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Belletti R, Osca J, Romero Perez L, Saiz J. Influence of genetic mutations to atria vulnerability to atrial fibrillation: An in-silico 3D human atria study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108307. [PMID: 38981143 DOI: 10.1016/j.cmpb.2024.108307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 06/21/2024] [Accepted: 06/24/2024] [Indexed: 07/11/2024]
Abstract
BACKGROUND AND OBJECTIVE Personalized 3D computer models of atria have been extensively implemented in the last yearsas a tool to facilitate the understanding of the mechanisms underlying different forms of arrhythmia, such as atrial fibrillation (AF). Meanwhile, genetic mutations acting on potassium channel dynamics were demonstrated to induce fibrillatory episodes in asymptomatic patients. This research study aims at assessing the effects and the atrial susceptibility to AF of three gain-of-function mutations - namely, KCNH2 T895M, KCNH2 T436M, and KCNE3-V17M - associated with AF outbreaks, using highly detailed 3D atrial models with realistic wall thickness and heterogenous histological properties. METHODS The 3D atrial model was generated by reconstructing segmented anatomical structures from CT scans of an AF patient. Modified versions of the Courtemanche human atrial myocyte model were used to reproduce the electrophysiological activity of the WT and of the three mutant cells. Ectopic foci (EF) were simulated in sixteen locations across the atrial mesh using an S1-S2 protocol with two S2 basic cycle lengths (BCL) and eleven coupling intervals in order to induce arrhythmias. RESULTS The three genetic mutations at 3D level reduced the APD90. The KCNE3-V17M mutation provoked the highest shortening (55 % in RA and LA with respect to WT), followed by KCNH2 T895M (14 % in RA and 18 % LA with respect to WT)and KCNH2 T436M (7 % in RA and 9 % LA with respect to WT). The KCNE3-V17M mutation led to arrhythmia in 67 % of the cases simulated and in 94 % of ectopic foci considered, at S2 BCL equal to 100 ms. The KCNH2 T436M and KCNH2 T895M mutations increased the vulnerability to AF in a similar way, leading to arrhythmic episodes in 7 % of the simulated conditions, at S2 BCL set to 160 ms. Overall, 60 % of the arrhythmic events generated arise in the left atrium. Spiral waves, multiple rotors and disordered electrical pattern were elicited in the presence of the KCNE3-V17M mutation, exhibiting an instantaneous mean frequency of 7.6 Hz with a mean standard deviation of 1.12 Hz. The scroll waves induced in the presence of the KCNH2 T436M and KCNH2 T895M mutations showed steadiness and regularity with an instantaneous mean frequencies in the range of 4.9 - 5.1 Hz and a mean standard deviation within 0.19 - 0.53 Hz. CONCLUSIONS The pro-arrhythmogenicity of the KCNE3-V17M, KCNH2 T895M and KCNH2 T436M mutations was studied and proved on personalized 3D cardiac models. The three genetic mutations were demonstrated to increase the predisposition of atrial tissue to the formation of AF-susceptible substrate in different ways based on their effects on electrophysiological properties of the atria.
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Affiliation(s)
- Rebecca Belletti
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politécnica de València, Camino de Vera, s/n, 46022,Valencia, Spain.
| | - Joaquín Osca
- Electrophysiology Section, Cardiology Department, Hospital Universitari i Politecnic La Fe, Avinguda de Fernando Abril Martorell, 106, Quatre Carreres, 46026, València, Spain
| | - Lucia Romero Perez
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politécnica de València, Camino de Vera, s/n, 46022,Valencia, Spain
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politécnica de València, Camino de Vera, s/n, 46022,Valencia, Spain
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Shaaban A, Scott SS, Greenlee AN, Binda N, Noor A, Webb A, Guo S, Purdy N, Pennza N, Habib A, Mohammad SJ, Smith SA. Atrial fibrillation in cancer, anticancer therapies, and underlying mechanisms. J Mol Cell Cardiol 2024; 194:118-132. [PMID: 38897563 DOI: 10.1016/j.yjmcc.2024.06.005] [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: 11/02/2023] [Revised: 06/03/2024] [Accepted: 06/04/2024] [Indexed: 06/21/2024]
Abstract
Atrial fibrillation (AF) is a common arrhythmic complication in cancer patients and can be exacerbated by traditional cytotoxic and targeted anticancer therapies. Increased incidence of AF in cancer patients is independent of confounding factors, including preexisting myocardial arrhythmogenic substrates, type of cancer, or cancer stage. Mechanistically, AF is characterized by fast unsynchronized atrial contractions with rapid ventricular response, which impairs ventricular filling and results in various symptoms such as fatigue, chest pain, and shortness of breath. Due to increased blood stasis, a consequence of both cancer and AF, concern for stroke increases in this patient population. To compound matters, cardiotoxic anticancer therapies themselves promote AF; thereby exacerbating AF morbidity and mortality in cancer patients. In this review, we examine the relationship between AF, cancer, and cardiotoxic anticancer therapies with a focus on the shared molecular and electrophysiological mechanisms linking these disease processes. We also explore the potential role of sodium-glucose co-transporter 2 inhibitors (SGLT2i) in the management of anticancer-therapy-induced AF.
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Affiliation(s)
- Adnan Shaaban
- The Ohio State University College of Medicine, Department of Internal Medicine, Columbus, OH 43210, USA
| | - Shane S Scott
- Medical Scientist Training Program, Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH, USA; Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University College of Medicine, Columbus, OH 43210, USA; Bob and Corrinne Frick Center for Heart Failure and Arrhythmia Research, The Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Ashley N Greenlee
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University College of Medicine, Columbus, OH 43210, USA; Bob and Corrinne Frick Center for Heart Failure and Arrhythmia Research, The Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Nkongho Binda
- The Ohio State University College of Medicine, Department of Internal Medicine, Columbus, OH 43210, USA
| | - Ali Noor
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Averie Webb
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Shuliang Guo
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University College of Medicine, Columbus, OH 43210, USA; Bob and Corrinne Frick Center for Heart Failure and Arrhythmia Research, The Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Najhee Purdy
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University College of Medicine, Columbus, OH 43210, USA; Bob and Corrinne Frick Center for Heart Failure and Arrhythmia Research, The Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Nicholas Pennza
- Ohio University Heritage College of Osteopathic Medicine, Athens, OH 45701, USA
| | - Alma Habib
- The Ohio State University College of Medicine, Department of Internal Medicine, Division of Hematology, Columbus, OH 43210, USA
| | - Somayya J Mohammad
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University College of Medicine, Columbus, OH 43210, USA; Bob and Corrinne Frick Center for Heart Failure and Arrhythmia Research, The Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Sakima A Smith
- The Ohio State University College of Medicine, Department of Internal Medicine, Columbus, OH 43210, USA; Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University College of Medicine, Columbus, OH 43210, USA; Bob and Corrinne Frick Center for Heart Failure and Arrhythmia Research, The Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA.
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3
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Lim S, Mangala MM, Holliday M, Cserne Szappanos H, Barratt-Ross S, Li S, Thorpe J, Liang W, Ranpura GN, Vandenberg JI, Semsarian C, Hill AP, Hool LC. Reduced connexin-43 expression, slow conduction and repolarisation dispersion in a model of hypertrophic cardiomyopathy. Dis Model Mech 2024; 17:dmm050407. [PMID: 39189070 DOI: 10.1242/dmm.050407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 07/16/2024] [Indexed: 08/28/2024] Open
Abstract
Hypertrophic cardiomyopathy (HCM) is an inherited heart muscle disease that is characterised by left ventricular wall thickening, cardiomyocyte disarray and fibrosis, and is associated with arrhythmias, heart failure and sudden death. However, it is unclear to what extent the electrophysiological disturbances that lead to sudden death occur secondary to structural changes in the myocardium or as a result of HCM cardiomyocyte electrophysiology. In this study, we used an induced pluripotent stem cell model of the R403Q variant in myosin heavy chain 7 (MYH7) to study the electrophysiology of HCM cardiomyocytes in electrically coupled syncytia, revealing significant conduction slowing and increased spatial dispersion of repolarisation - both well-established substrates for arrhythmia. Analysis of rhythmonome protein expression in MYH7 R403Q cardiomyocytes showed reduced expression of connexin-43 (also known as GJA1), sodium channels and inward rectifier potassium channels - a three-way hit that reduces electrotonic coupling and slows cardiac conduction. Our data represent a previously unreported, biophysical basis for arrhythmia in HCM that is intrinsic to cardiomyocyte electrophysiology. Later in the progression of the disease, these proarrhythmic phenotypes may be accentuated by myocyte disarray and fibrosis to contribute to sudden death.
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Affiliation(s)
- Seakcheng Lim
- Agnes Ginges Centre for Molecular Cardiology at Centenary Institute, The University of Sydney, Sydney 2050, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Melissa M Mangala
- Victor Chang Cardiac Research Institute, Sydney, 2010, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Sydney 2052, Australia
| | - Mira Holliday
- Agnes Ginges Centre for Molecular Cardiology at Centenary Institute, The University of Sydney, Sydney 2050, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | | | - Samantha Barratt-Ross
- Agnes Ginges Centre for Molecular Cardiology at Centenary Institute, The University of Sydney, Sydney 2050, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Serena Li
- Agnes Ginges Centre for Molecular Cardiology at Centenary Institute, The University of Sydney, Sydney 2050, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Jordan Thorpe
- Victor Chang Cardiac Research Institute, Sydney, 2010, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Sydney 2052, Australia
| | - Whitney Liang
- Victor Chang Cardiac Research Institute, Sydney, 2010, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Sydney 2052, Australia
| | - Ginell N Ranpura
- Agnes Ginges Centre for Molecular Cardiology at Centenary Institute, The University of Sydney, Sydney 2050, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Jamie I Vandenberg
- Victor Chang Cardiac Research Institute, Sydney, 2010, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Sydney 2052, Australia
| | - Christopher Semsarian
- Agnes Ginges Centre for Molecular Cardiology at Centenary Institute, The University of Sydney, Sydney 2050, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney 2050, Australia
| | | | - Livia C Hool
- Victor Chang Cardiac Research Institute, Sydney, 2010, Australia
- School of Human Sciences, The University of Western Australia, Crawley 6009, Australia
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Medvedev RY, Afolabi SO, Turner DGP, Glukhov AV. Mechanisms of stretch-induced electro-anatomical remodeling and atrial arrhythmogenesis. J Mol Cell Cardiol 2024; 193:11-24. [PMID: 38797242 PMCID: PMC11260238 DOI: 10.1016/j.yjmcc.2024.05.011] [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: 10/31/2023] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 05/29/2024]
Abstract
Atrial fibrillation (AF) is the most common cardiac rhythm disorder, often occurring in the setting of atrial distension and elevated myocardialstretch. While various mechano-electrochemical signal transduction pathways have been linked to AF development and progression, the underlying molecular mechanisms remain poorly understood, hampering AF therapies. In this review, we describe different aspects of stretch-induced electro-anatomical remodeling as seen in animal models and in patients with AF. Specifically, we focus on cellular and molecular mechanisms that are responsible for mechano-electrochemical signal transduction and the development of ectopic beats triggering AF from pulmonary veins, the most common source of paroxysmal AF. Furthermore, we describe structural changes caused by stretch occurring before and shortly after the onset of AF as well as during AF progression, contributing to longstanding forms of AF. We also propose mechanical stretch as a new dimension to the concept "AF begets AF", in addition to underlying diseases. Finally, we discuss the mechanisms of these electro-anatomical alterations in a search for potential therapeutic strategies and the development of novel antiarrhythmic drugs targeted at the components of mechano-electrochemical signal transduction not only in cardiac myocytes, but also in cardiac non-myocyte cells.
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Affiliation(s)
- Roman Y Medvedev
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Saheed O Afolabi
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Department of Pharmacology and Therapeutics, University of Ilorin, Ilorin, Nigeria
| | - Daniel G P Turner
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Alexey V Glukhov
- Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.
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5
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Trayanova NA, Lyon A, Shade J, Heijman J. Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation. Physiol Rev 2024; 104:1265-1333. [PMID: 38153307 DOI: 10.1152/physrev.00017.2023] [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: 04/05/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023] Open
Abstract
The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.
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Affiliation(s)
- Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Aurore Lyon
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Heart and Lungs, Department of Medical Physiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jordi Heijman
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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6
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Sakata K, Bradley RP, Prakosa A, Yamamoto CAP, Ali SY, Loeffler S, Tice BM, Boyle PM, Kholmovski EG, Yadav R, Sinha SK, Marine JE, Calkins H, Spragg DD, Trayanova NA. Assessing the arrhythmogenic propensity of fibrotic substrate using digital twins to inform a mechanisms-based atrial fibrillation ablation strategy. NATURE CARDIOVASCULAR RESEARCH 2024; 3:857-868. [PMID: 39157719 PMCID: PMC11329066 DOI: 10.1038/s44161-024-00489-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 05/15/2024] [Indexed: 08/20/2024]
Abstract
Atrial fibrillation (AF), the most common heart rhythm disorder, may cause stroke and heart failure. For patients with persistent AF with fibrosis proliferation, the standard AF treatment-pulmonary vein isolation-has poor outcomes, necessitating redo procedures, owing to insufficient understanding of what constitutes good targets in fibrotic substrates. Here we present a prospective clinical and personalized digital twin study that characterizes the arrhythmogenic properties of persistent AF substrates and uncovers locations possessing rotor-attracting capabilities. Among these, a portion needs to be ablated to render the substrate not inducible for rotors, but the rest (37%) lose rotor-attracting capabilities when another location is ablated. Leveraging digital twin mechanistic insights, we suggest ablation targets that eliminate arrhythmia propensity with minimum lesions while also minimizing the risk of iatrogenic tachycardia and AF recurrence. Our findings provide further evidence regarding the appropriate substrate ablation targets in persistent AF, opening the door for effective strategies to mitigate patients' AF burden.
