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Rodríguez-González E, Martínez-Legazpi P, González-Mansilla A, Espinosa MÁ, Mombiela T, Guzmán De-Villoria JA, Borja MG, Díaz-Otero F, Gómez de Antonio R, Fernández-García P, Fernández-Ávila AI, Pascual-Izquierdo C, Del Álamo JC, Bermejo J. Cardiac stasis imaging, stroke, and silent brain infarcts in patients with nonischemic dilated cardiomyopathy. Am J Physiol Heart Circ Physiol 2024; 327:H446-H453. [PMID: 38847759 DOI: 10.1152/ajpheart.00245.2024] [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: 04/18/2024] [Revised: 05/23/2024] [Accepted: 05/31/2024] [Indexed: 07/17/2024]
Abstract
Cardioembolic stroke is one of the most devastating complications of nonischemic dilated cardiomyopathy (NIDCM). However, in clinical trials of primary prevention, the benefits of anticoagulation are hampered by the risk of bleeding. Indices of cardiac blood stasis may account for the risk of stroke and be useful to individualize primary prevention treatments. We performed a cross-sectional study in patients with NIDCM and no history of atrial fibrillation (AF) from two sources: 1) a prospective enrollment of unselected patients with left ventricular (LV) ejection fraction <45% and 2) a retrospective identification of patients with a history of previous cardioembolic neurological event. The primary end point integrated a history of ischemic stroke or the presence intraventricular thrombus, or a silent brain infarction (SBI) by imaging. From echocardiography, we calculated blood flow inside the LV, its residence time (TR) maps, and its derived stasis indices. Of the 89 recruited patients, 18 showed a positive end point, 9 had a history of stroke or transient ischemic attack (TIA) and 9 were diagnosed with SBIs in the brain imaging. Averaged TR, [Formula: see text] performed well to identify the primary end point [AUC (95% CI) = 0.75 (0.61-0.89), P = 0.001]. When accounting only for identifying a history of stroke or TIA, AUC for [Formula: see text] was 0.92 (0.85-1.00) with odds ratio = 7.2 (2.3-22.3) per cycle, P < 0.001. These results suggest that in patients with NIDCM in sinus rhythm, stasis imaging derived from echocardiography may account for the burden of stroke.NEW & NOTEWORTHY Patients with nonischemic dilated cardiomyopathy (NIDCM) are at higher risk of stroke than their age-matched population. However, the risk of bleeding neutralizes the benefit of preventive oral anticoagulation. In this work, we show that in patients in sinus rhythm, the burden of stroke is related to intraventricular stasis metrics derived from echocardiography. Therefore, stasis metrics may be useful to personalize primary prevention anticoagulation in these patients.
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Affiliation(s)
- Elena Rodríguez-González
- Department of Cardiology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Department of Medicine, Facultad de Medicina, Universidad Complutense, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares, Madrid, Spain
| | - Pablo Martínez-Legazpi
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares, Madrid, Spain
- Department of Mathematical Physics and Fluids, Facultad de Ciencias, Universidad Nacional de Educación a Distancia, Madrid, Spain
| | - Ana González-Mansilla
- Department of Cardiology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Department of Medicine, Facultad de Medicina, Universidad Complutense, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares, Madrid, Spain
| | - M Ángeles Espinosa
- Department of Cardiology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Department of Medicine, Facultad de Medicina, Universidad Complutense, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares, Madrid, Spain
| | - Teresa Mombiela
- Department of Cardiology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Department of Medicine, Facultad de Medicina, Universidad Complutense, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares, Madrid, Spain
| | - Juan A Guzmán De-Villoria
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
- Department of Radiology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
| | - Maria Guadalupe Borja
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California, United States
| | - Fernando Díaz-Otero
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
- Department of Neurology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Rubén Gómez de Antonio
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
- Department of Hematology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Pilar Fernández-García
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
| | - Ana I Fernández-Ávila
- Department of Cardiology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Department of Medicine, Facultad de Medicina, Universidad Complutense, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares, Madrid, Spain
| | - Cristina Pascual-Izquierdo
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
- Department of Hematology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Juan C Del Álamo
- Division of Cardiology, Department of Mechanical Engineering, Center for Cardiovascular Biology, University of Washington, Seattle, Washington, United States
| | - Javier Bermejo
- Department of Cardiology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Department of Medicine, Facultad de Medicina, Universidad Complutense, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares, Madrid, Spain
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Catalano C, Crascì F, Puleo S, Scuoppo R, Pasta S, Raffa GM. Computational fluid dynamics in cardiac surgery and perfusion: A review. Perfusion 2024:2676591241239277. [PMID: 38850015 DOI: 10.1177/02676591241239277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2024]
Abstract
Cardiovascular diseases persist as a leading cause of mortality and morbidity, despite significant advances in diagnostic and surgical approaches. Computational Fluid Dynamics (CFD) represents a branch of fluid mechanics widely used in industrial engineering but is increasingly applied to the cardiovascular system. This review delves into the transformative potential for simulating cardiac surgery procedures and perfusion systems, providing an in-depth examination of the state-of-the-art in cardiovascular CFD modeling. The study first describes the rationale for CFD modeling and later focuses on the latest advances in heart valve surgery, transcatheter heart valve replacement, aortic aneurysms, and extracorporeal membrane oxygenation. The review underscores the role of CFD in better understanding physiopathology and its clinical relevance, as well as the profound impact of hemodynamic stimuli on patient outcomes. By integrating computational methods with advanced imaging techniques, CFD establishes a quantitative framework for understanding the intricacies of the cardiac field, providing valuable insights into disease progression and treatment strategies. As technology advances, the evolving synergy between computational simulations and clinical interventions is poised to revolutionize cardiovascular care. This collaboration sets the stage for more personalized and effective therapeutic strategies. With its potential to enhance our understanding of cardiac pathologies, CFD stands as a promising tool for improving patient outcomes in the dynamic landscape of cardiovascular medicine.
