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Strocchi M, Wijesuriya N, Mehta V, de Vere F, Rinaldi CA, Niederer SA. Computational Modelling Enabling In Silico Trials for Cardiac Physiologic Pacing. J Cardiovasc Transl Res 2023:10.1007/s12265-023-10453-y. [PMID: 37870689 DOI: 10.1007/s12265-023-10453-y] [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] [Received: 08/21/2023] [Accepted: 10/10/2023] [Indexed: 10/24/2023]
Abstract
Conduction system pacing (CSP) has the potential to achieve physiological-paced activation by pacing the ventricular conduction system. Before CSP is adopted in standard clinical practice, large, randomised, and multi-centre trials are required to investigate CSP safety and efficacy compared to standard biventricular pacing (BVP). Furthermore, there are unanswered questions about pacing thresholds required to achieve optimal pacing delivery while preventing device battery draining, and about which patient groups are more likely to benefit from CSP rather than BVP. In silico studies have been increasingly used to investigate mechanisms underlying changes in cardiac function in response to pathologies and treatment. In the context of CSP, they have been used to improve our understanding of conduction system capture to optimise CSP delivery and battery life, and noninvasively compare different pacing methods on different patient groups. In this review, we discuss the in silico studies published to date investigating different aspects of CSP delivery.
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Affiliation(s)
- Marina Strocchi
- National Heart and Lung Institute, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
| | - Nadeev Wijesuriya
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Vishal Mehta
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Felicity de Vere
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Christopher A Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Steven A Niederer
- National Heart and Lung Institute, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- The Alan Turing Institute, London, UK
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Africa PC, Piersanti R, Fedele M, Dede' L, Quarteroni A. lifex-fiber: an open tool for myofibers generation in cardiac computational models. BMC Bioinformatics 2023; 24:143. [PMID: 37046208 PMCID: PMC10091584 DOI: 10.1186/s12859-023-05260-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 03/27/2023] [Indexed: 04/14/2023] Open
Abstract
BACKGROUND Modeling the whole cardiac function involves the solution of several complex multi-physics and multi-scale models that are highly computationally demanding, which call for simpler yet accurate, high-performance computational tools. Despite the efforts made by several research groups, no software for whole-heart fully-coupled cardiac simulations in the scientific community has reached full maturity yet. RESULTS In this work we present [Formula: see text]-fiber, an innovative tool for the generation of myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, which are the essential building blocks for modeling the electrophysiological, mechanical and electromechanical cardiac function, from single-chamber to whole-heart simulations. [Formula: see text]-fiber is the first publicly released module for cardiac simulations based on [Formula: see text], an open-source, high-performance Finite Element solver for multi-physics, multi-scale and multi-domain problems developed in the framework of the iHEART project, which aims at making in silico experiments easily reproducible and accessible to a wide community of users, including those with a background in medicine or bio-engineering. CONCLUSIONS The tool presented in this document is intended to provide the scientific community with a computational tool that incorporates general state of the art models and solvers for simulating the cardiac function within a high-performance framework that exposes a user- and developer-friendly interface. This report comes with an extensive technical and mathematical documentation to welcome new users to the core structure of [Formula: see text]-fiber and to provide them with a possible approach to include the generated cardiac fibers into more sophisticated computational pipelines. In the near future, more modules will be successively published either as pre-compiled binaries for x86-64 Linux systems or as open source software.
