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Emfietzoglou M, Mavrogiannis MC, García-García HM, Stamatelopoulos K, Kanakakis I, Papafaklis MI. Current Toolset in Predicting Acute Coronary Thrombotic Events: The “Vulnerable Plaque” in a “Vulnerable Patient” Concept. Life (Basel) 2023; 13:life13030696. [PMID: 36983851 PMCID: PMC10052113 DOI: 10.3390/life13030696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/01/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
Despite major advances in pharmacotherapy and interventional procedures, coronary artery disease (CAD) remains a principal cause of morbidity and mortality worldwide. Invasive coronary imaging along with the computation of hemodynamic forces, primarily endothelial shear stress and plaque structural stress, have enabled a comprehensive identification of atherosclerotic plaque components, providing a unique insight into the understanding of plaque vulnerability and progression, which may help guide patient treatment. However, the invasive-only approach to CAD has failed to show high predictive value. Meanwhile, it is becoming increasingly evident that along with the “vulnerable plaque”, the presence of a “vulnerable patient” state is also necessary to precipitate an acute coronary thrombotic event. Non-invasive imaging techniques have also evolved, providing new opportunities for the identification of high-risk plaques, the study of atherosclerosis in asymptomatic individuals, and general population screening. Additionally, risk stratification scores, circulating biomarkers, immunology, and genetics also complete the armamentarium of a broader “vulnerable plaque and patient” concept approach. In the current review article, the invasive and non-invasive modalities used for the detection of high-risk plaques in patients with CAD are summarized and critically appraised. The challenges of the vulnerable plaque concept are also discussed, highlighting the need to shift towards a more interdisciplinary approach that can identify the “vulnerable plaque” in a “vulnerable patient”.
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Affiliation(s)
| | - Michail C. Mavrogiannis
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK
| | - Hector M. García-García
- Section of Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC 20010, USA
| | - Kimon Stamatelopoulos
- Department of Therapeutics, Faculty of Medicine, National and Kapodistrian University of Athens, 157 72 Athens, Greece
| | - Ioannis Kanakakis
- Catheterization and Hemodynamic Unit, Alexandra University Hospital, 115 28 Athens, Greece
| | - Michail I. Papafaklis
- Catheterization and Hemodynamic Unit, Alexandra University Hospital, 115 28 Athens, Greece
- Correspondence: ; Tel.: +30-6944376572
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2
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An automated software for real-time quantification of wall shear stress distribution in quantitative coronary angiography data. Int J Cardiol 2022; 357:14-19. [PMID: 35292271 DOI: 10.1016/j.ijcard.2022.03.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 02/26/2022] [Accepted: 03/09/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND Wall shear stress (WSS) estimated in 3D-quantitative coronary angiography (QCA) models appears to provide useful prognostic information and identifies high-risk patients and lesions. However, conventional computational fluid dynamics (CFD) analysis is cumbersome limiting its application in the clinical arena. This report introduces a user-friendly software that allows real-time WSS computation and examines its reproducibility and accuracy in assessing WSS distribution against conventional CFD analysis. METHODS From a registry of 414 patients with borderline negative fractional flow reserve (0.81-0.85), 100 lesions were randomly selected. 3D-QCA and CFD analysis were performed using the conventional approach and the novel CAAS Workstation WSS software, and QCA as well as WSS estimations of the two approaches were compared. The reproducibility of the two methodologies was evaluated in a subgroup of 50 lesions. RESULTS A good agreement was noted between the conventional approach and the novel software for 3D-QCA metrics (ICC range: 0.73-0-93) and maximum WSS at the lesion site (ICC: 0.88). Both methodologies had a high reproducibility in assessing lesion severity (ICC range: 0.83-0.97 for the conventional approach; 0.84-0.96 for the CAAS Workstation WSS software) and WSS distribution (ICC: 0.85-0.89 and 0.83-0.87, respectively). Simulation time was significantly shorter using the CAAS Workstation WSS software compared to the conventional approach (4.13 ± 0.59 min vs 23.14 ± 2.56 min, p < 0.001). CONCLUSION CAAS Workstation WSS software is fast, reproducible, and accurate in assessing WSS distribution. Therefore, this software is expected to enable the broad use of WSS metrics in the clinical arena to identify high-risk lesions and vulnerable patients.
