement of the failing valve with a prosthesis generally improves patient outcomes but leaves the patient with non-physiological intracardiac flow patterns that might contribute to their future risk of thrombus formation and embolism. It has been suggested that the angular orientation of the implanted valve might modify the postoperative distortion of the intraventricular flow field. In this study, we investigated the effect of prosthetic valve orientation on LV flow patterns by using heart geometry from a patient with LV dysfunction and a competent native mitral valve to calculate intracardiac flow fields with computational fluid dynamics (CFD). Results were validated using in vivo 4D Flow MRI. The computed flow fields were compared to calculations following virtual implantation of a mechanical heart valve oriented in four different angles to assess the effect of leaflet position. Flow patterns were visualized in long- and short-axes and quantified with flow component analysis. In comparison to a native valve, valve implantation increased the proportion of the mitral inflow remaining in the basal region and further increased the residual volume in the apical area. Only slight changes due to valve orientation were observed. Using our numerical framework, we demonstrated quantitative changes in left ventricular blood flow due to prosthetic mitral replacement. This framework may be used to improve design of prosthetic heart valves and implantation procedures to minimize the potential for apical flow stasis, and potentially assist personalized treatment planning.
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Affiliation(s)
- Jonas Lantz
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Sophia Bäck
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Carl-Johan Carlhäll
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden; Department of Clinical Physiology in Linköping, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Ann Bolger
- Department of Clinical Physiology in Linköping, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden; Department of Medicine, University of California, San Francisco, United States
| | - Anders Persson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden; Division of Radiology, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Matts Karlsson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden; Division of Applied Thermodynamics and Fluid Mechanics, Department of Management and Engineering, Linköping University, Sweden
| | - Tino Ebbers
- Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
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14
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From CT to artificial intelligence for complex assessment of plaque-associated risk. Int J Cardiovasc Imaging 2020; 36:2403-2427. [PMID: 32617720 DOI: 10.1007/s10554-020-01926-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 06/25/2020] [Indexed: 02/07/2023]
Abstract
The recent technological developments in the field of cardiac imaging have established coronary computed tomography angiography (CCTA) as a first-line diagnostic tool in patients with suspected coronary artery disease (CAD). CCTA offers robust information on the overall coronary circulation and luminal stenosis, also providing the ability to assess the composition, morphology, and vulnerability of atherosclerotic plaques. In addition, the perivascular adipose tissue (PVAT) has recently emerged as a marker of increased cardiovascular risk. The addition of PVAT quantification to standard CCTA imaging may provide the ability to extract information on local inflammation, for an individualized approach in coronary risk stratification. The development of image post-processing tools over the past several years allowed CCTA to provide a significant amount of data that can be incorporated into machine learning (ML) applications. ML algorithms that use radiomic features extracted from CCTA are still at an early stage. However, the recent development of artificial intelligence will probably bring major changes in the way we integrate clinical, biological, and imaging information, for a complex risk stratification and individualized therapeutic decision making in patients with CAD. This review aims to present the current evidence on the complex role of CCTA in the detection and quantification of vulnerable plaques and the associated coronary inflammation, also describing the most recent developments in the radiomics-based machine learning approach for complex assessment of plaque-associated risk.