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Affiliation(s)
| | - Roberto Piersanti
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Marco Fedele
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Luca Dede'
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Alfio Quarteroni
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Galappaththige S, Gray RA, Costa CM, Niederer S, Pathmanathan P. Credibility assessment of patient-specific computational modeling using patient-specific cardiac modeling as an exemplar. PLoS Comput Biol 2022; 18:e1010541. [PMID: 36215228 PMCID: PMC9550052 DOI: 10.1371/journal.pcbi.1010541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 09/02/2022] [Indexed: 11/07/2022] Open
Abstract
Reliable and robust simulation of individual patients using patient-specific models (PSMs) is one of the next frontiers for modeling and simulation (M&S) in healthcare. PSMs, which form the basis of digital twins, can be employed as clinical tools to, for example, assess disease state, predict response to therapy, or optimize therapy. They may also be used to construct virtual cohorts of patients, for in silico evaluation of medical product safety and/or performance. Methods and frameworks have recently been proposed for evaluating the credibility of M&S in healthcare applications. However, such efforts have generally been motivated by models of medical devices or generic patient models; how best to evaluate the credibility of PSMs has largely been unexplored. The aim of this paper is to understand and demonstrate the credibility assessment process for PSMs using patient-specific cardiac electrophysiological (EP) modeling as an exemplar. We first review approaches used to generate cardiac PSMs and consider how verification, validation, and uncertainty quantification (VVUQ) apply to cardiac PSMs. Next, we execute two simulation studies using a publicly available virtual cohort of 24 patient-specific ventricular models, the first a multi-patient verification study, the second investigating the impact of uncertainty in personalized and non-personalized inputs in a virtual cohort. We then use the findings from our analyses to identify how important characteristics of PSMs can be considered when assessing credibility with the approach of the ASME V&V40 Standard, accounting for PSM concepts such as inter- and intra-user variability, multi-patient and “every-patient” error estimation, uncertainty quantification in personalized vs non-personalized inputs, clinical validation, and others. The results of this paper will be useful to developers of cardiac and other medical image based PSMs, when assessing PSM credibility. Patient-specific models are computational models that have been personalized using data from a patient. After decades of research, recent computational, data science and healthcare advances have opened the door to the fulfilment of the enormous potential of such models, from truly personalized medicine to efficient and cost-effective testing of new medical products. However, reliability (credibility) of patient-specific models is key to their success, and there are currently no general guidelines for evaluating credibility of patient-specific models. Here, we consider how frameworks and model evaluation activities that have been developed for generic (not patient-specific) computational models, can be extended to patient specific models. We achieve this through a detailed analysis of the activities required to evaluate cardiac electrophysiological models, chosen as an exemplar field due to its maturity and the complexity of such models. This is the first paper on the topic of reliability of patient-specific models and will help pave the way to reliable and trusted patient-specific modeling across healthcare applications.
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Affiliation(s)
- Suran Galappaththige
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Richard A. Gray
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Caroline Mendonca Costa
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Steven Niederer
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Pras Pathmanathan
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
- * E-mail:
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Koivumäki JT, Hoffman J, Maleckar MM, Einevoll GT, Sundnes J. Computational cardiac physiology for new modelers: Origins, foundations, and future. Acta Physiol (Oxf) 2022; 236:e13865. [PMID: 35959512 DOI: 10.1111/apha.13865] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 01/29/2023]
Abstract
Mathematical models of the cardiovascular system have come a long way since they were first introduced in the early 19th century. Driven by a rapid development of experimental techniques, numerical methods, and computer hardware, detailed models that describe physical scales from the molecular level up to organs and organ systems have been derived and used for physiological research. Mathematical and computational models can be seen as condensed and quantitative formulations of extensive physiological knowledge and are used for formulating and testing hypotheses, interpreting and directing experimental research, and have contributed substantially to our understanding of cardiovascular physiology. However, in spite of the strengths of mathematics to precisely describe complex relationships and the obvious need for the mathematical and computational models to be informed by experimental data, there still exist considerable barriers between experimental and computational physiological research. In this review, we present a historical overview of the development of mathematical and computational models in cardiovascular physiology, including the current state of the art. We further argue why a tighter integration is needed between experimental and computational scientists in physiology, and point out important obstacles and challenges that must be overcome in order to fully realize the synergy of experimental and computational physiological research.