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3
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Lu G, Ye W, Ou J, Li X, Tan Z, Li T, Liu H. Coronary Computed Tomography Angiography Assessment of High-Risk Plaques in Predicting Acute Coronary Syndrome. Front Cardiovasc Med 2021; 8:743538. [PMID: 34660742 PMCID: PMC8517134 DOI: 10.3389/fcvm.2021.743538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 09/07/2021] [Indexed: 01/07/2023] Open
Abstract
Coronary computed tomography angiography (CCTA) is a comprehensive, non-invasive and cost-effective imaging assessment approach, which can provide the ability to identify the characteristics and morphology of high-risk atherosclerotic plaques associated with acute coronary syndrome (ACS). The development of CCTA and latest advances in emerging technologies, such as computational fluid dynamics (CFD), have made it possible not only to identify the morphological characteristics of high-risk plaques non-invasively, but also to assess the hemodynamic parameters, the environment surrounding coronaries and so on, which may help to predict the risk of ACS. In this review, we present how CCTA was used to characterize the composition and morphology of high-risk plaques prone to ACS and the current role of CCTA, including emerging CCTA technologies, advanced analysis, and characterization techniques in prognosticating the occurrence of ACS.
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Affiliation(s)
- Guanyu Lu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,College of Medicine, Shantou University, Shantou, China
| | - Weitao Ye
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jiehao Ou
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xinyun Li
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zekun Tan
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Tingyu Li
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Hui Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,College of Medicine, Shantou University, Shantou, China
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4
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van den Hoogen IJ, van Rosendael AR, Lin FY, Gianni U, Andreini D, Al-Mallah MH, Budoff MJ, Cademartiri F, Chinnaiyan K, Hyun Choi J, Conte E, Marques H, de Araújo Gonçalves P, Gottlieb I, Hadamitzky M, Leipsic J, Maffei E, Pontone G, Shin S, Kim YJ, Lee BK, Chun EJ, Sung JM, Lee SE, Berman DS, Virmani R, Samady H, Stone PH, Narula J, Chang HJ, Min JK, Shaw LJ, Bax JJ. Measurement of compensatory arterial remodelling over time with serial coronary computed tomography angiography and 3D metrics. Eur Heart J Cardiovasc Imaging 2021; 23:1336-1344. [PMID: 34468717 DOI: 10.1093/ehjci/jeab138] [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: 06/18/2020] [Indexed: 11/14/2022] Open
Abstract
AIMS The magnitude of alterations in which coronary arteries remodel and narrow over time is not well understood. We aimed to examine changes in coronary arterial remodelling and luminal narrowing by three-dimensional (3D) metrics from serial coronary computed tomography angiography (CCTA). METHODS AND RESULTS From a multicentre registry of patients with suspected coronary artery disease who underwent clinically indicated serial CCTA (median interscan interval = 3.3 years), we quantitatively measured coronary plaque, vessel, and lumen volumes on both scans. Primary outcome was the per-segment change in coronary vessel and lumen volume from a change in plaque volume, focusing on arterial remodelling. Multivariate generalized estimating equations including statins were calculated comparing associations between groups of baseline percent atheroma volume (PAV) and location within the coronary artery tree. From 1245 patients (mean age 61 ± 9 years, 39% women), a total of 5721 segments were analysed. For each 1.00 mm3 increase in plaque volume, the vessel volume increased by 0.71 mm3 [95% confidence interval (CI) 0.63 to 0.79 mm3, P < 0.001] with a corresponding reduction in lumen volume by 0.29 mm3 (95% CI -0.37 to -0.21 mm3, P < 0.001). Serial 3D arterial remodelling and luminal narrowing was similar in segments with low and high baseline PAV (P ≥ 0.496). No differences were observed between left main and non-left main segments, proximal and distal segments and side branch and non-side branch segments (P ≥ 0.281). CONCLUSIONS Over time, atherosclerotic coronary plaque reveals prominent outward arterial remodelling that co-occurs with modest luminal narrowing. These findings provide additional insight into the compensatory mechanisms involved in the progression of coronary atherosclerosis.