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15
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Le Gall A, Vallée F, Pushparajah K, Hussain T, Mebazaa A, Chapelle D, Gayat É, Chabiniok R. Monitoring of cardiovascular physiology augmented by a patient-specific biomechanical model during general anesthesia. A proof of concept study. PLoS One 2020; 15:e0232830. [PMID: 32407353 PMCID: PMC7224549 DOI: 10.1371/journal.pone.0232830] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 04/22/2020] [Indexed: 12/29/2022] Open
Abstract
During general anesthesia (GA), direct analysis of arterial pressure or aortic flow waveforms may be inconclusive in complex situations. Patient-specific biomechanical models, based on data obtained during GA and capable to perform fast simulations of cardiac cycles, have the potential to augment hemodynamic monitoring. Such models allow to simulate Pressure-Volume (PV) loops and estimate functional indicators of cardiovascular (CV) system, e.g. ventricular-arterial coupling (Vva), cardiac efficiency (CE) or myocardial contractility, evolving throughout GA. In this prospective observational study, we created patient-specific biomechanical models of heart and vasculature of a reduced geometric complexity for n = 45 patients undergoing GA, while using transthoracic echocardiography and aortic pressure and flow signals acquired in the beginning of GA (baseline condition). If intraoperative hypotension (IOH) appeared, diluted norepinephrine (NOR) was administered and the model readjusted according to the measured aortic pressure and flow signals. Such patients were a posteriori assigned into a so-called hypotensive group. The accuracy of simulated mean aortic pressure (MAP) and stroke volume (SV) at baseline were in accordance with the guidelines for the validation of new devices or reference measurement methods in all patients. After NOR administration in the hypotensive group, the percentage of concordance with 10% exclusion zone between measurement and simulation was >95% for both MAP and SV. The modeling results showed a decreased Vva (0.64±0.37 vs 0.88±0.43; p = 0.039) and an increased CE (0.8±0.1 vs 0.73±0.11; p = 0.042) in hypotensive vs normotensive patients. Furthermore, Vva increased by 92±101%, CE decreased by 13±11% (p < 0.001 for both) and contractility increased by 14±11% (p = 0.002) in the hypotensive group post-NOR administration. In this work we demonstrated the application of fast-running patient-specific biophysical models to estimate PV loops and functional indicators of CV system using clinical data available during GA. The work paves the way for model-augmented hemodynamic monitoring at operating theatres or intensive care units to enhance the information on patient-specific physiology.
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Affiliation(s)
- Arthur Le Gall
- Inria, Paris, France
- LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Paris, France
- Anesthesiology and Intensive Care Department, Lariboisière - Saint Louis - Fernand Widal University Hospitals, Paris, France
- INSERM, Paris, France
| | - Fabrice Vallée
- Inria, Paris, France
- LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Paris, France
- Anesthesiology and Intensive Care Department, Lariboisière - Saint Louis - Fernand Widal University Hospitals, Paris, France
- INSERM, Paris, France
| | - Kuberan Pushparajah
- School of Biomedical Engineering & Imaging Sciences, St Thomas’ Hospital, King’s College London, London, United Kingdom
| | - Tarique Hussain
- Department of Pediatrics, Division of Pediatric Cardiology, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Alexandre Mebazaa
- Anesthesiology and Intensive Care Department, Lariboisière - Saint Louis - Fernand Widal University Hospitals, Paris, France
- INSERM, Paris, France
| | - Dominique Chapelle
- Inria, Paris, France
- LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Paris, France
| | - Étienne Gayat
- Anesthesiology and Intensive Care Department, Lariboisière - Saint Louis - Fernand Widal University Hospitals, Paris, France
- INSERM, Paris, France
| | - Radomír Chabiniok
- Inria, Paris, France
- LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Paris, France
- School of Biomedical Engineering & Imaging Sciences, St Thomas’ Hospital, King’s College London, London, United Kingdom
- Department of Mathematics, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic
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16
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Keshavarz-Motamed Z. A diagnostic, monitoring, and predictive tool for patients with complex valvular, vascular and ventricular diseases. Sci Rep 2020; 10:6905. [PMID: 32327673 PMCID: PMC7181740 DOI: 10.1038/s41598-020-63728-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 03/31/2020] [Indexed: 01/03/2023] Open
Abstract
Hemodynamics quantification is critically useful for accurate and early diagnosis, but we still lack proper diagnosticmethods for many cardiovascular diseases. Furthermore, as most interventions intend to recover the healthy condition, the ability to monitor and predict hemodynamics following interventions can have significant impacts on saving lives. Predictive methods are rare, enabling prediction of effects of interventions, allowing timely and personalized interventions and helping critical clinical decision making about life-threatening risks based on quantitative data. In this study, an innovative non-invasive imaged-based patient-specific diagnostic, monitoring and predictive tool (called C3VI-CMF) was developed, enabling quantifying (1) details of physiological flow and pressures through the heart and circulatory system; (2) heart function metrics. C3VI-CMF also predicts the breakdown of the effects of each disease constituents on the heart function. Presently, neither of these can be obtained noninvasively in patients and when invasive procedures are undertaken, the collected metrics cannot be by any means as complete as the ones C3VI-CMF provides. C3VI-CMF purposefully uses a limited number of noninvasive input parameters all of which can be measured using Doppler echocardiography and sphygmomanometer. Validation of C3VI-CMF, against cardiac catheterization in forty-nine patients with complex cardiovascular diseases, showed very good agreement with the measurements.