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Affiliation(s)
- Jussi T Koivumäki
- Faculty of Medicine and Health Technology, and Centre of Excellence in Body-on-Chip Research, Tampere University, Tampere, Finland
| | - Johan Hoffman
- Division of Computational Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Mary M Maleckar
- Computational Physiology Department, Simula Research Laboratory, Oslo, Norway
| | - Gaute T Einevoll
- Centre for Integrative Neuroplasticity, University of Oslo, Oslo, Norway.,Department of Physics, University of Oslo, Oslo, Norway.,Department of Physics, Norwegian University of Life Sciences, Ås, Norway
| | - Joakim Sundnes
- Computational Physiology Department, Simula Research Laboratory, Oslo, Norway
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Barone A, Grieco D, Gizzi A, Molinari L, Zaltieri M, Massaroni C, Loppini A, Schena E, Bressi E, de Ruvo E, Caló L, Filippi S. A Simulation Study of the Effects of His Bundle Pacing in Left Bundle Branch Block. Med Eng Phys 2022; 107:103847. [DOI: 10.1016/j.medengphy.2022.103847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/30/2022] [Accepted: 07/09/2022] [Indexed: 11/28/2022]
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Vergara C, Stella S, Maines M, Africa PC, Catanzariti D, Demattè C, Centonze M, Nobile F, Quarteroni A, Del Greco M. Computational electrophysiology of the coronary sinus branches based on electro-anatomical mapping for the prediction of the latest activated region. Med Biol Eng Comput 2022; 60:2307-2319. [PMID: 35729476 PMCID: PMC9293833 DOI: 10.1007/s11517-022-02610-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 06/07/2022] [Indexed: 01/18/2023]
Abstract
This work dealt with the assessment of a computational tool to estimate the electrical activation in the left ventricle focusing on the latest electrically activated segment (LEAS) in patients with left bundle branch block and possible myocardial fibrosis. We considered the Eikonal-diffusion equation and to recover the electrical activation maps in the myocardium. The model was calibrated by using activation times acquired in the coronary sinus (CS) branches or in the CS solely with an electroanatomic mapping system (EAMS) during cardiac resynchronization therapy (CRT). We applied our computational tool to ten patients founding an excellent accordance with EAMS measures; in particular, the error for LEAS location was less than 4 mm. We also calibrated our model using only information in the CS, still obtaining an excellent agreement with the measured LEAS. The proposed tool was able to accurately reproduce the electrical activation maps and in particular LEAS location in the CS branches, with an almost real-time computational effort, regardless of the presence of myocardial fibrosis, even when information only at CS was used to calibrate the model. This could be useful in the clinical practice since LEAS is often used as a target site for the left lead placement during CRT.
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Affiliation(s)
- Christian Vergara
- LABS, Dipartimento Di Chimica, Materiali E Ingegneria Chimica “Giulio Natta”, Politecnico Di Milano, Piazza Leonardo da Vinci 32, 20233 Milan, Italy
| | - Simone Stella
- Dipartimento Di Matematica, MOX, Politecnico Di Milano, Piazza Leonardo da Vinci 32, 20233 Milan, Italy
| | - Massimiliano Maines
- Department of Cardiology, S. Maria del Carmine Hospital, corso Verona 4, 38068 Rovereto, TN Italy
| | - Pasquale Claudio Africa
- Dipartimento Di Matematica, MOX, Politecnico Di Milano, Piazza Leonardo da Vinci 32, 20233 Milan, Italy
| | - Domenico Catanzariti
- Department of Cardiology, S. Maria del Carmine Hospital, corso Verona 4, 38068 Rovereto, TN Italy
| | - Cristina Demattè
- Department of Cardiology, S. Maria del Carmine Hospital, corso Verona 4, 38068 Rovereto, TN Italy
| | - Maurizio Centonze
- U.O. Di Radiologia Di Borgo-Pergine, Borgo Valsugana Hospital, viale Vicenza 9, 38051 Borgo Valsugana, (TN) Italy
| | - Fabio Nobile
- Institute of Mathematics, CSQI, École Polytechnique Fédérale de Lausanne, Route Cantonale, 1015 Lausanne, Switzerland
| | - Alfio Quarteroni
- Dipartimento Di Matematica, MOX, Politecnico Di Milano, Piazza Leonardo da Vinci 32, 20233 Milan, Italy
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Maurizio Del Greco
- Department of Cardiology, S. Maria del Carmine Hospital, corso Verona 4, 38068 Rovereto, TN Italy
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Grandits T, Effland A, Pock T, Krause R, Plank G, Pezzuto S. GEASI: Geodesic-based earliest activation sites identification in cardiac models. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3505. [PMID: 34170082 PMCID: PMC8459297 DOI: 10.1002/cnm.3505] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 06/19/2021] [Accepted: 06/22/2021] [Indexed: 05/18/2023]