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Affiliation(s)
- Kensuke Sakata
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Ryan P. Bradley
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
- Research Computing, Lehigh University, Bethlehem, PA, USA
| | - Adityo Prakosa
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | | | - Syed Yusuf Ali
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Shane Loeffler
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Brock M. Tice
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Patrick M. Boyle
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Eugene G. Kholmovski
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Ritu Yadav
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sunil Kumar Sinha
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joseph E. Marine
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hugh Calkins
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David D. Spragg
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Natalia A. Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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7
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Clayton RH, Sridhar S. Re-entry in models of cardiac ventricular tissue with scar represented as a Gaussian random field. Front Physiol 2024; 15:1403545. [PMID: 39005500 PMCID: PMC11239552 DOI: 10.3389/fphys.2024.1403545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 06/10/2024] [Indexed: 07/16/2024] Open
Abstract
Introduction: Fibrotic scar in the heart is known to act as a substrate for arrhythmias. Regions of fibrotic scar are associated with slowed or blocked conduction of the action potential, but the detailed mechanisms of arrhythmia formation are not well characterised and this can limit the effective diagnosis and treatment of scar in patients. The aim of this computational study was to evaluate different representations of fibrotic scar in models of 2D 10 × 10 cm ventricular tissue, where the region of scar was defined by sampling a Gaussian random field with an adjustable length scale of between 1.25 and 10.0 mm. Methods: Cellular electrophysiology was represented by the Ten Tusscher 2006 model for human ventricular cells. Fibrotic scar was represented as a spatially varying diffusion, with different models of the boundary between normal and fibrotic tissue. Dispersion of activation time and action potential duration (APD) dispersion was assessed in each sample by pacing at an S1 cycle length of 400 ms followed by a premature S2 beat with a coupling interval of 323 ms. Vulnerability to reentry was assessed with an aggressive pacing protocol. In all models, simulated fibrosis acted to delay activation, to increase the dispersion of APD, and to generate re-entry. Results: A higher incidence of re-entry was observed in models with simulated fibrotic scar at shorter length scale, but the type of model used to represent fibrotic scar had a much bigger influence on the incidence of reentry. Discussion: This study shows that in computational models of fibrotic scar the effects that lead to either block or propagation of the action potential are strongly influenced by the way that fibrotic scar is represented in the model, and so the results of computational studies involving fibrotic scar should be interpreted carefully.
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Affiliation(s)
- Richard H Clayton
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - S Sridhar
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
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8
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Martínez Díaz P, Sánchez J, Fitzen N, Ravens U, Dössel O, Loewe A. The right atrium affects in silico arrhythmia vulnerability in both atria. Heart Rhythm 2024; 21:799-805. [PMID: 38301854 DOI: 10.1016/j.hrthm.2024.01.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/18/2024] [Accepted: 01/24/2024] [Indexed: 02/03/2024]
Affiliation(s)
- Patricia Martínez Díaz
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Jorge Sánchez
- Institute of Information and Communication Technologies (ITACA), Universitat Politècnica de València (UPV), Valencia, Spain
| | - Nikola Fitzen
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Ursula Ravens
- Institute for Experimental Cardiovascular Medicine, Universitätsklinikum Freiburg, Freiburg, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
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9
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Gonzalo A, Augustin CM, Bifulco SF, Telle Å, Chahine Y, Kassar A, Guerrero-Hurtado M, Durán E, Martínez-Legazpi P, Flores O, Bermejo J, Plank G, Akoum N, Boyle PM, Del Alamo JC. Patient-specific multi-physics simulations of fibrotic changes in left atrial tissue mechanics impact on hemodynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.596526. [PMID: 38853952 PMCID: PMC11160719 DOI: 10.1101/2024.05.29.596526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Stroke is a leading cause of death and disability worldwide. Atrial myopathy, including fibrosis, is associated with an increased risk of ischemic stroke, but the mechanisms underlying this association are poorly understood. Fibrosis modifies myocardial structure, impairing electrical propagation and tissue biomechanics, and creating stagnant flow regions where clots could form. Fibrosis can be mapped non-invasively using late gadolinium enhancement magnetic resonance imaging (LGE-MRI). However, fibrosis maps are not currently incorporated into stroke risk calculations or computational electro-mechano-fluidic models. We present multi-physics simulations of left atrial (LA) myocardial motion and hemodynamics using patient-specific anatomies and fibrotic maps from LGE-MRI. We modify tissue stiffness and active tension generation in fibrotic regions and investigate how these changes affect LA flow for different fibrotic burdens. We find that fibrotic regions and, to a lesser extent, non-fibrotic regions experience reduced myocardial strain, resulting in decreased LA emptying fraction consistent with clinical observations. Both fibrotic tissue stiffening and hypocontractility independently reduce LA function, but together, these two alterations cause more pronounced effects than either one alone. Fibrosis significantly alters flow patterns throughout the atrial chamber, and particularly, the filling and emptying jets of the left atrial appendage (LAA). The effects of fibrosis in LA flow are largely captured by the concomitant changes in LA emptying fraction except inside the LAA, where a multi-factorial behavior is observed. This work illustrates how high-fidelity, multi-physics models can be used to study thrombogenesis mechanisms in a patient-specific manner, shedding light onto the link between atrial fibrosis and ischemic stroke. Key points Left atrial (LA) fibrosis is associated with arrhythmogenesis and increased risk of ischemic stroke; its extent and pattern can be quantified on a patient-specific basis using late gadolinium enhancement magnetic resonance imaging.Current stroke risk prediction tools have limited personalization, and their accuracy could be improvedfib by incorporating patient-specific information like fibrotic maps and hemodynamic patterns.We present the first electro-mechano-fluidic multi-physics computational simulations of LA flow, including fibrosis and anatomies from medical imaging.Mechanical changes in fibrotic tissue impair global LA motion, decreasing LA and left atrial appendage (LAA) emptying fractions, especially in subjects with higher fibrosis burdens.Fibrotic-mediated LA motion impairment alters LA and LAA flow near the endocardium and the whole cavity, ultimately leading to more stagnant blood regions in the LAA.
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Riaz Gondal MU, Atta Mehdi H, Khenhrani RR, Kumari N, Ali MF, Kumar S, Faraz M, Malik J. Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology. Cardiol Rev 2024:00045415-990000000-00270. [PMID: 38761137 DOI: 10.1097/crd.0000000000000715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/20/2024]
Abstract
Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.
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Affiliation(s)
| | - Hassan Atta Mehdi
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Raja Ram Khenhrani
- Department of Medicine, Internal Medicine Fellow, Shaheed Mohtarma Benazir Bhutto Medical College and Lyari General Hospital, Karachi, Pakistan
| | - Neha Kumari
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Muhammad Faizan Ali
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi, Pakistan
| | - Sooraj Kumar
- Department of Medicine, Jinnah Sindh Medical University, Karachi, Pakistan; and
| | - Maria Faraz
- Department of Cardiovascular Medicine, Cardiovascular Analytics Group, Rawalpindi, Pakistan
| | - Jahanzeb Malik
- Department of Cardiovascular Medicine, Cardiovascular Analytics Group, Rawalpindi, Pakistan
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11
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Okenov A, Nezlobinsky T, Zeppenfeld K, Vandersickel N, Panfilov AV. Computer based method for identification of fibrotic scars from electrograms and local activation times on the epi- and endocardial surfaces of the ventricles. PLoS One 2024; 19:e0300978. [PMID: 38625849 PMCID: PMC11020530 DOI: 10.1371/journal.pone.0300978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 03/07/2024] [Indexed: 04/18/2024] Open
Abstract
Cardiac fibrosis stands as one of the most critical conditions leading to lethal cardiac arrhythmias. Identifying the precise location of cardiac fibrosis is crucial for planning clinical interventions in patients with various forms of ventricular and atrial arrhythmias. As fibrosis impedes and alters the path of electrical waves, detecting fibrosis in the heart can be achieved through analyzing electrical signals recorded from its surface. In current clinical practices, it has become feasible to record electrical activity from both the endocardial and epicardial surfaces of the heart. This paper presents a computational method for reconstructing 3D fibrosis using unipolar electrograms obtained from both surfaces of the ventricles. The proposed method calculates the percentage of fibrosis in various ventricular segments by analyzing the local activation times and peak-to-peak amplitudes of the electrograms. Initially, the method was tested using simulated data representing idealized fibrosis in a heart segment; subsequently, it was validated in the left ventricle with fibrosis obtained from a patient with nonischemic cardiomyopathy. The method successfully determined the location and extent of fibrosis in 204 segments of the left ventricle model with an average error of 0.0±4.3% (N = 204). Moreover, the method effectively detected fibrotic scars in the mid-myocardial region, a region known to present challenges in accurate detection using electrogram amplitude as the primary criterion.
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Affiliation(s)
- Arstanbek Okenov
- Department of Physics and Astronomy, Ghent University, Gent, Belgium
| | - Timur Nezlobinsky
- Department of Physics and Astronomy, Ghent University, Gent, Belgium
| | - Katja Zeppenfeld
- Department of Cardiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Nele Vandersickel
- Department of Physics and Astronomy, Ghent University, Gent, Belgium
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12
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Jin Z, Hwang T, Kim D, Lim B, Kwon OS, Kim S, Kim MH, Park JW, Yu HT, Kim TH, Uhm JS, Joung B, Lee MH, Pak HN. Anti- and pro-fibrillatory effects of pulmonary vein isolation gaps in human atrial fibrillation digital twins. NPJ Digit Med 2024; 7:81. [PMID: 38532181 DOI: 10.1038/s41746-024-01075-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 03/07/2024] [Indexed: 03/28/2024] Open
Abstract
Although pulmonary vein isolation (PVI) gaps and extrapulmonary vein triggers contribute to recurrence after atrial fibrillation (AF) ablation, their precise mechanisms remain unproven. Our study assessed the impact of PVI gaps on rhythm outcomes using a human AF digital twin. We included 50 patients (76.0% with persistent AF) who underwent catheter ablation with a realistic AF digital twin by integrating computed tomography and electroanatomical mapping. We evaluated the final rhythm status, including AF and atrial tachycardia (AT), across 600 AF episodes, considering factors including PVI level, PVI gap number, and pacing locations. Our findings revealed that antral PVI had a significantly lower ratio of AF at the final rhythm (28% vs. 56%, p = 0.002) than ostial PVI. Increasing PVI gap numbers correlated with an increased ratio of AF at the final rhythm (p < 0.001). Extra-PV induction yielded a higher ratio of AF at the final rhythm than internal PV induction (77.5% vs. 59.0%, p < 0.001). In conclusion, our human AF digital twin model helped assess AF maintenance mechanisms. Clinical trial registration: https://www.clinicaltrials.gov ; Unique identifier: NCT02138695.
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Affiliation(s)
- Ze Jin
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Taehyun Hwang
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Daehoon Kim
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Byounghyun Lim
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Oh-Seok Kwon
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sangbin Kim
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Moon-Hyun Kim
- Division of Cardiology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Je-Wook Park
- Division of Cardiology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Hee Tae Yu
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Tae-Hoon Kim
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jae-Sun Uhm
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Boyoung Joung
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Moon-Hyoung Lee
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hui-Nam Pak
- Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
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13
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Lootens S, Janssens I, Van Den Abeele R, Wülfers EM, Bezerra AS, Verstraeten B, Hendrickx S, Okenov A, Nezlobinsky T, Panfilov AV, Vandersickel N. Directed Graph Mapping exceeds Phase Mapping for the detection of simulated 2D meandering rotors in fibrotic tissue with added noise. Comput Biol Med 2024; 171:108138. [PMID: 38401451 DOI: 10.1016/j.compbiomed.2024.108138] [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: 10/27/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 02/26/2024]
Abstract
Cardiac arrhythmias such as atrial fibrillation (AF) are recognised to be associated with re-entry or rotors. A rotor is a wave of excitation in the cardiac tissue that wraps around its refractory tail, causing faster-than-normal periodic excitation. The detection of rotor centres is of crucial importance in guiding ablation strategies for the treatment of arrhythmia. The most popular technique for detecting rotor centres is Phase Mapping (PM), which detects phase singularities derived from the phase of a signal. This method has been proven to be prone to errors, especially in regimes of fibrotic tissue and temporal noise. Recently, a novel technique called Directed Graph Mapping (DGM) was developed to detect rotational activity such as rotors by creating a network of excitation. This research aims to compare the performance of advanced PM techniques versus DGM for the detection of rotors using 64 simulated 2D meandering rotors in the presence of various levels of fibrotic tissue and temporal noise. Four strategies were employed to compare the performances of PM and DGM. These included a visual analysis, a comparison of F2-scores and distance distributions, and calculating p-values using the mid-p McNemar test. Results indicate that in the case of low meandering, fibrosis and noise, PM and DGM yield excellent results and are comparable. However, in the case of high meandering, fibrosis and noise, PM is undeniably prone to errors, mainly in the form of an excess of false positives, resulting in low precision. In contrast, DGM is more robust against these factors as F2-scores remain high, yielding F2≥0.931 as opposed to the best PM F2≥0.635 across all 64 simulations.