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Affiliation(s)
- Inge J van den Hoogen
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA.,Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Alexander R van Rosendael
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA.,Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Fay Y Lin
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Umberto Gianni
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA.,Department of Healthcare Policy and Research, New York-Presbyterian Hospital and the Weill Cornell Medical College, New York, NY, USA
| | | | - Mouaz H Al-Mallah
- Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, TX, USA
| | - Matthew J Budoff
- Department of Medicine, Los Angeles Biomedical Research Institute, Torrance, CA, USA
| | | | | | | | | | - Hugo Marques
- UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisboa, Portugal
| | | | - Ilan Gottlieb
- Department of Radiology, Casa de Saude São Jose, Rio de Janeiro, Brazil
| | - Martin Hadamitzky
- Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany
| | - Jonathon Leipsic
- Department of Medicine and Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Erica Maffei
- Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy
| | | | - Sanghoon Shin
- Division of Cardiology, Department of Internal Medicine, Ewha Womans University Seoul Hospital, Seoul, Korea
| | - Yong-Jin Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Cardiovascular Center, Seoul National University Hospital, Seoul, South Korea
| | - Byoung Kwon Lee
- Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Eun Ju Chun
- Seoul National University Bundang Hospital, Sungnam, South Korea
| | - Ji Min Sung
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea.,Yonsei-Cedars-Sinai Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea
| | - Sang-Eun Lee
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea.,Yonsei-Cedars-Sinai Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea
| | - Daniel S Berman
- Department of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Renu Virmani
- Department of Pathology, CVPath Institute, Gaithersburg, MD, USA
| | - Habib Samady
- Division of Cardiology, Emory University School of Medicine, Atlanta, GA, USA
| | - Peter H Stone
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jagat Narula
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, Zena and Michael A. Wiener Cardiovascular Institute, and Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, New York, NY, USA
| | - Hyuk-Jae Chang
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea.,Ontact Health, Inc, Seoul, South Korea
| | - James K Min
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Leslee J Shaw
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Jeroen J Bax
- Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
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5
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Nudi F, Nudi A, Biondi-Zoccai G, Schillaci O. Myocardial perfusion imaging with cadmium-zinc-telluride cameras: Harry Potter and the Radiation Hallows? J Nucl Cardiol 2021; 28:1000-1004. [PMID: 32676912 DOI: 10.1007/s12350-020-02267-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 05/19/2020] [Indexed: 02/08/2023]
Affiliation(s)
- Francesco Nudi
- Service of Hybrid Cardio Imaging, Madonna della Fiducia Clinic, Via Giuseppe Mantellini 3, 00179, Rome, Italy.
- Ostia Radiologica, Rome, Italy.
- Replycare, Rome, Italy.
- ETISAN, Rome, Italy.