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Affiliation(s)
- Zahra Keshavarz-Motamed
- Department of Mechanical Engineering, McMaster University, Hamilton, ON, Canada.
- School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada.
- School of Computational Science and Engineering, McMaster University, Hamilton, ON, Canada.
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17
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Keller SP, Chang BY, Tan Q, Zhang Z, El Katerji A, Edelman ER. Dynamic Modulation of Device-Arterial Coupling to Determine Cardiac Output and Vascular Resistance. Ann Biomed Eng 2020; 48:2333-2342. [PMID: 32285344 DOI: 10.1007/s10439-020-02510-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 04/07/2020] [Indexed: 11/24/2022]
Abstract
Clinical adoption of mechanical circulatory support for shock is rapidly expanding. Achieving optimal therapeutic benefit requires metrics of state to guide titration and weaning of support. Using the transvalvular positioning of a percutaneous ventricular assist device (pVAD), device:heart interactions are leveraged to determine cardiac output (CO) and systemic vascular resistance (SVR) near-continuously without disrupting therapeutic function. An automated algorithm rapidly alternates between device support levels to dynamically modulate physiological response. Employing a two-element lumped parameter model of the vasculature, SVR and CO are quantified directly from measurements obtained by the pVAD without external calibration or invasive catheters. The approach was validated in an acute porcine model across a range of cardiac (CO = 3-10.6 L/min) and vascular (SVR = 501-1897 dyn s/cm5) states. Cardiac output calculations closely correlated (r = 0.82) to measurements obtained by the pulmonary artery catheter-based thermodilution method with a mean bias of 0.109 L/min and limits of agreement from - 1.67 to 1.89 L/min. SVR was also closely correlated (r = 0.86) to traditional catheter-based measurements with a mean bias of 62.1 dyn s/cm5 and limits of agreement from - 260 to 384 dyn s/cm5. Use of diagnostics integrated into therapeutic device function enables the potential for optimizing support to improve outcomes for cardiogenic shock.
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Affiliation(s)
- Steven P Keller
- Institute for Medical Engineering and Science, Massachusetts Institutes of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - Brian Y Chang
- Institute for Medical Engineering and Science, Massachusetts Institutes of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Qing Tan
- Abiomed Inc., Danvers, MA, 01923, USA
| | - Zhengyang Zhang
- Institute for Medical Engineering and Science, Massachusetts Institutes of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | | | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institutes of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
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18
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Kim EJ, Capoccia M. Synergistic Model of Cardiac Function with a Heart Assist Device. Bioengineering (Basel) 2019; 7:E1. [PMID: 31861506 PMCID: PMC7148543 DOI: 10.3390/bioengineering7010001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 12/03/2019] [Accepted: 12/17/2019] [Indexed: 11/16/2022] Open
Abstract
The breakdown of cardiac self-organization leads to heart diseases and failure, the number one cause of death worldwide. The left ventricular pressure-volume relation plays a key role in the diagnosis and treatment of heart diseases. Lumped-parameter models combined with pressure-volume loop analysis are very effective in simulating clinical scenarios with a view to treatment optimization and outcome prediction. Unfortunately, often invoked in this analysis is the traditional, time-varying elastance concept, in which the ratio of the ventricular pressure to its volume is prescribed by a periodic function of time, instead of being calculated consistently according to the change in feedback mechanisms (e.g., the lack or breakdown of self-organization) in heart diseases. Therefore, the application of the time-varying elastance for the analysis of left ventricular assist device (LVAD)-heart interactions has been questioned. We propose a paradigm shift from the time-varying elastance concept to a synergistic model of cardiac function by integrating the mechanical, electric, and chemical activity on microscale sarcomere and macroscale heart levels and investigating the effect of an axial rotary pump on a failing heart. We show that our synergistic model works better than the time-varying elastance model in reproducing LVAD-heart interactions with sufficient accuracy to describe the left ventricular pressure-volume relation.