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Affiliation(s)
| | - Iris Janssens
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | | | - Eike M Wülfers
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | | | - Bjorn Verstraeten
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Sander Hendrickx
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Arstanbek Okenov
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Timur Nezlobinsky
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
| | - Alexander V Panfilov
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium; World-Class Research Center "Digital Biodesign and personalised healthcare", Sechenov University, Moscow 119991, Russia; Laboratory of Computational Biology and Medicine, Ural Federal University, Ekaterinburg 620002, Russia
| | - Nele Vandersickel
- Department of Physics and Astronomy, Ghent University, Ghent, Belgium
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14
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Laubenbacher R, Mehrad B, Shmulevich I, Trayanova N. Digital twins in medicine. NATURE COMPUTATIONAL SCIENCE 2024; 4:184-191. [PMID: 38532133 PMCID: PMC11102043 DOI: 10.1038/s43588-024-00607-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/12/2024] [Indexed: 03/28/2024]
Abstract
Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.
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Affiliation(s)
- R Laubenbacher
- Department of Medicine, University of Florida, Gainesville, FL, USA.
| | - B Mehrad
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | | | - N Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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15
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Riku S, Inden Y, Yanagisawa S, Fujii A, Tomomatsu T, Nakagomi T, Shimojo M, Okajima T, Furui K, Suga K, Suzuki S, Shibata R, Murohara T. Distributions and number of drivers on real-time phase mapping associated with successful atrial fibrillation termination during catheter ablation for non-paroxysmal atrial fibrillation. J Interv Card Electrophysiol 2024; 67:303-317. [PMID: 37354370 DOI: 10.1007/s10840-023-01588-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 05/31/2023] [Indexed: 06/26/2023]
Abstract
BACKGROUND Real-time phase mapping (ExTRa™) is useful in determining the strategy of catheter ablation for non-paroxysmal atrial fibrillation (AF). This study aimed to investigate the features of drivers of AF associated with its termination during ablation. METHODS Thirty-six patients who underwent catheter ablation for non-paroxysmal AF using online real-time phase mapping (ExTRa™) were enrolled. A significant AF driver was defined as an area with a non-passively activated ratio of ≥ 50% on mapping analysis in the left atrium (LA). All drivers were simultaneously evaluated using a low-voltage area, complex fractionated atrial electrogram (CFAE), and rotational activity by unipolar electrogram analysis. The electrical characteristics of drivers were compared between patients with and without AF termination during the procedure. RESULTS Twelve patients achieved AF termination during the procedure. The total number of drivers detected on the mapping was significantly lower (4.4 ± 1.6 vs. 7.4 ± 3.8, p = 0.007), and the drivers were more concentrated in limited LA regions (2.8 ± 0.9 vs. 3.9 ± 1.4, p = 0.009) in the termination group than in the non-termination group. The presence of drivers 2-6 with limited (≤ 3) LA regions showed a tenfold increase in the likelihood of AF termination, with 83% specificity and 67% sensitivity. Among 231 AF drivers, the drivers related to termination exhibited a greater overlap of CFAE (56.8 ± 34.1% vs. 39.5 ± 30.4%, p = 0.004) than the non-related drivers. The termination group showed a trend toward a lower recurrence rate after ablation (p = 0.163). CONCLUSIONS Rotors responsible for AF maintenance may be characterized in cases with concentrated regions and fewer drivers on mapping.
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Affiliation(s)
- Shuro Riku
- Department of Cardiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan
| | - Yasuya Inden
- Department of Cardiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan
| | - Satoshi Yanagisawa
- Department of Cardiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan.
| | - Aya Fujii
- Department of Cardiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan
| | - Toshiro Tomomatsu
- Department of Cardiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan
| | - Toshifumi Nakagomi
- Department of Cardiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan
| | - Masafumi Shimojo
- Department of Cardiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan
| | - Takashi Okajima
- Department of Cardiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan
| | - Koichi Furui
- Department of Cardiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan
| | - Kazumasa Suga
- Department of Cardiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan
| | - Susumu Suzuki
- Department of Cardiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan
| | - Rei Shibata
- Department of Cardiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan
| | - Toyoaki Murohara
- Department of Cardiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan
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16
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Sridhar S, Clayton RH. Fibroblast mediated dynamics in diffusively uncoupled myocytes: a simulation study using 2-cell motifs. Sci Rep 2024; 14:4493. [PMID: 38396245 PMCID: PMC10891142 DOI: 10.1038/s41598-024-54564-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
In healthy hearts myocytes are typically coupled to nearest neighbours through gap junctions. Under pathological conditions such as fibrosis, or in scar tissue, or across ablation lines myocytes can uncouple from their neighbours. Electrical conduction may still occur via fibroblasts that not only couple proximal myocytes but can also couple otherwise unconnected regions. We hypothesise that such coupling can alter conduction between myocytes via introduction of delays or by initiation of premature stimuli that can potentially result in reentry or conduction blocks. To test this hypothesis we have developed several 2-cell motifs and investigated the effect of fibroblast mediated electrical coupling between uncoupled myocytes. We have identified various regimes of myocyte behaviour that depend on the strength of gap-junctional conductance, connection topology, and parameters of the myocyte and fibroblast models. These motifs are useful in developing a mechanistic understanding of long-distance coupling on myocyte dynamics and enable the characterisation of interaction between different features such as myocyte and fibroblast properties, coupling strengths and pacing period. They are computationally inexpensive and allow for incorporation of spatial effects such as conduction velocity. They provide a framework for constructing scar tissue boundaries and enable linking of cellular level interactions with scar induced arrhythmia.
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Affiliation(s)
- S Sridhar
- Department of Computer Science, University of Sheffield, Sheffield, UK.
| | - Richard H Clayton
- Department of Computer Science, University of Sheffield, Sheffield, UK
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17
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Fumagalli I, Pagani S, Vergara C, Dede’ L, Adebo DA, Del Greco M, Frontera A, Luciani GB, Pontone G, Scrofani R, Quarteroni A. The role of computational methods in cardiovascular medicine: a narrative review. Transl Pediatr 2024; 13:146-163. [PMID: 38323181 PMCID: PMC10839285 DOI: 10.21037/tp-23-184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 12/13/2023] [Indexed: 02/08/2024] Open
Abstract
Background and Objective Computational models of the cardiovascular system allow for a detailed and quantitative investigation of both physiological and pathological conditions, thanks to their ability to combine clinical-possibly patient-specific-data with physical knowledge of the processes underlying the heart function. These models have been increasingly employed in clinical practice to understand pathological mechanisms and their progression, design medical devices, support clinicians in improving therapies. Hinging upon a long-year experience in cardiovascular modeling, we have recently constructed a computational multi-physics and multi-scale integrated model of the heart for the investigation of its physiological function, the analysis of pathological conditions, and to support clinicians in both diagnosis and treatment planning. This narrative review aims to systematically discuss the role that such model had in addressing specific clinical questions, and how further impact of computational models on clinical practice are envisaged. Methods We developed computational models of the physical processes encompassed by the heart function (electrophysiology, electrical activation, force generation, mechanics, blood flow dynamics, valve dynamics, myocardial perfusion) and of their inherently strong coupling. To solve the equations of such models, we devised advanced numerical methods, implemented in a flexible and highly efficient software library. We also developed computational procedures for clinical data post-processing-like the reconstruction of the heart geometry and motion from diagnostic images-and for their integration into computational models. Key Content and Findings Our integrated computational model of the heart function provides non-invasive measures of indicators characterizing the heart function and dysfunctions, and sheds light on its underlying processes and their coupling. Moreover, thanks to the close collaboration with several clinical partners, we addressed specific clinical questions on pathological conditions, such as arrhythmias, ventricular dyssynchrony, hypertrophic cardiomyopathy, degeneration of prosthetic valves, and the way coronavirus disease 2019 (COVID-19) infection may affect the cardiac function. In multiple cases, we were also able to provide quantitative indications for treatment. Conclusions Computational models provide a quantitative and detailed tool to support clinicians in patient care, which can enhance the assessment of cardiac diseases, the prediction of the development of pathological conditions, and the planning of treatments and follow-up tests.
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Affiliation(s)
- Ivan Fumagalli
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Stefano Pagani
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Christian Vergara
- Laboratory of Biological Structures Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Milan, Italy
| | - Luca Dede’
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Dilachew A. Adebo
- Children’s Heart Institute, Hermann Children’s Hospital, University of Texas Health Science Center, McGovern Medical School, Houston, TX, USA
| | - Maurizio Del Greco
- Department of Cardiology, S. Maria del Carmine Hospital, Rovereto, Italy
| | - Antonio Frontera
- Electrophysiology Department, De Gasperis Cardio Center, ASST Great Metropolitan Hospital Niguarda, Milan, Italy
| | | | - Gianluca Pontone
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCSS, Milan, Italy
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Roberto Scrofani
- Cardiovascular Department, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Alfio Quarteroni
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Switzerland
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18
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Bifulco SF, Boyle PM. Computational Modeling and Simulation of the Fibrotic Human Atria. Methods Mol Biol 2024; 2735:105-115. [PMID: 38038845 DOI: 10.1007/978-1-0716-3527-8_6] [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] [Indexed: 12/02/2023]
Abstract
Patient-specific modeling of atrial electrical activity enables the execution of simulations that can provide mechanistic insights and provide novel solutions to vexing clinical problems. The geometry and fibrotic remodeling of the heart can be reconstructed from clinical-grade medical scans and used to inform personalized models with detail incorporated at the cell- and tissue-scale to represent changes in image-identified diseased regions. Here, we provide a rubric for the reconstruction of realistic atrial models from pre-segmented 3D renderings of the left atrium with fibrotic tissue regions delineated, which are the output from clinical-grade systems for quantifying fibrosis. We then provide a roadmap for using those models to carry out patient-specific characterization of the fibrotic substrate in terms of its potential to harbor reentrant drivers via cardiac electrophysiology simulations.
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Affiliation(s)
- Savannah F Bifulco
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA.
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19
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Trayanova NA, Prakosa A. Up digital and personal: How heart digital twins can transform heart patient care. Heart Rhythm 2024; 21:89-99. [PMID: 37871809 PMCID: PMC10872898 DOI: 10.1016/j.hrthm.2023.10.019] [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: 09/06/2023] [Revised: 10/12/2023] [Accepted: 10/15/2023] [Indexed: 10/25/2023]
Abstract
Precision medicine is the vision of health care where therapy is tailored to each patient. As part of this vision, digital twinning technology promises to deliver a digital representation of organs or even patients by using tools capable of simulating personal health conditions and predicting patient or disease trajectories on the basis of relationships learned both from data and from biophysics knowledge. Such virtual replicas would update themselves with data from monitoring devices and medical tests and assessments, reflecting dynamically the changes in our health conditions and the responses to treatment. In precision cardiology, the concepts and initial applications of heart digital twins have slowly been gaining popularity and the trust of the clinical community. In this article, we review the advancement in heart digital twinning and its initial translation to the management of heart rhythm disorders.
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Affiliation(s)
- Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland.
| | - Adityo Prakosa
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland
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20
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Song E. Impact of noise on the instability of spiral waves in stochastic 2D mathematical models of human atrial fibrillation. J Biol Phys 2023; 49:521-533. [PMID: 37792115 PMCID: PMC10651617 DOI: 10.1007/s10867-023-09644-0] [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: 08/02/2023] [Accepted: 09/08/2023] [Indexed: 10/05/2023] Open
Abstract
Sustained spiral waves, also known as rotors, are pivotal mechanisms in persistent atrial fibrillation (AF). Stochasticity is inevitable in nonlinear biological systems such as the heart; however, it is unclear how noise affects the instability of spiral waves in human AF. This study presents a stochastic two-dimensional mathematical model of human AF and explores how Gaussian white noise affects the instability of spiral waves. In homogeneous tissue models, Gaussian white noise may lead to spiral-wave meandering and wavefront break-up. As the noise intensity increases, the spatial dispersion of phase singularity (PS) points increases. This finding indicates the potential AF-protective effects of cardiac system stochasticity by destabilizing the rotors. By contrast, Gaussian white noise is unlikely to affect the spiral-wave instability in the presence of localized scar or fibrosis regions. The PS points are located at the boundary or inside the scar/fibrosis regions. Localized scar or fibrosis may play a pivotal role in stabilizing spiral waves regardless of the presence of noise. This study suggests that fibrosis and scars are essential for stabilizing the rotors in stochastic mathematical models of AF. Further patient-derived realistic modeling studies are required to confirm the role of scar/fibrosis in AF pathophysiology.