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Affiliation(s)
- Chiara Catalano
- Department of Engineering, Università degli Studi di Palermo, Palermo, Italy
| | - Fabrizio Crascì
- Department of Engineering, Università degli Studi di Palermo, Palermo, Italy
- Department of Research, IRCCS-ISMETT, Palermo, Italy
| | - Silvia Puleo
- Department of Engineering, Università degli Studi di Palermo, Palermo, Italy
| | - Roberta Scuoppo
- Department of Engineering, Università degli Studi di Palermo, Palermo, Italy
| | - Salvatore Pasta
- Department of Engineering, Università degli Studi di Palermo, Palermo, Italy
- Department of Research, IRCCS-ISMETT, Palermo, Italy
| | - Giuseppe M Raffa
- Department for the Treatment and Study of Cardiothoracic Diseases and Cardiothoracic Transplantation, IRCCS-ISMETT, Palermo, Italy
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Verstraeten S, Hoeijmakers M, Tonino P, Brüning J, Capelli C, van de Vosse F, Huberts W. Generation of synthetic aortic valve stenosis geometries for in silico trials. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2024; 40:e3778. [PMID: 37961993 DOI: 10.1002/cnm.3778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 09/01/2023] [Accepted: 09/17/2023] [Indexed: 11/15/2023]
Abstract
In silico trials are a promising way to increase the efficiency of the development, and the time to market of cardiovascular implantable devices. The development of transcatheter aortic valve implantation (TAVI) devices, could benefit from in silico trials to overcome frequently occurring complications such as paravalvular leakage and conduction problems. To be able to perform in silico TAVI trials virtual cohorts of TAVI patients are required. In a virtual cohort, individual patients are represented by computer models that usually require patient-specific aortic valve geometries. This study aimed to develop a virtual cohort generator that generates anatomically plausible, synthetic aortic valve stenosis geometries for in silico TAVI trials and allows for the selection of specific anatomical features that influence the occurrence of complications. To build the generator, a combination of non-parametrical statistical shape modeling and sampling from a copula distribution was used. The developed virtual cohort generator successfully generated synthetic aortic valve stenosis geometries that are comparable with a real cohort, and therefore, are considered as being anatomically plausible. Furthermore, we were able to select specific anatomical features with a sensitivity of around 90%. The virtual cohort generator has the potential to be used by TAVI manufacturers to test their devices. Future work will involve including calcifications to the synthetic geometries, and applying high-fidelity fluid-structure-interaction models to perform in silico trials.