| | - Alessandro Nudi
- Service of Hybrid Cardio Imaging, Madonna della Fiducia Clinic, Via Giuseppe Mantellini 3, 00179, Rome, Italy
| | - Giuseppe Biondi-Zoccai
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Latina, Italy
- Mediterranea Cardiocentro, Napoli, Italy
| | - Orazio Schillaci
- Department of Biomedicine and Prevention, Tor Vergata University, Rome, Italy
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6
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Gijsen F, Katagiri Y, Barlis P, Bourantas C, Collet C, Coskun U, Daemen J, Dijkstra J, Edelman E, Evans P, van der Heiden K, Hose R, Koo BK, Krams R, Marsden A, Migliavacca F, Onuma Y, Ooi A, Poon E, Samady H, Stone P, Takahashi K, Tang D, Thondapu V, Tenekecioglu E, Timmins L, Torii R, Wentzel J, Serruys P. Expert recommendations on the assessment of wall shear stress in human coronary arteries: existing methodologies, technical considerations, and clinical applications. Eur Heart J 2020; 40:3421-3433. [PMID: 31566246 PMCID: PMC6823616 DOI: 10.1093/eurheartj/ehz551] [Citation(s) in RCA: 153] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 04/09/2019] [Accepted: 09/23/2019] [Indexed: 01/09/2023] Open
Affiliation(s)
- Frank Gijsen
- Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Yuki Katagiri
- Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, the Netherlands
| | - Peter Barlis
- Department of Medicine and Radiology, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia.,Department of Cardiology, Northern Hospital, 185 Cooper Street, Epping, Australia.,St Vincent's Heart Centre, Building C, 41 Victoria Parade, Fitzroy, Australia
| | - Christos Bourantas
- Institute of Cardiovascular Sciences, University College of London, London, UK.,Department of Cardiology, Barts Heart Centre, London, UK.,School of Medicine and Dentistry, Queen Mary University London, London, UK
| | - Carlos Collet
- Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, the Netherlands
| | - Umit Coskun
- Division of Cardiovascular Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joost Daemen
- Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Jouke Dijkstra
- LKEB-Division of Image Processing, Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands
| | - Elazer Edelman
- Division of Cardiovascular Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.,Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
| | - Paul Evans
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
| | - Kim van der Heiden
- Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Rod Hose
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK.,Department of Circulation and Imaging, NTNU, Trondheim, Norway
| | - Bon-Kwon Koo
- Department of Internal Medicine and Cardiovascular Center, Seoul National University Hospital, Seoul, Korea.,Institute of Aging, Seoul National University, Seoul, Korea
| | - Rob Krams
- School of Engineering and Materials Science Queen Mary University of London, London, UK
| | - Alison Marsden
- Departments of Bioengineering and Pediatrics, Institute of Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Francesco Migliavacca
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Yoshinobu Onuma
- Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Andrew Ooi
- Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Eric Poon
- Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Habib Samady
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Peter Stone
- Division of Cardiovascular Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kuniaki Takahashi
- Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, the Netherlands
| | - Dalin Tang
- Department of Mathematics, Southeast University, Nanjing, China; Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Vikas Thondapu
- Department of Medicine and Radiology, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia.,Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia.,Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Erhan Tenekecioglu
- Department of Interventional Cardiology, Thoraxcentre, Erasmus Medical Centre, Rotterdam, the Netherlands
| | - Lucas Timmins
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT.,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, UK
| | - Jolanda Wentzel
- Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Patrick Serruys
- Erasmus University Medical Center, Rotterdam, the Netherlands.,Imperial College London, London, UK.,Melbourne School of Engineering, University of Melbourne, Melbourne, Australia
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7
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Abdelrahman KM, Chen MY, Dey AK, Virmani R, Finn AV, Khamis RY, Choi AD, Min JK, Williams MC, Buckler AJ, Taylor CA, Rogers C, Samady H, Antoniades C, Shaw LJ, Budoff MJ, Hoffmann U, Blankstein R, Narula J, Mehta NN. Coronary Computed Tomography Angiography From Clinical Uses to Emerging Technologies: JACC State-of-the-Art Review. J Am Coll Cardiol 2020; 76:1226-1243. [PMID: 32883417 PMCID: PMC7480405 DOI: 10.1016/j.jacc.2020.06.076] [Citation(s) in RCA: 130] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 05/08/2020] [Accepted: 06/10/2020] [Indexed: 12/14/2022]
Abstract
Evaluation of coronary artery disease (CAD) using coronary computed tomography angiography (CCTA) has seen a paradigm shift in the last decade. Evidence increasingly supports the clinical utility of CCTA across various stages of CAD, from the detection of early subclinical disease to the assessment of acute chest pain. Additionally, CCTA can be used to noninvasively quantify plaque burden and identify high-risk plaque, aiding in diagnosis, prognosis, and treatment. This is especially important in the evaluation of CAD in immune-driven conditions with increased cardiovascular disease prevalence. Emerging applications of CCTA based on hemodynamic indices and plaque characterization may provide personalized risk assessment, affect disease detection, and further guide therapy. This review provides an update on the evidence, clinical applications, and emerging technologies surrounding CCTA as highlighted at the 2019 National Heart, Lung and Blood Institute CCTA Summit.