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Affiliation(s)
- Euijun Song
- Yonsei University College of Medicine, Seoul, Republic of Korea.
- , Gyeonggi, Republic of Korea.
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21
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Africa PC, Piersanti R, Regazzoni F, Bucelli M, Salvador M, Fedele M, Pagani S, Dede' L, Quarteroni A. lifex-ep: a robust and efficient software for cardiac electrophysiology simulations. BMC Bioinformatics 2023; 24:389. [PMID: 37828428 PMCID: PMC10571323 DOI: 10.1186/s12859-023-05513-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/02/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community. RESULTS This work introduces [Formula: see text]-ep, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. [Formula: see text]-ep employs the monodomain equation to model the heart's electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, [Formula: see text]-ep integrates the generation of myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within [Formula: see text]-fiber. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying [Formula: see text]-ep, along with comprehensive implementation details and instructions for users. [Formula: see text]-ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of [Formula: see text]-ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files. CONCLUSIONS [Formula: see text]-ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. [Formula: see text]-ep represents a valuable tool for conducting in silico patient-specific simulations.
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Affiliation(s)
- Pasquale Claudio Africa
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
- mathLab, Mathematics Area, SISSA International School for Advanced Studies, Trieste, Italy
| | - Roberto Piersanti
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy.
| | | | - Michele Bucelli
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Matteo Salvador
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, USA
| | - Marco Fedele
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Stefano Pagani
- 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, Professor emeritus, Switzerland
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Macheret F, Bifulco SF, Scott GD, Kwan KT, Chahine Y, Afroze T, McDonagh R, Akoum N, Boyle PM. Comparing Inducibility of Re-Entrant Arrhythmia in Patient-Specific Computational Models to Clinical Atrial Fibrillation Phenotypes. JACC Clin Electrophysiol 2023; 9:2149-2162. [PMID: 37656099 PMCID: PMC10909381 DOI: 10.1016/j.jacep.2023.06.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 06/21/2023] [Accepted: 06/30/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND Computational models of fibrosis-mediated, re-entrant left atrial (LA) arrhythmia can identify possible substrate for persistent atrial fibrillation (AF) ablation. Contemporary models use a 1-size-fits-all approach to represent electrophysiological properties, limiting agreement between simulations and patient outcomes. OBJECTIVES The goal of this study was to test the hypothesis that conduction velocity (ϴ) modulation in persistent AF models can improve simulation agreement with clinical arrhythmias. METHODS Patients with persistent AF (n = 37) underwent ablation and were followed up for ≥2 years to determine post-ablation outcomes: AF, atrial flutter (AFL), or no recurrence. Patient-specific LA models (n = 74) were constructed using pre-ablation and ≥90 days' post-ablation magnetic resonance imaging data. Simulated pacing gauged in silico arrhythmia inducibility due to AF-like rotors or AFL-like macro re-entrant tachycardias. A physiologically plausible range of ϴ values (±10 or 20% vs. baseline) was tested, and model/clinical agreement was assessed. RESULTS Fifteen (41%) patients had a recurrence with AF and 6 (16%) with AFL. Arrhythmia was induced in 1,078 of 5,550 simulations. Using baseline ϴ, model/clinical agreement was 46% (34 of 74 models), improving to 65% (48 of 74) when any possible ϴ value was used (McNemar's test, P = 0.014). ϴ modulation improved model/clinical agreement in both pre-ablation and post-ablation models. Pre-ablation model/clinical agreement was significantly greater for patients with extensive LA fibrosis (>17.2%) and an elevated body mass index (>32.0 kg/m2). CONCLUSIONS Simulations in persistent AF models show a 41% relative improvement in model/clinical agreement when ϴ is modulated. Patient-specific calibration of ϴ values could improve model/clinical agreement and model usefulness, especially in patients with higher body mass index or LA fibrosis burden. This could ultimately facilitate better personalized modeling, with immediate clinical implications.
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Affiliation(s)
- Fima Macheret
- Division of Cardiology, University of Washington, Seattle, Washington, USA
| | - Savannah F Bifulco
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Griffin D Scott
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Kirsten T Kwan
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Yaacoub Chahine
- Division of Cardiology, University of Washington, Seattle, Washington, USA
| | - Tanzina Afroze
- Division of Cardiology, University of Washington, Seattle, Washington, USA
| | | | - Nazem Akoum
- Division of Cardiology, University of Washington, Seattle, Washington, USA; Department of Bioengineering, University of Washington, Seattle, Washington, USA.
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, Washington, USA; Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, Washington, USA; Center for Cardiovascular Biology, University of Washington, Seattle, Washington, USA.
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Azzolin L, Eichenlaub M, Nagel C, Nairn D, Sánchez J, Unger L, Arentz T, Westermann D, Dössel O, Jadidi A, Loewe A. AugmentA: Patient-specific augmented atrial model generation tool. Comput Med Imaging Graph 2023; 108:102265. [PMID: 37392493 DOI: 10.1016/j.compmedimag.2023.102265] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 01/07/2023] [Accepted: 06/03/2023] [Indexed: 07/03/2023]
Abstract
Digital twins of patients' hearts are a promising tool to assess arrhythmia vulnerability and to personalize therapy. However, the process of building personalized computational models can be challenging and requires a high level of human interaction. We propose a patient-specific Augmented Atria generation pipeline (AugmentA) as a highly automated framework which, starting from clinical geometrical data, provides ready-to-use atrial personalized computational models. AugmentA identifies and labels atrial orifices using only one reference point per atrium. If the user chooses to fit a statistical shape model to the input geometry, it is first rigidly aligned with the given mean shape before a non-rigid fitting procedure is applied. AugmentA automatically generates the fiber orientation and finds local conduction velocities by minimizing the error between the simulated and clinical local activation time (LAT) map. The pipeline was tested on a cohort of 29 patients on both segmented magnetic resonance images (MRI) and electroanatomical maps of the left atrium. Moreover, the pipeline was applied to a bi-atrial volumetric mesh derived from MRI. The pipeline robustly integrated fiber orientation and anatomical region annotations in 38.4 ± 5.7 s. In conclusion, AugmentA offers an automated and comprehensive pipeline delivering atrial digital twins from clinical data in procedural time.
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Affiliation(s)
- Luca Azzolin
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany.
| | - Martin Eichenlaub
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Claudia Nagel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Deborah Nairn
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Jorge Sánchez
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Laura Unger
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Thomas Arentz
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Dirk Westermann
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Amir Jadidi
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
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Dasí A, Pope MT, Wijesurendra RS, Betts TR, Sachetto R, Bueno‐Orovio A, Rodriguez B. What determines the optimal pharmacological treatment of atrial fibrillation? Insights from in silico trials in 800 virtual atria. J Physiol 2023; 601:4013-4032. [PMID: 37475475 PMCID: PMC10952228 DOI: 10.1113/jp284730] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/05/2023] [Indexed: 07/22/2023] Open
Abstract
The best pharmacological treatment for each atrial fibrillation (AF) patient is unclear. We aim to exploit AF simulations in 800 virtual atria to identify key patient characteristics that guide the optimal selection of anti-arrhythmic drugs. The virtual cohort considered variability in electrophysiology and low voltage areas (LVA) and was developed and validated against experimental and clinical data from ionic currents to ECG. AF sustained in 494 (62%) atria, with large inward rectifier K+ current (IK1 ) and Na+ /K+ pump (INaK ) densities (IK1 0.11 ± 0.03 vs. 0.07 ± 0.03 S mF-1 ; INaK 0.68 ± 0.15 vs. 0.38 ± 26 S mF-1 ; sustained vs. un-sustained AF). In severely remodelled left atrium, with LVA extensions of more than 40% in the posterior wall, higher IK1 (median density 0.12 ± 0.02 S mF-1 ) was required for AF maintenance, and rotors localized in healthy right atrium. For lower LVA extensions, rotors could also anchor to LVA, in atria presenting short refractoriness (median L-type Ca2+ current, ICaL , density 0.08 ± 0.03 S mF-1 ). This atrial refractoriness, modulated by ICaL and fast Na+ current (INa ), determined pharmacological treatment success for both small and large LVA. Vernakalant was effective in atria presenting long refractoriness (median ICaL density 0.13 ± 0.05 S mF-1 ). For short refractoriness, atria with high INa (median density 8.92 ± 2.59 S mF-1 ) responded more favourably to amiodarone than flecainide, and the opposite was found in atria with low INa (median density 5.33 ± 1.41 S mF-1 ). In silico drug trials in 800 human atria identify inward currents as critical for optimal stratification of AF patient to pharmacological treatment and, together with the left atrial LVA extension, for accurately phenotyping AF dynamics. KEY POINTS: Atrial fibrillation (AF) maintenance is facilitated by small L-type Ca2+ current (ICaL ) and large inward rectifier K+ current (IK1 ) and Na+ /K+ pump. In severely remodelled left atrium, with low voltage areas (LVA) covering more than 40% of the posterior wall, sustained AF requires higher IK1 and rotors localize in healthy right atrium. For lower LVA extensions, rotors can also anchor to LVA, if the atria present short refractoriness (low ICaL ) Vernakalant is effective in atria presenting long refractoriness (high ICaL ). For short refractoriness, atria with fast Na+ current (INa ) up-regulation respond more favourably to amiodarone than flecainide, and the opposite is found in atria with low INa . The inward currents (ICaL and INa ) are critical for optimal stratification of AF patient to pharmacological treatment and, together with the left atrial LVA extension, for accurately phenotyping AF dynamics.
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Affiliation(s)
- Albert Dasí
- Department of Computer ScienceUniversity of OxfordOxfordUK
| | - Michael T.B. Pope
- Department of CardiologyOxford University Hospitals NHS Foundation TrustOxfordUK
- Department for Human Development and HealthUniversity of SouthamptonSouthamptonUK
| | - Rohan S. Wijesurendra
- Department of CardiologyOxford University Hospitals NHS Foundation TrustOxfordUK
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
| | - Tim R. Betts
- Department of CardiologyOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Rafael Sachetto
- Departamento de Ciência da ComputaçãoUniversidade Federal de São João del‐ReiSão João del‐ReiBrazil
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25
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Bifulco SF, Macheret F, Scott GD, Akoum N, Boyle PM. Explainable Machine Learning to Predict Anchored Reentry Substrate Created by Persistent Atrial Fibrillation Ablation in Computational Models. J Am Heart Assoc 2023; 12:e030500. [PMID: 37581387 PMCID: PMC10492949 DOI: 10.1161/jaha.123.030500] [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: 04/16/2023] [Accepted: 07/21/2023] [Indexed: 08/16/2023]
Abstract
Background Postablation arrhythmia recurrence occurs in ~40% of patients with persistent atrial fibrillation. Fibrotic remodeling exacerbates arrhythmic activity in persistent atrial fibrillation and can play a key role in reentrant arrhythmia, but emergent interaction between nonconductive ablation-induced scar and native fibrosis (ie, residual fibrosis) is poorly understood. Methods and Results We conducted computational simulations in pre- and postablation left atrial models reconstructed from late gadolinium enhanced magnetic resonance imaging scans to test the hypothesis that ablation in patients with persistent atrial fibrillation creates new substrate conducive to recurrent arrhythmia mediated by anchored reentry. We trained a random forest machine learning classifier to accurately pinpoint specific nonconductive tissue regions (ie, areas of ablation-delivered scar or vein/valve boundaries) with the capacity to serve as substrate for anchored reentry-driven recurrent arrhythmia (area under the curve: 0.91±0.03). Our analysis suggests there is a distinctive nonconductive tissue pattern prone to serving as arrhythmogenic substrate in postablation models, defined by a specific size and proximity to residual fibrosis. Conclusions Overall, this suggests persistent atrial fibrillation ablation transforms substrate that favors functional reentry (ie, rotors meandering in excitable tissue) into an arrhythmogenic milieu more conducive to anchored reentry. Our work also indicates that explainable machine learning and computational simulations can be combined to effectively probe mechanisms of recurrent arrhythmia.