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Affiliation(s)
- Eun-Jin Kim
- Fluid and Complex Systems Research Centre, Coventry University, Coventry CV1 2TT, UK
- School of Mathematics and Statistics, University of Sheffield, Sheffield S3 7RH, UK
| | - Massimo Capoccia
- Aortic and Cardiac Surgery, Royal Brompton Hospital, Sydney Street, Chelsea, SW3 6NP London, UK
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19
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Callaghan FM, Burkhardt B, Geiger J, Valsangiacomo Buechel ER, Kellenberger CJ. Flow quantification dependency on background phase correction techniques in 4D‐flow MRI. Magn Reson Med 2019; 83:2264-2275. [DOI: 10.1002/mrm.28085] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 10/09/2019] [Accepted: 10/24/2019] [Indexed: 12/23/2022]
Affiliation(s)
- Fraser M. Callaghan
- Center for MR Research University Children's Hospital Zurich Switzerland
- Children's Research Center University Children's Hospital Zurich Switzerland
| | - Barbara Burkhardt
- Children's Research Center University Children's Hospital Zurich Switzerland
- Division of Pediatric Cardiology University Children's Hospital Zurich Switzerland
| | - Julia Geiger
- Children's Research Center University Children's Hospital Zurich Switzerland
- Department of Diagnostic Imaging University Children's Hospital Zurich Switzerland
| | - Emanuela R. Valsangiacomo Buechel
- Children's Research Center University Children's Hospital Zurich Switzerland
- Division of Pediatric Cardiology University Children's Hospital Zurich Switzerland
| | - Christian J. Kellenberger
- Children's Research Center University Children's Hospital Zurich Switzerland
- Department of Diagnostic Imaging University Children's Hospital Zurich Switzerland
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20
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Chang BY, Keller SP, Edelman ER. Leveraging Device-Arterial Coupling to Determine Cardiac and Vascular State. IEEE Trans Biomed Eng 2019; 66:2800-2808. [PMID: 30703007 PMCID: PMC6661194 DOI: 10.1109/tbme.2019.2895752] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
OBJECTIVE Limitations in available diagnostic metrics restrict the efficacy of managing therapies for cardiogenic shock. In current clinical practice, cardiovascular state is inferred through measurement of pulmonary capillary wedge pressure and reliance on linear approximations between pressure and flow to estimate peripheral vascular resistance. Mechanical circulatory support devices residing within the left ventricle and aorta provide an opportunity for both determining cardiac and vascular state and offering therapeutic benefit. We leverage the controllable mode of operation and transvalvular position of an indwelling percutaneous ventricular assist device to assess vascular and, in turn, cardiac state through the effects of device-arterial coupling across different levels of device support. METHODS Vascular state is determined by measuring changes in the pressure waveforms induced through intentional variation in the device generated blood flow. We evaluate this impact by applying a lumped parameter model to quantify state-specific vascular resistance and compliance and calculate beat-to-beat stroke volume and cardiac output in both animal models and retrospective patient data without external calibration. RESULTS Vascular state was accurately predicted in patients and animals in both baseline and experimental conditions. In the animal, stroke volume was predicted within a total root mean square error of 3.71 mL (n = 482). CONCLUSION We demonstrate that device-arterial coupling is a powerful tool for evaluating patient and state specific parameters of cardiovascular function. SIGNIFICANCE These insights may yield improved clinical care and support the development of next generation mechanical circulatory support devices that determine and operate in tandem with the supported organ.