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Affiliation(s)
- Khaled M Abdelrahman
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Marcus Y Chen
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Amit K Dey
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Renu Virmani
- Department of Pathology, CVPath Institute, Gaithersburg, Maryland
| | - Aloke V Finn
- Department of Pathology, CVPath Institute, Gaithersburg, Maryland
| | - Ramzi Y Khamis
- Vascular Sciences Section, National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Andrew D Choi
- Division of Cardiology and Department of Radiology, The George Washington University School of Medicine, Washington, DC
| | - James K Min
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, Queen's Medical Research Institute University of Edinburgh, Edinburgh, United Kingdom
| | | | | | | | - Habib Samady
- Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia
| | - Charalambos Antoniades
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Leslee J Shaw
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, New York
| | - Matthew J Budoff
- Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California
| | - Udo Hoffmann
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ron Blankstein
- Departments of Medicine (Cardiovascular Division) and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jagat Narula
- Zena and Michael A. Wiener Cardiovascular Institute, Marie-Josée and Henry R. Kravis Center for Cardiovascular Health Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, New York
| | - Nehal N Mehta
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
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8
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Sakellarios AI, Pezoulas VC, Bourantas C, Naka KK, Michalis LK, Serruys PW, Stone G, Garcia-Garcia HM, Fotiadis DI. Prediction of atherosclerotic disease progression combining computational modelling with machine learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2760-2763. [PMID: 33018578 DOI: 10.1109/embc44109.2020.9176435] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Non-invasive serial computed tomography coronary angiography (CTCA) was acquired from 32 patients and 3D reconstruction of 58 coronary arteries was achieved. The arterial geometries were utilized for blood flow and LDL transport modelling. Navier-Stokes and convection-diffusion equations were employed for simulation of blood flow and LDL transport, respectively. Disease progression was assessed comparing the follow-up and baseline arterial models after co-registration using side branches as anatomical landmarks. A machine learning model for predicting disease progression was built using the Gradient Boosted Trees (GBT) algorithm. The Accuracy, Sensitivity, Specificity and AUC of the developed methodology for predicting lumen area decrease equal was 0.68, 0.56, 0.34 and 0.59, respectively. The best results were found for the prediction of plaque area increase by 20%, with 0.73, 0.67, 0.86, and 0.76 accuracy, sensitivity, specificity andAUC, respectively. This approach outperforms significantly the predictive capability of models based on binary logistic regression.
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9
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Sakellarios AI, Pelosi G, Fotiadis DI, Tsompou P, Siogkas P, Kigka V, Andrikos I, Tachos N, Georga E, Kyriakidis S, Rocchiccioli S. Predictive Models of Coronary Artery Disease Based on Computational Modeling: The SMARTool System. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:7002-7005. [PMID: 31947450 DOI: 10.1109/embc.2019.8857040] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
SMARTool aims to the development of Decision Support Systems (DSS) for the risk stratification, diagnosis, prediction and treatment of coronary artery disease (CAD). In this work, we present the results of the prediction DSS, which utilizes clinical data, imaging morphological characteristics and computational modeling results. More specifically, 263 patients were recruited in the SMARTool clinical trial and 196 patients were selected for the DSS development. Traditional risk factors, blood examinations and computed coronary tomography angiography (CCTA) were performed at two different time points with an interscan period 6.22 ± 1.42 years. Computational modeling of blood flow and LDL transport was performed at the baseline. Predictive models are built for the prediction of CAD at the follow-up. The results show that CAD can be predicted with 83% accuracy, when low ESS, high accumulation of LDL and imaging data are included in the model.