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Affiliation(s)
| | - Fima Macheret
- Division of CardiologyUniversity of WashingtonSeattleWAUSA
| | - Griffin D. Scott
- Department of BioengineeringUniversity of WashingtonSeattleWAUSA
| | - Nazem Akoum
- Department of BioengineeringUniversity of WashingtonSeattleWAUSA
- Division of CardiologyUniversity of WashingtonSeattleWAUSA
| | - Patrick M. Boyle
- Department of BioengineeringUniversity of WashingtonSeattleWAUSA
- Institute for Stem Cell and Regenerative MedicineUniversity of WashingtonSeattleWAUSA
- Center for Cardiovascular BiologyUniversity of WashingtonSeattleWAUSA
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26
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Halfar R, Lawson BAJ, Dos Santos RW, Burrage K. Recurrence quantification analysis for fine-scale characterisation of arrhythmic patterns in cardiac tissue. Sci Rep 2023; 13:11828. [PMID: 37481668 PMCID: PMC10363137 DOI: 10.1038/s41598-023-38256-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 07/05/2023] [Indexed: 07/24/2023] Open
Abstract
This paper uses recurrence quantification analysis (RQA) combined with entropy measures and organization indices to characterize arrhythmic patterns and dynamics in computer simulations of cardiac tissue. We performed different simulations of cardiac tissues of sizes comparable to the human heart atrium. In these simulations, we observed four classic arrhythmic patterns: a spiral wave anchored to a highly fibrotic region resulting in sustained re-entry, a meandering spiral wave, fibrillation, and a spiral wave anchored to a scar region that breaks up into wavelets away from the main rotor. A detailed analysis revealed that, within the same simulation, maps of RQA metrics could differentiate regions with regular AP propagation from ones with chaotic activity. In particular, the combination of two RQA metrics, the length of the longest diagonal string of recurrence points and the mean length of diagonal lines, was able to identify the location of rotor tips, which are the active elements that maintain spiral waves and fibrillation. By proposing low-dimensional models based on the mean value and spatial correlation of metrics calculated from membrane potential time series, we identify RQA-based metrics that successfully separate the four different types of cardiac arrhythmia into distinct regions of the feature space, and thus might be used for automatic classification, in particular distinguishing between fibrillation driven by self-sustaining chaos and that created by a persistent rotor and wavebreak. We also discuss the practical applicability of such an approach.
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Affiliation(s)
- Radek Halfar
- IT4Innovations, VSB - Technical University of Ostrava, 708 00, Ostrava, Czech Republic.
| | - Brodie A J Lawson
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, Queensland University of Technology, Brisbane, 4000, Australia
- Centre for Data Science, Queensland Univeristy of Technology, Brisbane, 4000, Australia
| | - Rodrigo Weber Dos Santos
- Graduate Program in Computational Modeling, Universidade Federal de Juiz de Fora, Juiz de Fora, 36036-330, Brazil
| | - Kevin Burrage
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, Queensland University of Technology, Brisbane, 4000, Australia
- Department of Computer Science, University of Oxford, Oxford, UK
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27
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Telle Å, Bargellini C, Chahine Y, Del Álamo JC, Akoum N, Boyle PM. Personalized biomechanical insights in atrial fibrillation: opportunities & challenges. Expert Rev Cardiovasc Ther 2023; 21:817-837. [PMID: 37878350 PMCID: PMC10841537 DOI: 10.1080/14779072.2023.2273896] [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: 08/12/2023] [Accepted: 10/18/2023] [Indexed: 10/26/2023]
Abstract
INTRODUCTION Atrial fibrillation (AF) is an increasingly prevalent and significant worldwide health problem. Manifested as an irregular atrial electrophysiological activation, it is associated with many serious health complications. AF affects the biomechanical function of the heart as contraction follows the electrical activation, subsequently leading to reduced blood flow. The underlying mechanisms behind AF are not fully understood, but it is known that AF is highly correlated with the presence of atrial fibrosis, and with a manifold increase in risk of stroke. AREAS COVERED In this review, we focus on biomechanical aspects in atrial fibrillation, current and emerging use of clinical images, and personalized computational models. We also discuss how these can be used to provide patient-specific care. EXPERT OPINION Understanding the connection betweenatrial fibrillation and atrial remodeling might lead to valuable understanding of stroke and heart failure pathophysiology. Established and emerging imaging modalities can bring us closer to this understanding, especially with continued advancements in processing accuracy, reproducibility, and clinical relevance of the associated technologies. Computational models of cardiac electromechanics can be used to glean additional insights on the roles of AF and remodeling in heart function.
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Affiliation(s)
- Åshild Telle
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Clarissa Bargellini
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Yaacoub Chahine
- Division of Cardiology, University of Washington, Seattle, WA, USA
| | - Juan C Del Álamo
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
| | - Nazem Akoum
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Division of Cardiology, University of Washington, Seattle, WA, USA
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
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Chahine Y, Magoon MJ, Maidu B, del Álamo JC, Boyle PM, Akoum N. Machine Learning and the Conundrum of Stroke Risk Prediction. Arrhythm Electrophysiol Rev 2023; 12:e07. [PMID: 37427297 PMCID: PMC10326666 DOI: 10.15420/aer.2022.34] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 02/07/2023] [Indexed: 07/11/2023] Open
Abstract
Stroke is a leading cause of death worldwide. With escalating healthcare costs, early non-invasive stroke risk stratification is vital. The current paradigm of stroke risk assessment and mitigation is focused on clinical risk factors and comorbidities. Standard algorithms predict risk using regression-based statistical associations, which, while useful and easy to use, have moderate predictive accuracy. This review summarises recent efforts to deploy machine learning (ML) to predict stroke risk and enrich the understanding of the mechanisms underlying stroke. The surveyed body of literature includes studies comparing ML algorithms with conventional statistical models for predicting cardiovascular disease and, in particular, different stroke subtypes. Another avenue of research explored is ML as a means of enriching multiscale computational modelling, which holds great promise for revealing thrombogenesis mechanisms. Overall, ML offers a new approach to stroke risk stratification that accounts for subtle physiologic variants between patients, potentially leading to more reliable and personalised predictions than standard regression-based statistical associations.
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Affiliation(s)
- Yaacoub Chahine
- Division of Cardiology, University of Washington, Seattle, WA, US
| | - Matthew J Magoon
- Department of Bioengineering, University of Washington, Seattle, WA, US
| | - Bahetihazi Maidu
- Department of Mechanical Engineering, University of Washington, Seattle, WA, US
| | - Juan C del Álamo
- Department of Mechanical Engineering, University of Washington, Seattle, WA, US
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, US
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, US
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, WA, US
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, US
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, US
| | - Nazem Akoum
- Division of Cardiology, University of Washington, Seattle, WA, US
- Department of Bioengineering, University of Washington, Seattle, WA, US
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29
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Li L, Hua C, Liu X, Wang Y, Zhao L, Zhang Y, Wang L, Su P, Yang MF, Xie B. SGLT2i alleviates epicardial adipose tissue inflammation by modulating ketone body-glyceraldehyde-3-phosphate dehydrogenase malonylation pathway. J Cardiovasc Med (Hagerstown) 2023; 24:232-243. [PMID: 36938811 DOI: 10.2459/jcm.0000000000001453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2023]
Abstract
AIMS Inflammation in the epicardial adipose tissue (EAT) is a contributor to atrial fibrillation. Studies have reported that sodium glucose co-transporter 2 inhibitor (SGLT2i) can alleviate EAT inflammation. However, the mechanism remains elusive. This study aims to investigate the molecular mechanism of SGLT2i in reducing EAT inflammation and to explore the effects of SGLT2i on atrial fibrosis in atrial fibrillation. METHODS Sprague-Dawley rats were injected with angiotensin II to induce atrial fibrillation and randomly assigned to receive SGLT2i ( n = 6) or vehicle ( n = 6). Macrophages (RAW264.7) were treated with ketone bodies; ACC1 knockdown/overexpression and malonyl-CoA overexpression were performed in vitro . The levels of inflammatory cytokines, ACC1, and malonyl-CoA were examined by ELISA. GAPDH malonylation was measured by co-immunoprecipitation. RESULTS In atrial fibrillation rats, SGLT2i increased the ketone body levels and decreased the expression of ACC1 and alleviated EAT inflammation and atrial fibrosis. In RAW264.7 cells, ketone bodies decreased the levels of ACC1, malonyl-CoA, and GAPDH malonylation, accompanied by reduced inflammatory cytokines. ACC1 knockdown decreased the expression of malonyl-CoA and GAPDH malonylation and alleviated lipopolysaccharide (LPS)-induced macrophage inflammation; these effects were inhibited by malonyl-CoA overexpression. Furthermore, the protective effects of ketone bodies on macrophage inflammation were abrogated by ACC1 overexpression. CONCLUSION SGLT2i alleviates EAT inflammation by reducing GAPDH malonylation via downregulating the expression of ACC1 through increasing ketone bodies, thus attenuating atrial fibrosis.
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Affiliation(s)
- Lina Li
- Department of Nuclear Medicine
| | - Cuncun Hua
- Cardiac Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Xiaoyan Liu
- Cardiac Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Yidan Wang
- Cardiac Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Lei Zhao
- Cardiac Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Yeping Zhang
- Cardiac Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Li Wang
- Department of Nuclear Medicine
| | - Pixiong Su
- Cardiac Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | | | - Boqia Xie
- Cardiac Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
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Desantis V, Potenza MA, Sgarra L, Nacci C, Scaringella A, Cicco S, Solimando AG, Vacca A, Montagnani M. microRNAs as Biomarkers of Endothelial Dysfunction and Therapeutic Target in the Pathogenesis of Atrial Fibrillation. Int J Mol Sci 2023; 24:ijms24065307. [PMID: 36982382 PMCID: PMC10049145 DOI: 10.3390/ijms24065307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 03/12/2023] Open
Abstract
The pathophysiology of atrial fibrillation (AF) may involve atrial fibrosis/remodeling and dysfunctional endothelial activities. Despite the currently available treatment approaches, the progression of AF, its recurrence rate, and the high mortality risk of related complications underlay the need for more advanced prognostic and therapeutic strategies. There is increasing attention on the molecular mechanisms controlling AF onset and progression points to the complex cell to cell interplay that triggers fibroblasts, immune cells and myofibroblasts, enhancing atrial fibrosis. In this scenario, endothelial cell dysfunction (ED) might play an unexpected but significant role. microRNAs (miRNAs) regulate gene expression at the post-transcriptional level. In the cardiovascular compartment, both free circulating and exosomal miRNAs entail the control of plaque formation, lipid metabolism, inflammation and angiogenesis, cardiomyocyte growth and contractility, and even the maintenance of cardiac rhythm. Abnormal miRNAs levels may indicate the activation state of circulating cells, and thus represent a specific read-out of cardiac tissue changes. Although several unresolved questions still limit their clinical use, the ease of accessibility in biofluids and their prognostic and diagnostic properties make them novel and attractive biomarker candidates in AF. This article summarizes the most recent features of AF associated with miRNAs and relates them to potentially underlying mechanisms.
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Affiliation(s)
- Vanessa Desantis
- Department of Precision and Regenerative Medicine and Ionian Area, Pharmacology Section, University of Bari Aldo Moro Medical School, 70124 Bari, Italy
- Correspondence: (V.D.); (M.A.P.)
| | - Maria Assunta Potenza
- Department of Precision and Regenerative Medicine and Ionian Area, Pharmacology Section, University of Bari Aldo Moro Medical School, 70124 Bari, Italy
- Correspondence: (V.D.); (M.A.P.)
| | - Luca Sgarra
- General Hospital “F. Miulli” Acquaviva delle Fonti, 70021 Bari, Italy
| | - Carmela Nacci
- Department of Precision and Regenerative Medicine and Ionian Area, Pharmacology Section, University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Antonietta Scaringella
- Department of Precision and Regenerative Medicine and Ionian Area, Pharmacology Section, University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Sebastiano Cicco
- Department of Precision and Regenerative Medicine and Ionian Area, Unit of Internal Medicine and Clinical Oncology, University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Antonio Giovanni Solimando
- Department of Precision and Regenerative Medicine and Ionian Area, Unit of Internal Medicine and Clinical Oncology, University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Angelo Vacca
- Department of Precision and Regenerative Medicine and Ionian Area, Unit of Internal Medicine and Clinical Oncology, University of Bari Aldo Moro Medical School, 70124 Bari, Italy
| | - Monica Montagnani
- Department of Precision and Regenerative Medicine and Ionian Area, Pharmacology Section, University of Bari Aldo Moro Medical School, 70124 Bari, Italy
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Deng CY, Zou AL, Sun L, Ji Y. Development and Validation of a Postoperative Prognostic Nomogram to Predict Recurrence in Patients with Persistent Atrial Fibrillation: A Retrospective Cohort Study. CARDIOVASCULAR INNOVATIONS AND APPLICATIONS 2023. [DOI: 10.15212/cvia.2023.0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2023] Open
Abstract
Background: Patients with persistent atrial fibrillation (PsAF) have a high risk of recurrence after catheter radiofrequency ablation. Nevertheless, no effective prognostic tools have been developed to identify these high-risk patients to date. This study sought to develop and validate a simple linear predictive model for predicting postoperative recurrence in patients with PsAF.
Methods: From June 2013 to June 2021, patients with PsAF admitted to our hospital were enrolled in this single-center, retrospective, observational study. The characteristics substantially associated with recurrence in patients with PsAF were screened through univariate and multivariate logistic regression analysis. The receiver operating characteristic curve was used to assess the predictive significance of the nomogram model after nomogram development. Furthermore, to assess the clinical value of the nomogram, we performed calibration curve and decision curve analyses.
Results: A total of 209 patients were included in the study, 42 (20.10%) of whom were monitored up to 1 year for recurrent AF. The duration of AF episodes, left atrial diameter, BMI, CKMB, and alcohol consumption were found to be independent risk factors (P<0.05) and were integrated into the nomogram model development. The area under the curve was 0.895, the sensitivity was 93.3%, and the specificity was 71.4%, thus indicating the model’s excellent predictive ability. The C-index of the predictive nomogram model was 0.906. Calibration curve and decision curve analyses further revealed that the model had robust prediction and strong discrimination ability.