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Affiliation(s)
- Sabine Verstraeten
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Pim Tonino
- Department of Cardiology, Catharina Hospital, Eindhoven, The Netherlands
| | - Jan Brüning
- Institute of Computer-assisted Cardiovascular Medicine, Charite Universitaetsmedizin, Berlin, Germany
| | - Claudio Capelli
- Institute of Cardiovascular Science, University College London, London, UK
| | - Frans van de Vosse
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Wouter Huberts
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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Zingaro A, Bucelli M, Fumagalli I, Dede' L, Quarteroni A. Modeling isovolumetric phases in cardiac flows by an Augmented Resistive Immersed Implicit Surface method. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2023; 39:e3767. [PMID: 37615375 DOI: 10.1002/cnm.3767] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 06/05/2023] [Accepted: 07/30/2023] [Indexed: 08/25/2023]
Abstract
A major challenge in the computational fluid dynamics modeling of the heart function is the simulation of isovolumetric phases when the hemodynamics problem is driven by a prescribed boundary displacement. During such phases, both atrioventricular and semilunar valves are closed: consequently, the ventricular pressure may not be uniquely defined, and spurious oscillations may arise in numerical simulations. These oscillations can strongly affect valve dynamics models driven by the blood flow, making unlikely to recovering physiological dynamics. Hence, prescribed opening and closing times are usually employed, or the isovolumetric phases are neglected altogether. In this article, we propose a suitable modification of the Resistive Immersed Implicit Surface (RIIS) method (Fedele et al., Biomech Model Mechanobiol 2017, 16, 1779-1803) by introducing a reaction term to correctly capture the pressure transients during isovolumetric phases. The method, that we call Augmented RIIS (ARIIS) method, extends the previously proposed ARIS method (This et al., Int J Numer Methods Biomed Eng 2020, 36, e3223) to the case of a mesh which is not body-fitted to the valves. We test the proposed method on two different benchmark problems, including a new simplified problem that retains all the characteristics of a heart cycle. We apply the ARIIS method to a fluid dynamics simulation of a realistic left heart geometry, and we show that ARIIS allows to correctly simulate isovolumetric phases, differently from standard RIIS method. Finally, we demonstrate that by the new method the cardiac valves can open and close without prescribing any opening/closing times.
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Affiliation(s)
- Alberto Zingaro
- MOX, Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
- ELEM Biotech S.L., Barcelona, Spain
| | - Michele Bucelli
- MOX, Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
| | - Ivan Fumagalli
- MOX, Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
| | - Luca Dede'
- MOX, Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
| | - Alfio Quarteroni
- MOX, Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Syed F, Khan S, Toma M. Modeling Dynamics of the Cardiovascular System Using Fluid-Structure Interaction Methods. BIOLOGY 2023; 12:1026. [PMID: 37508455 PMCID: PMC10376821 DOI: 10.3390/biology12071026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/07/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023]
Abstract
Using fluid-structure interaction algorithms to simulate the human circulatory system is an innovative approach that can provide valuable insights into cardiovascular dynamics. Fluid-structure interaction algorithms enable us to couple simulations of blood flow and mechanical responses of the blood vessels while taking into account interactions between fluid dynamics and structural behaviors of vessel walls, heart walls, or valves. In the context of the human circulatory system, these algorithms offer a more comprehensive representation by considering the complex interplay between blood flow and the elasticity of blood vessels. Algorithms that simulate fluid flow dynamics and the resulting forces exerted on vessel walls can capture phenomena such as wall deformation, arterial compliance, and the propagation of pressure waves throughout the cardiovascular system. These models enhance the understanding of vasculature properties in human anatomy. The utilization of fluid-structure interaction methods in combination with medical imaging can generate patient-specific models for individual patients to facilitate the process of devising treatment plans. This review evaluates current applications and implications of fluid-structure interaction algorithms with respect to the vasculature, while considering their potential role as a guidance tool for intervention procedures.
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Affiliation(s)
- Faiz Syed
- College of Osteopathic Medicine, New York Institute of Technology, Northern Boulevard, Old Westbury, NY 11568, USA
| | - Sahar Khan
- College of Osteopathic Medicine, New York Institute of Technology, Northern Boulevard, Old Westbury, NY 11568, USA
| | - Milan Toma
- College of Osteopathic Medicine, New York Institute of Technology, Northern Boulevard, Old Westbury, NY 11568, USA
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Tahir AM, Mutlu O, Bensaali F, Ward R, Ghareeb AN, Helmy SMHA, Othman KT, Al-Hashemi MA, Abujalala S, Chowdhury MEH, Alnabti ARDMH, Yalcin HC. Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes. J Clin Med 2023; 12:4774. [PMID: 37510889 PMCID: PMC10381346 DOI: 10.3390/jcm12144774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/08/2023] [Accepted: 04/10/2023] [Indexed: 07/30/2023] Open
Abstract
Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.
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Affiliation(s)
- Anas M Tahir
- Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
| | - Onur Mutlu
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
| | - Faycal Bensaali
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Rabab Ward
- Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Abdel Naser Ghareeb
- Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
- Faculty of Medicine, Al Azhar University, Cairo 11884, Egypt
| | - Sherif M H A Helmy
- Noninvasive Cardiology Section, Cardiology Department, Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
| | | | - Mohammed A Al-Hashemi
- Noninvasive Cardiology Section, Cardiology Department, Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
| | | | | | | | - Huseyin C Yalcin
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
- Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha 2713, Qatar
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