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10
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Pleouras D, Rocchiccioli S, Pelosi G, Michalis LK, Fotiadis DI, Sakellarios AI, Kyriakidis S, Kigka V, Siogkas P, Tsompou P, Tachos N, Georga E, Andrikos I. A computational multi-level atherosclerotic plaque growth model for coronary arteries. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:5010-5013. [PMID: 31946985 DOI: 10.1109/embc.2019.8857329] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this work, we present a novel computational approach for the prediction of atherosclerotic plaque growth. In particular, patient-specific coronary computed tomography angiography (CCTA) data were collected from 60 patients at two time points. Additionally, blood samples were collected for biochemical analysis. The CCTA data were used for 3D reconstruction of the coronary arteries, which were then used for computational modeling of plaque growth. The model of plaque growth is based on a multi-level approach: i) the blood flow is modeled in the lumen and the arterial wall, ii) the low and high density lipoprotein and monocytes transport is included, and iii) the major atherosclerotic processes are modeled including the foam cells formation, the proliferation of smooth muscle cells and the formation of atherosclerotic plaque. Validation of the model was performed using the follow-up CCTA. The results show a correlation of the simulated follow-up arterial wall area to be correlated with the corresponding realistic follow-up with r2=0.49, P<; 0.0001.
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11
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Ramasamy A, Safi H, Moon J, Andiapen M, Rathod K, Maurovich-Horvat P, Bajaj R, Serruys P, Mathur A, Baumbach A, Pugliese F, Torii R, Bourantas C. Evaluation of the Efficacy of Computed Tomographic Coronary Angiography in Assessing Coronary Artery Morphology and Physiology: Rationale and Study Design. Cardiology 2020; 145:285-293. [DOI: 10.1159/000506537] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 02/12/2020] [Indexed: 11/19/2022]
Abstract
Computed tomographic coronary angiography (CTCA) is a non-invasive imaging modality, which allows plaque burden and composition assessment and detection of plaque characteristics associated with increased vulnerability. In addition, CTCA-based coronary artery reconstruction enables local haemodynamic forces assessment, which regulate plaque formation and vascular inflammation and prediction of lesions that are prone to progress and cause events. However, the use of CTCA for vulnerable plaque detection in the clinical arena remains limited. To unlock the full potential of CTCA and enable its broad use, further work is needed to develop user-friendly processing tools that will allow fast and accurate analysis of CTCA, computational fluid dynamic modelling, and evaluation of the local haemodynamic forces. The present study aims to develop a seamless platform that will overcome the limitations of CTCA and enable fast and accurate evaluation of plaque morphology and physiology. We will analyse imaging data from 70 patients with coronary artery disease who will undergo state-of-the-art CTCA and near-infrared spectroscopy-intravascular ultrasound imaging and develop and train algorithms that will take advantage of the intravascular imaging data to optimise vessel segmentation and plaque characterisation. Furthermore, we will design an advanced module that will enable reconstruction of coronary artery anatomy from CTCA, blood flow simulation, shear stress estimation, and comprehensive visualisation of vessel pathophysiology. These advances are expected to facilitate the broad use of CTCA, not only for risk stratification but also for the evaluation of the effect of emerging therapies on plaque evolution.