Conclusion: This simple, practical, and innovative nomogram can help clinicians in evaluation of the risk of PsAF recurrence after catheter ablation, thus facilitating preoperative evaluation, postoperative monitoring and ultimately the construction of more personalized therapeutic protocols.
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Affiliation(s)
- Cong-Ying Deng
- Department of Cardiovascular Medicine, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu, China
| | - Ai-Lin Zou
- Department of Cardiovascular Medicine, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu, China
| | - Ling Sun
- Department of Cardiovascular Medicine, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu, China
| | - Yuan Ji
- Department of Cardiovascular Medicine, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu, China
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Kamali R, Gillete K, Tate J, Abhyankar DA, Dosdall DJ, Plank G, Bunch TJ, Macleod RS, Ranjan R. Treatment Planning for Atrial Fibrillation Using Patient-Specific Models Showing the Importance of Fibrillatory-Areas. Ann Biomed Eng 2023; 51:329-342. [PMID: 35930093 PMCID: PMC10440744 DOI: 10.1007/s10439-022-03029-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 07/18/2022] [Indexed: 01/25/2023]
Abstract
Computational models have made it possible to study the effect of fibrosis and scar on atrial fibrillation (AF) and plan future personalized treatments. Here, we study the effect of area available for fibrillatory waves to sustain AF. Then we use it to plan for AF ablation to improve procedural outcomes. CARPentry was used to create patient-specific models to determine the association between the size of residual contiguous areas available for AF wavefronts to propagate and sustain AF [fibrillatory area (FA)] after ablation with procedural outcomes. The FA was quantified in a novel manner accounting for gaps in ablation lines. We selected 30 persistent AF patients with known ablation outcomes. We divided the atrial surface into five areas based on ablation scar pattern and anatomical landmarks and calculated the FAs. We validated the models based on clinical outcomes and suggested future ablation lines that minimize the FAs and terminate rotor activities in simulations. We also simulated the effects of three common antiarrhythmic drugs. In the patient-specific models, the predicted arrhythmias matched the clinical outcomes in 25 of 30 patients (accuracy 83.33%). The average largest FA (FAmax) in the recurrence group was 8517 ± 1444 vs. 6772 ± 1531 mm2 in the no recurrence group (p < 0.004). The final FAs after adding the suggested ablation lines in the AF recurrence group reduced the average FAmax from 8517 ± 1444 to 6168 ± 1358 mm2 (p < 0.001) and stopped the sustained rotor activity. Simulations also correctly anticipated the effect of antiarrhythmic drugs in 5 out of 6 patients who used drug therapy post unsuccessful ablation (accuracy 83.33%). Sizes of FAs available for AF wavefronts to propagate are important determinants for ablation outcomes. FA size in combination with computational simulations can be used to direct ablation in persistent AF to minimize the critical mass required to sustain recurrent AF.
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Affiliation(s)
- Roya Kamali
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Karli Gillete
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Jess Tate
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
| | | | - Derek J Dosdall
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - T Jared Bunch
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - Rob S Macleod
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Ravi Ranjan
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA.
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA.
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Corrado C, Roney CH, Razeghi O, Lemus JAS, Coveney S, Sim I, Williams SE, O'Neill MD, Wilkinson RD, Clayton RH, Niederer SA. Quantifying the impact of shape uncertainty on predicted arrhythmias. Comput Biol Med 2023; 153:106528. [PMID: 36634600 DOI: 10.1016/j.compbiomed.2022.106528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 12/15/2022] [Accepted: 12/31/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Personalised computer models are increasingly used to diagnose cardiac arrhythmias and tailor treatment. Patient-specific models of the left atrium are often derived from pre-procedural imaging of anatomy and fibrosis. These images contain noise that can affect simulation predictions. There are few computationally tractable methods for propagating uncertainties from images to clinical predictions. METHOD We describe the left atrium anatomy using our Bayesian shape model that captures anatomical uncertainty in medical images and has been validated on 63 independent clinical images. This algorithm describes the left atrium anatomy using Nmodes=15 principal components, capturing 95% of the shape variance and calculated from 70 clinical cardiac magnetic resonance (CMR) images. Latent variables encode shape uncertainty: we evaluate their posterior distribution for each new anatomy. We assume a normally distributed prior. We use the unscented transform to sample from the posterior shape distribution. For each sample, we assign the local material properties of the tissue using the projection of late gadolinium enhancement CMR (LGE-CMR) onto the anatomy to estimate local fibrosis. To test which activation patterns an atrium can sustain, we perform an arrhythmia simulation for each sample. We consider 34 possible outcomes (31 macro-re-entries, functional re-entry, atrial fibrillation, and non-sustained arrhythmia). For each sample, we determine the outcome by comparing pre- and post-ablation activation patterns following a cross-field stimulus. RESULTS We create patient-specific atrial electrophysiology models of ten patients. We validate the mean and standard deviation maps from the unscented transform with the same statistics obtained with 12,000 Monte Carlo (ground truth) samples. We found discrepancies <3% and <2% for the mean and standard deviation for fibrosis burden and activation time, respectively. For each patient case, we then compare the predicted outcome from a model built on the clinical data (deterministic approach) with the probability distribution obtained from the simulated samples. We found that the deterministic approach did not predict the most likely outcome in 80% of the cases. Finally, we estimate the influence of each source of uncertainty independently. Fixing the anatomy to the posterior mean and maintaining uncertainty in fibrosis reduced the prediction of self-terminating arrhythmias from ≃14% to ≃7%. Keeping the fibrosis fixed to the sample mean while retaining uncertainty in shape decreased the prediction of substrate-driven arrhythmias from ≃33% to ≃18% and increased the prediction of macro-re-entries from ≃54% to ≃68%. CONCLUSIONS We presented a novel method for propagating shape uncertainty in atrial models through to uncertainty in numerical simulations. The algorithm takes advantage of the unscented transform to compute the output distribution of the outcomes. We validated the unscented transform as a viable sampling strategy to deal with anatomy uncertainty. We then showed that the prediction computed with a deterministic model does not always coincide with the most likely outcome. Finally, we found that shape uncertainty affects the predictions of macro-re-entries, while fibrosis uncertainty affects the predictions of functional re-entries.
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Affiliation(s)
- Cesare Corrado
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom.
| | - Caroline H Roney
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom; School of Engineering and Materials Science, Queen Mary University of London, London, United Kingdom
| | - Orod Razeghi
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom; UCL Centre for Advanced Research Computing, London, United Kingdom
| | - Josè Alonso Solís Lemus
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Sam Coveney
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Iain Sim
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Steven E Williams
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Mark D O'Neill
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Richard D Wilkinson
- School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Richard H Clayton
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Steven A Niederer
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
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Kamali R, Kwan E, Regouski M, Bunch TJ, Dosdall DJ, Hsu E, Macleod RS, Polejaeva I, Ranjan R. Contribution of atrial myofiber architecture to atrial fibrillation. PLoS One 2023; 18:e0279974. [PMID: 36719871 PMCID: PMC9888724 DOI: 10.1371/journal.pone.0279974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 12/19/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND The role of fiber orientation on a global chamber level in sustaining atrial fibrillation (AF) is unknown. The goal of this study was to correlate the fiber direction derived from Diffusion Tensor Imaging (DTI) with AF inducibility. METHODS Transgenic goats with cardiac-specific overexpression of constitutively active TGF-β1 (n = 14) underwent AF inducibility testing by rapid pacing in the left atrium. We chose a minimum of 10 minutes of sustained AF as a cut-off for AF inducibility. Explanted hearts underwent DTI to determine the fiber direction. Using tractography data, we clustered, visualized, and quantified the fiber helix angles in 8 different regions of the left atrial wall using two reference vectors defined based on anatomical landmarks. RESULTS Sustained AF was induced in 7 out of 14 goats. The mean helix fiber angles in 7 out of 8 selected regions were statistically different (P-Value < 0.05) in the AF inducible group. The average fractional anisotropy (FA) and the mean diffusivity (MD) were similar in the two groups with FA of 0.32±0.08 and MD of 8.54±1.72 mm2/s in the non-inducible group and FA of 0.31±0.05 (P-value = 0.90) and MD of 8.68±1.60 mm2/s (P-value = 0.88) in the inducible group. CONCLUSIONS DTI based fiber direction shows significant variability across subjects with a significant difference between animals that are AF inducible versus animals that are not inducible. Fiber direction might be contributing to the initiation and sustaining of AF, and its role needs to be investigated further.
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Affiliation(s)
- Roya Kamali
- Department of Bioengineering, University of Utah, Salt Lake City, Utah, United States of America
- Cardiovascular Medicine, University of Utah, Salt Lake City, Utah, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute, Salt Lake City, Utah, United States of America
| | - Eugene Kwan
- Department of Bioengineering, University of Utah, Salt Lake City, Utah, United States of America
- Cardiovascular Medicine, University of Utah, Salt Lake City, Utah, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute, Salt Lake City, Utah, United States of America
| | - Misha Regouski
- Department of Animal, Dairy and Veterinary Sciences, Utah State University, Logan, Utah, United States of America
| | - T. Jared Bunch
- Cardiovascular Medicine, University of Utah, Salt Lake City, Utah, United States of America
| | - Derek J. Dosdall
- Department of Bioengineering, University of Utah, Salt Lake City, Utah, United States of America
- Cardiovascular Medicine, University of Utah, Salt Lake City, Utah, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute, Salt Lake City, Utah, United States of America
- Department of Surgery, University of Utah, Salt Lake City, Utah, United States of America
| | - Ed Hsu
- Department of Bioengineering, University of Utah, Salt Lake City, Utah, United States of America
| | - Rob S. Macleod
- Department of Bioengineering, University of Utah, Salt Lake City, Utah, United States of America
| | - Irina Polejaeva
- Department of Animal, Dairy and Veterinary Sciences, Utah State University, Logan, Utah, United States of America
| | - Ravi Ranjan
- Department of Bioengineering, University of Utah, Salt Lake City, Utah, United States of America
- Cardiovascular Medicine, University of Utah, Salt Lake City, Utah, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute, Salt Lake City, Utah, United States of America
- * E-mail:
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Lawson BA, dos Santos RW, Turner IW, Bueno-Orovio A, Burrage P, Burrage K. Homogenisation for the monodomain model in the presence of microscopic fibrotic structures. COMMUNICATIONS IN NONLINEAR SCIENCE & NUMERICAL SIMULATION 2023; 116:None. [PMID: 37113591 PMCID: PMC10124103 DOI: 10.1016/j.cnsns.2022.106794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 05/06/2022] [Accepted: 08/04/2022] [Indexed: 06/08/2023]
Abstract
Computational models in cardiac electrophysiology are notorious for long runtimes, restricting the numbers of nodes and mesh elements in the numerical discretisations used for their solution. This makes it particularly challenging to incorporate structural heterogeneities on small spatial scales, preventing a full understanding of the critical arrhythmogenic effects of conditions such as cardiac fibrosis. In this work, we explore the technique of homogenisation by volume averaging for the inclusion of non-conductive micro-structures into larger-scale cardiac meshes with minor computational overhead. Importantly, our approach is not restricted to periodic patterns, enabling homogenised models to represent, for example, the intricate patterns of collagen deposition present in different types of fibrosis. We first highlight the importance of appropriate boundary condition choice for the closure problems that define the parameters of homogenised models. Then, we demonstrate the technique's ability to correctly upscale the effects of fibrotic patterns with a spatial resolution of 10 µm into much larger numerical mesh sizes of 100- 250 µm . The homogenised models using these coarser meshes correctly predict critical pro-arrhythmic effects of fibrosis, including slowed conduction, source/sink mismatch, and stabilisation of re-entrant activation patterns. As such, this approach to homogenisation represents a significant step towards whole organ simulations that unravel the effects of microscopic cardiac tissue heterogeneities.
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Affiliation(s)
- Brodie A.J. Lawson
- Centre for Data Science, Queensland University of Technology, 2 George Street, Brisbane, 4000, Queensland, Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, 2 George Street, Brisbane, 4000, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, 2 George Street, Brisbane, 4000, Queensland, Australia
| | - Rodrigo Weber dos Santos
- Graduate Program on Computational Modelling, Universidade de Federal de Juiz de Fora, Rua Jose Lourenco Kelmer s/n, Juiz de Fora, 36036-900, Minas Gerais, Brazil
| | - Ian W. Turner
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, 2 George Street, Brisbane, 4000, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, 2 George Street, Brisbane, 4000, Queensland, Australia
| | - Alfonso Bueno-Orovio
- Department of Computer Science, University of Oxford, Parks Rd, Oxford, OX1 3QD, Oxfordshire, United Kingdom
| | - Pamela Burrage
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, 2 George Street, Brisbane, 4000, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, 2 George Street, Brisbane, 4000, Queensland, Australia
| | - Kevin Burrage
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, 2 George Street, Brisbane, 4000, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, 2 George Street, Brisbane, 4000, Queensland, Australia
- Department of Computer Science, University of Oxford, Parks Rd, Oxford, OX1 3QD, Oxfordshire, United Kingdom
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Yamamoto C, Trayanova NA. Atrial fibrillation: Insights from animal models, computational modeling, and clinical studies. EBioMedicine 2022; 85:104310. [PMID: 36309006 PMCID: PMC9619190 DOI: 10.1016/j.ebiom.2022.104310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/25/2022] [Accepted: 10/04/2022] [Indexed: 11/11/2022] Open
Abstract
Atrial fibrillation (AF) is the most common human arrhythmia, affecting millions of patients worldwide. A combination of risk factors and comorbidities results in complex atrial remodeling, which increases AF vulnerability and persistence. Insights from animal models, clinical studies, and computational modeling have advanced the understanding of the mechanisms and pathophysiology of AF. Areas of heterogeneous pathological remodeling, as well as altered electrophysiological properties, serve as a substrate for AF drivers and spontaneous activations. The complex and individualized presentation of this arrhythmia suggests that mechanisms-based personalized approaches will likely be needed to overcome current challenges in AF management. In this paper, we review the insights on the mechanisms of AF obtained from animal models and clinical studies and how computational models integrate this knowledge to advance AF clinical management. We also assess the challenges that need to be overcome to implement these mechanistic models in clinical practice.