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21
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Pulse transit time estimation of aortic pulse wave velocity and blood pressure using machine learning and simulated training data. PLoS Comput Biol 2019; 15:e1007259. [PMID: 31415554 PMCID: PMC6711549 DOI: 10.1371/journal.pcbi.1007259] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 08/27/2019] [Accepted: 07/09/2019] [Indexed: 01/17/2023] Open
Abstract
Recent developments in cardiovascular modelling allow us to simulate blood flow in an entire human body. Such model can also be used to create databases of virtual subjects, with sizes limited only by computational resources. In this work, we study if it is possible to estimate cardiovascular health indices using machine learning approaches. In particular, we carry out theoretical assessment of estimating aortic pulse wave velocity, diastolic and systolic blood pressure and stroke volume using pulse transit/arrival timings derived from photopletyshmography signals. For predictions, we train Gaussian process regression using a database of virtual subjects generated with a cardiovascular simulator. Simulated results provides theoretical assessment of accuracy for predictions of the health indices. For instance, aortic pulse wave velocity can be estimated with a high accuracy (r > 0.9) when photopletyshmography is measured from left carotid artery using a combination of foot-to-foot pulse transmit time and peak location derived for the predictions. Similar accuracy can be reached for diastolic blood pressure, but predictions of systolic blood pressure are less accurate (r > 0.75) and the stroke volume predictions are mostly contributed by heart rate. Recently there has been a strong trend for self-monitoring of your cardiovascular health and new wearable sport trackers and mobile applications are coming to the market everyday. However, such solutions are mostly taking advantage of heart rate measurement. Other health indices such as blood pressure and pulse wave velocity reflecting to the condition of cardiovascular system would also be of great interest, but such solutions for continuous monitoring are barely existing or are at least unreliable. In this paper, we use computational modelling to assess theoretical capabilities of such measurements. We concentrate on predicting health indices using on pulse transmit time type of measurements. Such measurements could be carried out, for example, with photopletyshmography sensor or an optical sensor already found from several wearable sport trackers. We use cardiovascular modelling to create a database of “virtual subjects”, which is applied with machine learning to construct predictors for health indices. Our findings suggest that aortic pulse wave velocity and diastolic blood pressured could be predicted with a high accuracy, but predictions of systolic blood pressure are less accurate.
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22
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Aguado AM, Olivares AL, Yagüe C, Silva E, Nuñez-García M, Fernandez-Quilez Á, Mill J, Genua I, Arzamendi D, De Potter T, Freixa X, Camara O. In silico Optimization of Left Atrial Appendage Occluder Implantation Using Interactive and Modeling Tools. Front Physiol 2019; 10:237. [PMID: 30967786 PMCID: PMC6440369 DOI: 10.3389/fphys.2019.00237] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 02/22/2019] [Indexed: 01/26/2023] Open
Abstract
According to clinical studies, around one third of patients with atrial fibrillation (AF) will suffer a stroke during their lifetime. Between 70 and 90% of these strokes are caused by thrombus formed in the left atrial appendage. In patients with contraindications to oral anticoagulants, a left atrial appendage occluder (LAAO) is often implanted to prevent blood flow entering in the LAA. A limited range of LAAO devices is available, with different designs and sizes. Together with the heterogeneity of LAA morphology, these factors make LAAO success dependent on clinician's experience. A sub-optimal LAAO implantation can generate thrombi outside the device, eventually leading to stroke if not treated. The aim of this study was to develop clinician-friendly tools based on biophysical models to optimize LAAO device therapies. A web-based 3D interactive virtual implantation platform, so-called VIDAA, was created to select the most appropriate LAAO configurations (type of device, size, landing zone) for a given patient-specific LAA morphology. An initial LAAO configuration is proposed in VIDAA, automatically computed from LAA shape features (centreline, diameters). The most promising LAAO settings and LAA geometries were exported from VIDAA to build volumetric meshes and run Computational Fluid Dynamics (CFD) simulations to assess blood flow patterns after implantation. Risk of thrombus formation was estimated from the simulated hemodynamics with an index combining information from blood flow velocity and complexity. The combination of the VIDAA platform with in silico indices allowed to identify the LAAO configurations associated to a lower risk of thrombus formation; device positioning was key to the creation of regions with turbulent flows after implantation. Our results demonstrate the potential for optimizing LAAO therapy settings during pre-implant planning based on modeling tools and contribute to reduce the risk of thrombus formation after treatment.