Abstract
The identification of the initial ventricular activation sequence is a critical step for the correct personalization of patient-specific cardiac models. In healthy conditions, the Purkinje network is the main source of the electrical activation, but under pathological conditions the so-called earliest activation sites (EASs) are possibly sparser and more localized. Yet, their number, location and timing may not be easily inferred from remote recordings, such as the epicardial activation or the 12-lead electrocardiogram (ECG), due to the underlying complexity of the model. In this work, we introduce GEASI (Geodesic-based Earliest Activation Sites Identification) as a novel approach to simultaneously identify all EASs. To this end, we start from the anisotropic eikonal equation modeling cardiac electrical activation and exploit its Hamilton-Jacobi formulation to minimize a given objective function, for example, the quadratic mismatch to given activation measurements. This versatile approach can be extended to estimate the number of activation sites by means of the topological gradient, or fitting a given ECG. We conducted various experiments in 2D and 3D for in-silico models and an in-vivo intracardiac recording collected from a patient undergoing cardiac resynchronization therapy. The results demonstrate the clinical applicability of GEASI for potential future personalized models and clinical intervention.
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Affiliation(s)
- Thomas Grandits
- Institute of Computer Graphics and VisionTU GrazGrazAustria
- BioTechMed‐GrazGrazAustria
| | - Alexander Effland
- Institute of Computer Graphics and VisionTU GrazGrazAustria
- Silicon Austria Labs (TU Graz SAL DES Lab)GrazAustria
- Institute for Applied MathematicsUniversity of BonnBonnGermany
| | - Thomas Pock
- Institute of Computer Graphics and VisionTU GrazGrazAustria
- BioTechMed‐GrazGrazAustria
| | - Rolf Krause
- Center for Computational Medicine in Cardiology, Euler InstituteUniversità della Svizzera ItalianaLuganoSwitzerland
| | - Gernot Plank
- BioTechMed‐GrazGrazAustria
- Gottfried Schatz Research Center—Division of BiophysicsMedical University of GrazGrazAustria
| | - Simone Pezzuto
- Center for Computational Medicine in Cardiology, Euler InstituteUniversità della Svizzera ItalianaLuganoSwitzerland
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Fedele M, Quarteroni A. Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3435. [PMID: 33415829 PMCID: PMC8244076 DOI: 10.1002/cnm.3435] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 01/01/2021] [Accepted: 01/02/2021] [Indexed: 06/05/2023]
Abstract
In order to simulate the cardiac function for a patient-specific geometry, the generation of the computational mesh is crucially important. In practice, the input is typically a set of unprocessed polygonal surfaces coming either from a template geometry or from medical images. These surfaces need ad-hoc processing to be suitable for a volumetric mesh generation. In this work we propose a set of new algorithms and tools aiming to facilitate the mesh generation process. In particular, we focus on different aspects of a cardiac mesh generation pipeline: (1) specific polygonal surface processing for cardiac geometries, like connection of different heart chambers or segmentation outputs; (2) generation of accurate boundary tags; (3) definition of mesh-size functions dependent on relevant geometric quantities; (4) processing and connecting together several volumetric meshes. The new algorithms-implemented in the open-source software vmtk-can be combined with each other allowing the creation of personalized pipelines, that can be optimized for each cardiac geometry or for each aspect of the cardiac function to be modeled. Thanks to these features, the proposed tools can significantly speed-up the mesh generation process for a large range of cardiac applications, from single-chamber single-physics simulations to multi-chambers multi-physics simulations. We detail all the proposed algorithms motivating them in the cardiac context and we highlight their flexibility by showing different examples of cardiac mesh generation pipelines.
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Affiliation(s)
- Marco Fedele
- MOX, Department of MathematicsPolitecnico di MilanoMilanItaly
| | - Alfio Quarteroni
- MOX, Department of MathematicsPolitecnico di MilanoMilanItaly
- Institute of MathematicsÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
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