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Affiliation(s)
- Carolyna Yamamoto
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA,Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA,Corresponding author. Johns Hopkins, Johns Hopkins University, United States.
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Abstract
The global burden caused by cardiovascular disease is substantial, with heart disease representing the most common cause of death around the world. There remains a need to develop better mechanistic models of cardiac function in order to combat this health concern. Heart rhythm disorders, or arrhythmias, are one particular type of disease which has been amenable to quantitative investigation. Here we review the application of quantitative methodologies to explore dynamical questions pertaining to arrhythmias. We begin by describing single-cell models of cardiac myocytes, from which two and three dimensional models can be constructed. Special focus is placed on results relating to pattern formation across these spatially-distributed systems, especially the formation of spiral waves of activation. Next, we discuss mechanisms which can lead to the initiation of arrhythmias, focusing on the dynamical state of spatially discordant alternans, and outline proposed mechanisms perpetuating arrhythmias such as fibrillation. We then review experimental and clinical results related to the spatio-temporal mapping of heart rhythm disorders. Finally, we describe treatment options for heart rhythm disorders and demonstrate how statistical physics tools can provide insights into the dynamics of heart rhythm disorders.
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Affiliation(s)
- Wouter-Jan Rappel
- Department of Physics, University of California San Diego, La Jolla, CA 92037
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Interpretable machine learning of action potential duration restitution kinetics in single-cell models of atrial cardiomyocytes. J Electrocardiol 2022; 74:137-145. [PMID: 36223672 DOI: 10.1016/j.jelectrocard.2022.09.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/28/2022] [Accepted: 09/19/2022] [Indexed: 12/13/2022]
Abstract
Action potential duration (APD) restitution curve and its maximal slope (Smax) reflect single cell-level dynamic instability for inducing chaotic heart rhythms. However, conventional parameter sensitivity analysis often fails to describe nonlinear relationships between ion channel parameters and electrophysiological phenotypes, such as Smax. We explored the parameter-phenotype mapping in a population of 5000 single-cell atrial cell models through interpretable machine learning (ML) approaches. Parameter sensitivity analyses could explain the linear relationships between parameters and electrophysiological phenotypes, including APD90, resting membrane potential, Vmax, refractory period, and APD/calcium alternans threshold, but not for Smax. However, neural network models had better prediction performance for Smax. To interpret the ML model, we evaluated the parameter importance at the global and local levels by computing the permutation feature importance and the local interpretable model-agnostic explanations (LIME) values, respectively. Increases in ICaL, INCX, and IKr, and decreases in IK1, Ib,Cl, IKur, ISERCA, and Ito are correlated with higher Smax values. The LIME algorithm determined that INaK plays a significant role in determining Smax as well as Ito and IKur. The atrial cardiomyocyte population was hierarchically clustered into three distinct groups based on the LIME values and the single-cell simulation confirmed that perturbations in INaK resulted in different behaviors of APD restitution curves in three clusters. Our combined top-down interpretable ML and bottom-up mechanistic simulation approaches uncovered the role of INaK in heterogeneous behaviors of Smax in the atrial cardiomyocyte population.
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Martinez-Mateu L, Saiz J, Berenfeld O. Rotors Drift Toward and Stabilize in Low Power Regions in Heterogeneous Models of Atrial Fibrillation. COMPUTING IN CARDIOLOGY 2022; 49:10.22489/cinc.2022.366. [PMID: 37560510 PMCID: PMC10411388 DOI: 10.22489/cinc.2022.366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
Atrial fibrillation (AF) afflicts more than 33 million people worldwide. Success of therapy strategies remains poor and better understanding of the arrhythmia and how to device more effective therapies are needed. The aim of this work is to study the role of electric power distributions in rotors and AF dynamics. For this purpose, single cell and tissue simulations were performed to study the effect of ionic currents gradients and fibrosis in rotor's drifting. The root mean square of the ionic (Pion), capacitance (Pc) and electrotonic (Pele) power was computed over action potentials. Single cell simulations were performed for different values of IK1 and ICaL and number of coupled myofibroblasts. Tissue simulations were performed in presence of IK1 and ICaL gradients and diffused fibrosis. Single cell simulations showed that Pion and Pc increased with IK1, while decreased by increasing ICaL. Increasing the number of coupled myofibroblasts reduced Pion and Pc, whereas Pele increased. Finally, in tissue simulations rotors drifted to regions with low power and anchored in regions with higher density of blunted ionic induced power gradients.
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Affiliation(s)
| | - Javier Saiz
- Universitat Politècnica de València, Valencia, Spain
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40
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Bai J, Lu Y, Wang H, Zhao J. How synergy between mechanistic and statistical models is impacting research in atrial fibrillation. Front Physiol 2022; 13:957604. [PMID: 36111152 PMCID: PMC9468674 DOI: 10.3389/fphys.2022.957604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Atrial fibrillation (AF) with multiple complications, high morbidity and mortality, and low cure rates, has become a global public health problem. Although significant progress has been made in the treatment methods represented by anti-AF drugs and radiofrequency ablation, the therapeutic effect is not as good as expected. The reason is mainly because of our lack of understanding of AF mechanisms. This field has benefited from mechanistic and (or) statistical methodologies. Recent renewed interest in digital twin techniques by synergizing between mechanistic and statistical models has opened new frontiers in AF analysis. In the review, we briefly present findings that gave rise to the AF pathophysiology and current therapeutic modalities. We then summarize the achievements of digital twin technologies in three aspects: understanding AF mechanisms, screening anti-AF drugs and optimizing ablation strategies. Finally, we discuss the challenges that hinder the clinical application of the digital twin heart. With the rapid progress in data reuse and sharing, we expect their application to realize the transition from AF description to response prediction.
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Affiliation(s)
- Jieyun Bai
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- College of Information Science and Technology, Jinan University, Guangzhou, China
- *Correspondence: Jieyun Bai, ; Jichao Zhao,
| | - Yaosheng Lu
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Huijin Wang
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Jichao Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- *Correspondence: Jieyun Bai, ; Jichao Zhao,
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Narducci ML, Volpe M. Atrial fibrosis detected by cardiac magnetic resonance in persistent atrial fibrillation: a useful risk stratifier but not ideal electrophysiological endpoint for catheter ablation. Eur Heart J 2022; 43:3196-3197. [PMID: 35938848 DOI: 10.1093/eurheartj/ehac424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Maria Lucia Narducci
- Cardiovascular Sciences Department, Agostino Gemelli IRCCS University Hospital Foundation, Rome, Italy
| | - Massimo Volpe
- Cardiology Department, Sapienza University of Rome, Sant'Andrea Hospital, Rome, Italy
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Qu Z, Liu MB, Olcese R, Karagueuzian H, Garfinkel A, Chen PS, Weiss JN. R-on-T and the initiation of reentry revisited: Integrating old and new concepts. Heart Rhythm 2022; 19:1369-1383. [PMID: 35364332 PMCID: PMC11334931 DOI: 10.1016/j.hrthm.2022.03.1224] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/11/2022] [Accepted: 03/23/2022] [Indexed: 12/29/2022]
Abstract
Initiation of reentry requires 2 factors: (1) a triggering event, most commonly focal excitations such as premature ventricular complexes (PVCs); and (2) a vulnerable substrate with regional dispersion of refractoriness and/or excitability, such as occurs during the T wave of the electrocardiogram when some areas of the ventricle have repolarized and recovered excitability but others have not. When the R wave of a PVC coincides in time with the T wave of the previous beat, this timing can lead to unidirectional block and initiation of reentry, known as the R-on-T phenomenon. Classically, the PVC triggering reentry has been viewed as arising focally from 1 region and propagating into another region whose recovery is delayed, resulting in unidirectional conduction block and reentry initiation. However, more recent evidence indicates that PVCs also can arise from the T wave itself. In the latter case, the PVC initiating reentry is not a separate event from the T wave but rather is causally generated from the repolarization gradient that manifests as the T wave. We call the former an "R-to-T" mechanism and the latter an "R-from-T" mechanism, which are initiation mechanisms distinct from each other. Both are important components of the R-on-T phenomenon and need to be taken into account when designing antiarrhythmic strategies. Strategies targeting suppression of triggers alone or vulnerable substrate alone may be appropriate in some instances but not in others. Preventing R-from-T arrhythmias requires suppressing the underlying dynamic tissue instabilities responsible for producing both triggers and substrate vulnerability simultaneously. The same principles are likely to apply to supraventricular arrhythmias.
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Affiliation(s)
- Zhilin Qu
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, California.
| | - Michael B Liu
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Riccardo Olcese
- Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California, Los Angeles, California; Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Hrayr Karagueuzian
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Alan Garfinkel
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California; Department of Integrative Biology and Physiology, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Peng-Sheng Chen
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - James N Weiss
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California; Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, California
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Jin Z, Hwang I, Lim B, Kwon OS, Park JW, Yu HT, Kim TH, Joung B, Lee MH, Pak HN. Ablation and antiarrhythmic drug effects on PITX2+/− deficient atrial fibrillation: A computational modeling study. Front Cardiovasc Med 2022; 9:942998. [PMID: 35928934 PMCID: PMC9343754 DOI: 10.3389/fcvm.2022.942998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 06/23/2022] [Indexed: 11/13/2022] Open
Abstract
IntroductionAtrial fibrillation (AF) is a heritable disease, and the paired-like homeodomain transcription factor 2 (PITX2) gene is highly associated with AF. We explored the differences in the circumferential pulmonary vein isolation (CPVI), which is the cornerstone procedure for AF catheter ablation, additional high dominant frequency (DF) site ablation, and antiarrhythmic drug (AAD) effects according to the patient genotype (wild-type and PITX2+/− deficient) using computational modeling.MethodsWe included 25 patients with AF (68% men, 59.8 ± 9.8 years of age, 32% paroxysmal AF) who underwent AF catheter ablation to develop a realistic computational AF model. The ion currents for baseline AF and the amiodarone, dronedarone, and flecainide AADs according to the patient genotype (wild type and PITX2+/− deficient) were defined by relevant publications. We tested the virtual CPVI (V-CPVI) with and without DF ablation (±DFA) and three virtual AADs (V-AADs, amiodarone, dronedarone, and flecainide) and evaluated the AF defragmentation rates (AF termination or changes to regular atrial tachycardia (AT), DF, and maximal slope of the action potential duration restitution curves (Smax), which indicates the vulnerability of wave-breaks.ResultsAt the baseline AF, mean DF (p = 0.003), and Smax (p < 0.001) were significantly lower in PITX2+/− deficient patients than wild-type patients. In the overall AF episodes, V-CPVI (±DFA) resulted in a higher AF defragmentation relative to V-AADs (65 vs. 42%, p < 0.001) without changing the DF or Smax. Although a PITX2+/− deficiency did not affect the AF defragmentation rate after the V-CPVI (±DFA), V-AADs had a higher AF defragmentation rate (p = 0.014), lower DF (p < 0.001), and lower Smax (p = 0.001) in PITX2+/− deficient AF than in wild-type patients. In the clinical setting, the PITX2+/− genetic risk score did not affect the AF ablation rhythm outcome (Log-rank p = 0.273).ConclusionConsistent with previous clinical studies, the V-CPVI had effective anti-AF effects regardless of the PITX2 genotype, whereas V-AADs exhibited more significant defragmentation or wave-dynamic change in the PITX2+/− deficient patients.