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Affiliation(s)
- Ainhoa M Aguado
- PhySense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Andy L Olivares
- PhySense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Carlos Yagüe
- PhySense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Etelvino Silva
- Division of Interventional Cardiology, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Marta Nuñez-García
- PhySense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Álvaro Fernandez-Quilez
- PhySense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jordi Mill
- PhySense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ibai Genua
- PhySense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Dabit Arzamendi
- Division of Interventional Cardiology, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Tom De Potter
- Arrhythmia Unit, Department of Cardiology, Cardiovascular Center, Aalst, Belgium
| | - Xavier Freixa
- Department of Cardiology, Hospital Clínic de Barcelona, Universitat de Barcelona, Barcelona, Spain
| | - Oscar Camara
- PhySense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
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23
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Seemann F, Arvidsson P, Nordlund D, Kopic S, Carlsson M, Arheden H, Heiberg E. Noninvasive Quantification of Pressure-Volume Loops From Brachial Pressure and Cardiovascular Magnetic Resonance. Circ Cardiovasc Imaging 2019; 12:e008493. [DOI: 10.1161/circimaging.118.008493] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Felicia Seemann
- Department of Clinical Physiology, Lund University, Skåne University Hospital (F.S., P.A., D.N., S.K., M.C., H.A., E.H.)
- Department of Biomedical Engineering (F.S., E.H.), Lund University, Sweden
| | - Per Arvidsson
- Department of Clinical Physiology, Lund University, Skåne University Hospital (F.S., P.A., D.N., S.K., M.C., H.A., E.H.)
| | - David Nordlund
- Department of Clinical Physiology, Lund University, Skåne University Hospital (F.S., P.A., D.N., S.K., M.C., H.A., E.H.)
| | - Sascha Kopic
- Department of Clinical Physiology, Lund University, Skåne University Hospital (F.S., P.A., D.N., S.K., M.C., H.A., E.H.)
| | - Marcus Carlsson
- Department of Clinical Physiology, Lund University, Skåne University Hospital (F.S., P.A., D.N., S.K., M.C., H.A., E.H.)
| | - Håkan Arheden
- Department of Clinical Physiology, Lund University, Skåne University Hospital (F.S., P.A., D.N., S.K., M.C., H.A., E.H.)
| | - Einar Heiberg
- Department of Clinical Physiology, Lund University, Skåne University Hospital (F.S., P.A., D.N., S.K., M.C., H.A., E.H.)