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Cunha PS, Laranjo S, Heijman J, Oliveira MM. The Atrium in Atrial Fibrillation - A Clinical Review on How to Manage Atrial Fibrotic Substrates. Front Cardiovasc Med 2022; 9:879984. [PMID: 35859594 PMCID: PMC9289204 DOI: 10.3389/fcvm.2022.879984] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 06/03/2022] [Indexed: 12/27/2022] Open
Abstract
Atrial fibrillation (AF) is the most common sustained arrhythmia in the population and is associated with a significant clinical and economic burden. Rigorous assessment of the presence and degree of an atrial arrhythmic substrate is essential for determining treatment options, predicting long-term success after catheter ablation, and as a substrate critical in the pathophysiology of atrial thrombogenesis. Catheter ablation of AF has developed into an essential rhythm-control strategy. Nowadays is one of the most common cardiac ablation procedures performed worldwide, with its success inversely related to the extent of atrial structural disease. Although atrial substrate evaluation remains complex, several diagnostic resources allow for a more comprehensive assessment and quantification of the extent of left atrial structural remodeling and the presence of atrial fibrosis. In this review, we summarize the current knowledge on the pathophysiology, etiology, and electrophysiological aspects of atrial substrates promoting the development of AF. We also describe the risk factors for its development and how to diagnose its presence using imaging, electrocardiograms, and electroanatomic voltage mapping. Finally, we discuss recent data regarding fibrosis biomarkers that could help diagnose atrial fibrotic substrates.
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Affiliation(s)
- Pedro Silva Cunha
- Arrhythmology, Pacing and Electrophysiology Unit, Cardiology Service, Santa Marta Hospital, Central Lisbon Hospital University Center, Lisbon, Portugal
- Lisbon School of Medicine, Universidade de Lisboa, Lisbon, Portugal
- Comprehensive Health Research Center, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Sérgio Laranjo
- Arrhythmology, Pacing and Electrophysiology Unit, Cardiology Service, Santa Marta Hospital, Central Lisbon Hospital University Center, Lisbon, Portugal
- Lisbon School of Medicine, Universidade de Lisboa, Lisbon, Portugal
- Comprehensive Health Research Center, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Jordi Heijman
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Mário Martins Oliveira
- Arrhythmology, Pacing and Electrophysiology Unit, Cardiology Service, Santa Marta Hospital, Central Lisbon Hospital University Center, Lisbon, Portugal
- Lisbon School of Medicine, Universidade de Lisboa, Lisbon, Portugal
- Comprehensive Health Research Center, Universidade NOVA de Lisboa, Lisbon, Portugal
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DG-Mapping: a novel software package for the analysis of any type of reentry and focal activation of simulated, experimental or clinical data of cardiac arrhythmia. Med Biol Eng Comput 2022; 60:1929-1945. [DOI: 10.1007/s11517-022-02550-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 02/13/2022] [Indexed: 01/24/2023]
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Serum periostin as a predictor of early recurrence of atrial fibrillation after catheter ablation. Heart Vessels 2022; 37:2059-2066. [PMID: 35778637 DOI: 10.1007/s00380-022-02115-x] [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: 03/23/2022] [Accepted: 06/03/2022] [Indexed: 11/04/2022]
Abstract
Catheter ablation is an effective method of rhythm therapy for atrial fibrillation (AF). AF recurrence is a common problem after catheter ablation. The aim of this study was to investigate influence factors of early recurrence after catheter ablation for AF. One hundred and three consecutive patients with AF were enrolled and underwent catheter ablation. Venous blood (Marked as A) was collected before ablation and left atrial blood (Marked as B) was collected after successful atrial septal puncture to detect serum periostin. After 3 months of follow-up, statistical analysis was made based on the recurrence of AF. 27 (26.2%) patients had a recurrence of atrial arrhythmia after catheter ablation. Patients with recurrent atrial arrhythmia had a larger left atrial volume (162.31 ± 47.76 vs. 141.98 ± 41.64,p = 0.039), and higher serum periostin levels (periostin A. 99.71 ± 16.475 vs. 90.36 ± 13.63, p = 0.005; periostin B. 103.95 ± 13.09 vs. 94.46 ± 15.85, p = 0.006) compared with the non-recurrent group. The numbers of patients with left atrial low-voltage areas (LVAs) were more in the recurrence group (p < 0.001). Left atrial volume, serum periostin and left atrial LVAs were included in univariate and multivariate COX regression analysis. It showed that left atrial LVAs (HR3.81; 95% CI 1.54 to 9.44; p = 0.004) and serum periostin A (HR1.07; 95% CI 1.02 to1.13; p = 0.008) were the independent predictors of AF recurrence. The cut-off value of serum periostin A was 87.95 ng/ ml (AUC, 0.681; sensitivity 88.9% and specificity 53.9%). Kaplan-Meier survival curve showed that the recurrence rate of AF was higher in patients with left atrial LVAs and higher serum periostin. The venous serum periostin level and left atrial LVAs were independent predictors of early recurrence of AF after catheter ablation.
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Farquhar ME, Burrage K, Weber Dos Santos R, Bueno-Orovio A, Lawson BA. Graph-based homogenisation for modelling cardiac fibrosis. JOURNAL OF COMPUTATIONAL PHYSICS 2022; 459:None. [PMID: 35959500 PMCID: PMC9352598 DOI: 10.1016/j.jcp.2022.111126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 02/15/2022] [Accepted: 03/02/2022] [Indexed: 05/02/2023]
Abstract
Fibrosis, the excess of extracellular matrix, can affect, and even block, propagation of action potential in cardiac tissue. This can result in deleterious effects on heart function, but the nature and severity of these effects depend strongly on the localisation of fibrosis and its by-products in cardiac tissue, such as collagen scar formation. Computer simulation is an important means of understanding the complex effects of fibrosis on activation patterns in the heart, but concerns of computational cost place restrictions on the spatial resolution of these simulations. In this work, we present a novel numerical homogenisation technique that uses both Eikonal and graph approaches to allow fine-scale heterogeneities in conductivity to be incorporated into a coarser mesh. Homogenisation achieves this by deriving effective conductivity tensors so that a coarser mesh can then be used for numerical simulation. By taking a graph-based approach, our homogenisation technique functions naturally on irregular grids and does not rely upon any assumptions of periodicity, even implicitly. We present results of action potential propagation through fibrotic tissue in two dimensions that show the graph-based homogenisation technique is an accurate and effective way to capture fine-scale domain information on coarser meshes in the context of sharp-fronted travelling waves of activation. As test problems, we consider excitation propagation in tissue with diffuse fibrosis and through a tunnel-like structure designed to test homogenisation, interaction of an excitation wave with a scar region, and functional re-entry.
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Affiliation(s)
- Megan E. Farquhar
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Kevin Burrage
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
- Department of Computer Science, Oxford University, Oxford, United Kingdom
| | - Rodrigo Weber Dos Santos
- Department of Computer Science and Program on Computational Modeling, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
| | | | - Brodie A.J. Lawson
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, Australia
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Sánchez J, Loewe A. A Review of Healthy and Fibrotic Myocardium Microstructure Modeling and Corresponding Intracardiac Electrograms. Front Physiol 2022; 13:908069. [PMID: 35620600 PMCID: PMC9127661 DOI: 10.3389/fphys.2022.908069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
Computational simulations of cardiac electrophysiology provide detailed information on the depolarization phenomena at different spatial and temporal scales. With the development of new hardware and software, in silico experiments have gained more importance in cardiac electrophysiology research. For plane waves in healthy tissue, in vivo and in silico electrograms at the surface of the tissue demonstrate symmetric morphology and high peak-to-peak amplitude. Simulations provided insight into the factors that alter the morphology and amplitude of the electrograms. The situation is more complex in remodeled tissue with fibrotic infiltrations. Clinically, different changes including fractionation of the signal, extended duration and reduced amplitude have been described. In silico, numerous approaches have been proposed to represent the pathological changes on different spatial and functional scales. Different modeling approaches can reproduce distinct subsets of the clinically observed electrogram phenomena. This review provides an overview of how different modeling approaches to incorporate fibrotic and structural remodeling affect the electrogram and highlights open challenges to be addressed in future research.
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Affiliation(s)
- Jorge Sánchez
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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Giorgios T, Antonio F, Limite LR, Felicia L, Zweiker D, Cireddu M, Vlachos K, Hadjis A, D'Angelo G, Baratto F, Bisceglia C, Vergara P, Marzi A, Peretto G, Paglino G, Radinovic A, Gulletta S, Sala S, Mazzone P, Bella PD. Bi‐atrial characterization of the electrical substrate in patients with atrial fibrillation. Pacing Clin Electrophysiol 2022; 45:752-760. [PMID: 35403246 PMCID: PMC9322275 DOI: 10.1111/pace.14490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/19/2022] [Accepted: 03/11/2022] [Indexed: 11/30/2022]
Abstract
Background Methods Results Conclusion
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Affiliation(s)
| | | | | | | | - David Zweiker
- Department of Arrhythmology San Raffaele Hospital Milan Italy
| | - Manuela Cireddu
- Department of Arrhythmology San Raffaele Hospital Milan Italy
| | | | - Alexios Hadjis
- Department of Arrhythmology San Raffaele Hospital Milan Italy
| | | | | | | | | | | | | | | | | | - Simone Gulletta
- Department of Arrhythmology San Raffaele Hospital Milan Italy
| | - Simone Sala
- Department of Arrhythmology San Raffaele Hospital Milan Italy
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Ríos-Muñoz GR, Soto N, Ávila P, Carta A, Atienza F, Datino T, González-Torrecilla E, Fernández-Avilés F, Arenal Á. Structural Remodeling and Rotational Activity in Persistent/Long-Lasting Atrial Fibrillation: Gender-Effect Differences and Impact on Post-ablation Outcome. Front Cardiovasc Med 2022; 9:819429. [PMID: 35387439 PMCID: PMC8977980 DOI: 10.3389/fcvm.2022.819429] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 02/02/2022] [Indexed: 11/13/2022] Open
Abstract
Background Structural and post-ablation gender differences are reported in atrial fibrillation (AF). We analyzed the gender differences in structural remodeling and AF mechanisms in patients with persistent/long-lasting AF who underwent wide area circumferential pulmonary vein isolation (WACPVI). Materials and Methods Ultra-high-density mapping was used to study atrial remodeling and AF drivers in 85 consecutive patients. Focal and rotational activity (RAc) were identified with the CartoFinder system and activation sequence analysis. The impact of RAc location on post-ablation outcomes was analyzed. Results This study included 64 men and 21 women. RAc was detected in 73.4% of men and 38.1% of women (p = 0.003). RAc patients had higher left atrium (LA) voltage (0.64 ± 0.3 vs. 0.50 ± 0.2 mV; p = 0.01), RAc sites had higher voltage than non-RAc sites 0.77 ± 0.46 vs. 0.53 ± 0.37 mV (p < 0.001). Women had lower LA voltage than men (0.42 vs. 0.64 mV; p < 0.001), including pulmonary vein (PV) antra (0.16 vs. 0.30 mV; p < 0.001) and posterior wall (0.34 vs. 0.51 mV; p < 0.001). RAc in the posterior atrium was recorded in few women (23.8 vs. 54.7% in men; p = 0.014). AF recurrence rate was higher in patients with RAc outside WACPVI than those with all RAc inside WACPVI or no RAc (63.4 vs. 11.1 and 31.0%; p = 0.008 and p = 0.01). Comparison of selected patients using propensity score matching confirmed lower atrial voltage (0.4 ± 0.2 vs. 0.7 ± 0.3 mV; p = 0.007) and less RAc (38 vs. 75%; p = 0.02) in women. Conclusion Women have shown more advanced structural remodeling at ablation, which is associated with a lower incidence of RAc (usually located outside the WACPVI). These findings could explain post-ablation gender differences.
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Affiliation(s)
- Gonzalo R Ríos-Muñoz
- Department of Cardiology, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Hospital General Universitario Gregorio Marañón, Madrid, Spain.,Center for Biomedical Research in Cardiovascular Disease Network (CIBERCV), Madrid, Spain.,Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
| | - Nina Soto
- Department of Cardiology, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Hospital General Universitario Gregorio Marañón, Madrid, Spain.,Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Pablo Ávila
- Department of Cardiology, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Hospital General Universitario Gregorio Marañón, Madrid, Spain.,Center for Biomedical Research in Cardiovascular Disease Network (CIBERCV), Madrid, Spain
| | - Alejandro Carta
- Department of Cardiology, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Hospital General Universitario Gregorio Marañón, Madrid, Spain.,Center for Biomedical Research in Cardiovascular Disease Network (CIBERCV), Madrid, Spain
| | - Felipe Atienza
- Department of Cardiology, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Hospital General Universitario Gregorio Marañón, Madrid, Spain.,Center for Biomedical Research in Cardiovascular Disease Network (CIBERCV), Madrid, Spain.,Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - Tomás Datino
- Department of Cardiology, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Hospital General Universitario Gregorio Marañón, Madrid, Spain.,Center for Biomedical Research in Cardiovascular Disease Network (CIBERCV), Madrid, Spain
| | - Esteban González-Torrecilla
- Department of Cardiology, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Hospital General Universitario Gregorio Marañón, Madrid, Spain.,Center for Biomedical Research in Cardiovascular Disease Network (CIBERCV), Madrid, Spain.,Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - Francisco Fernández-Avilés
- Department of Cardiology, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Hospital General Universitario Gregorio Marañón, Madrid, Spain.,Center for Biomedical Research in Cardiovascular Disease Network (CIBERCV), Madrid, Spain.,Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - Ángel Arenal
- Department of Cardiology, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Hospital General Universitario Gregorio Marañón, Madrid, Spain.,Center for Biomedical Research in Cardiovascular Disease Network (CIBERCV), Madrid, Spain
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