- Department of Biomedical Engineering (F.S., E.H.), Lund University, Sweden
- Wallenberg Center for Molecular Medicine (E.H.), Lund University, Sweden
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24
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Casas B, Viola F, Cedersund G, Bolger AF, Karlsson M, Carlhäll CJ, Ebbers T. Non-invasive Assessment of Systolic and Diastolic Cardiac Function During Rest and Stress Conditions Using an Integrated Image-Modeling Approach. Front Physiol 2018; 9:1515. [PMID: 30425650 PMCID: PMC6218619 DOI: 10.3389/fphys.2018.01515] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 10/09/2018] [Indexed: 01/08/2023] Open
Abstract
Background: The possibility of non-invasively assessing load-independent parameters characterizing cardiac function is of high clinical value. Typically, these parameters are assessed during resting conditions. However, for diagnostic purposes, the parameter behavior across a physiologically relevant range of heart rate and loads is more relevant than the isolated measurements performed at rest. This study sought to evaluate changes in non-invasive estimations of load-independent parameters of left-ventricular contraction and relaxation patterns at rest and during dobutamine stress. Methods: We applied a previously developed approach that combines non-invasive measurements with a physiologically-based, reduced-order model of the cardiovascular system to provide subject-specific estimates of parameters characterizing left ventricular function. In this model, the contractile state of the heart at each time point along the cardiac cycle is modeled using a time-varying elastance curve. Non-invasive data, including four-dimensional magnetic resonance imaging (4D Flow MRI) measurements, were acquired in nine subjects without a known heart disease at rest and during dobutamine stress. For each of the study subjects, we constructed two personalized models corresponding to the resting and the stress state. Results: Applying the modeling framework, we identified significant increases in the left ventricular contraction rate constant [from 1.5 ± 0.3 to 2 ± 0.5 (p = 0.038)] and relaxation constant [from 37.2 ± 6.9 to 46.1 ± 12 (p = 0.028)]. In addition, we found a significant decrease in the elastance diastolic time constant from 0.4 ± 0.04 s to 0.3 ± 0.03 s (p = 0.008). Conclusions: The integrated image-modeling approach allows the assessment of cardiovascular function given as model-based parameters. The agreement between the estimated parameter values and previously reported effects of dobutamine demonstrates the potential of the approach to assess advanced metrics of pathophysiology that are otherwise difficult to obtain non-invasively in clinical practice.
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Affiliation(s)
- Belén Casas
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Federica Viola
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Gunnar Cedersund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Ann F Bolger
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Department of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Matts Karlsson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Division of Applied Thermodynamics and Fluid Mechanics, Department of Management and Engineering, Linköping University, Linköping, Sweden
| | - Carl-Johan Carlhäll
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Clinical Physiology, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Tino Ebbers
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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25
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Bhalodiya JM, Palit A, Tiwari MK, Prasad SK, Bhudia SK, Arvanitis TN, Williams MA. A Novel Hierarchical Template Matching Model for Cardiac Motion Estimation. Sci Rep 2018. [PMID: 29540762 PMCID: PMC5852007 DOI: 10.1038/s41598-018-22543-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Cardiovascular disease diagnosis and prognosis can be improved by measuring patient-specific in-vivo local myocardial strain using Magnetic Resonance Imaging. Local myocardial strain can be determined by tracking the movement of sample muscles points during cardiac cycle using cardiac motion estimation model. The tracking accuracy of the benchmark Free Form Deformation (FFD) model is greatly affected due to its dependency on tunable parameters and regularisation function. Therefore, Hierarchical Template Matching (HTM) model, which is independent of tunable parameters, regularisation function, and image-specific features, is proposed in this article. HTM has dense and uniform points correspondence that provides HTM with the ability to estimate local muscular deformation with a promising accuracy of less than half a millimetre of cardiac wall muscle. As a result, the muscles tracking accuracy has been significantly (p < 0.001) improved (30%) compared to the benchmark model. Such merits of HTM provide reliably calculated clinical measures which can be incorporated into the decision-making process of cardiac disease diagnosis and prognosis.
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Affiliation(s)
- Jayendra M Bhalodiya
- Warwick Manufacturing Group (WMG), University of Warwick, CV4 7AL, Coventry, United Kingdom.
| | - Arnab Palit
- Warwick Manufacturing Group (WMG), University of Warwick, CV4 7AL, Coventry, United Kingdom
| | - Manoj K Tiwari
- Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - Sanjay K Prasad
- Royal Brompton and Harefield NHS Foundation Trust, London, United Kingdom
| | - Sunil K Bhudia
- Royal Brompton and Harefield NHS Foundation Trust, London, United Kingdom
| | - Theodoros N Arvanitis
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, United Kingdom
| | - Mark A Williams
- Warwick Manufacturing Group (WMG), University of Warwick, CV4 7AL, Coventry, United Kingdom
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