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Kigka VI, Sakellarios A, Kyriakidis S, Rigas G, Athanasiou L, Siogkas P, Tsompou P, Loggitsi D, Benz DC, Buechel R, Lemos PA, Pelosi G, Michalis LK, Fotiadis DI. A three-dimensional quantification of calcified and non-calcified plaques in coronary arteries based on computed tomography coronary angiography images: Comparison with expert's annotations and virtual histology intravascular ultrasound. Comput Biol Med 2019; 113:103409. [PMID: 31480007 DOI: 10.1016/j.compbiomed.2019.103409] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 08/23/2019] [Accepted: 08/23/2019] [Indexed: 12/31/2022]
Abstract
The detection, quantification and characterization of coronary atherosclerotic plaques has a major effect on the diagnosis and treatment of coronary artery disease (CAD). Different studies have reported and evaluated the noninvasive ability of Computed Tomography Coronary Angiography (CTCA) to identify coronary plaque features. The identification of calcified plaques (CP) and non-calcified plaques (NCP) using CTCA has been extensively studied in cardiovascular research. However, NCP detection remains a challenging problem in CTCA imaging, due to the similar intensity values of NCP compared to the perivascular tissue, which surrounds the vasculature. In this work, we present a novel methodology for the identification of the plaque burden of the coronary artery and the volumetric quantification of CP and NCP utilizing CTCA images and we compare the findings with virtual histology intravascular ultrasound (VH-IVUS) and manual expert's annotations. Bland-Altman analyses were employed to assess the agreement between the presented methodology and VH-IVUS. The assessment of the plaque volume, the lesion length and the plaque area in 18 coronary lesions indicated excellent correlation with VH-IVUS. More specifically, for the CP lesions the correlation of plaque volume, lesion length and plaque area was 0.93, 0.84 and 0.85, respectively, whereas the correlation of plaque volume, lesion length and plaque area for the NCP lesions was 0.92, 0.95 and 0.81, respectively. In addition to this, the segmentation of the lumen, CP and NCP in 1350 CTCA slices indicated that the mean value of DICE coefficient is 0.72, 0.7 and 0.62, whereas the mean HD value is 1.95, 1.74 and 1.95, for the lumen, CP and NCP, respectively.
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Affiliation(s)
- Vassiliki I Kigka
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110, Ioannina, Greece; Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research Institute - FORTH, University Campus of Ioannina, GR 45110, Ioannina, Greece
| | - Antonis Sakellarios
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110, Ioannina, Greece; Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research Institute - FORTH, University Campus of Ioannina, GR 45110, Ioannina, Greece
| | - Savvas Kyriakidis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110, Ioannina, Greece; Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research Institute - FORTH, University Campus of Ioannina, GR 45110, Ioannina, Greece
| | - George Rigas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110, Ioannina, Greece; Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research Institute - FORTH, University Campus of Ioannina, GR 45110, Ioannina, Greece
| | - Lambros Athanasiou
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, United States
| | - Panagiotis Siogkas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110, Ioannina, Greece; Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research Institute - FORTH, University Campus of Ioannina, GR 45110, Ioannina, Greece
| | - Panagiota Tsompou
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110, Ioannina, Greece
| | | | - Dominik C Benz
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, 8091, Zurich, Switzerland
| | - Ronny Buechel
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, 8091, Zurich, Switzerland
| | - Pedro A Lemos
- Dept. of Interventional Cardiology, Heart Institute, University of São Paulo Medical School, São Paulo-SP, 05403-000, Brazil; Dept. of Interventional Cardiology, Hospital Israelita Albert Einstein, Sao Paulo-SP, 05652-000, Brazil
| | - Gualtiero Pelosi
- Institute of Clinical Physiology, National Research Council, Pisa, IT 56124, Italy
| | - Lampros K Michalis
- Dept. of Interventional Cardiology, Medical School, University of Ioannina, GR 45110, Ioannina, Greece
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110, Ioannina, Greece; Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research Institute - FORTH, University Campus of Ioannina, GR 45110, Ioannina, Greece.
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Sakellarios A, Siogkas P, Georga E, Tachos N, Kigka V, Tsompou P, Andrikos I, Karanasiou GS, Rocchiccioli S, Correia J, Pelosi G, Stofella P, Filipovic N, Parodi O, Fotiadis DI. A Clinical Decision Support Platform for the Risk Stratification, Diagnosis, and Prediction of Coronary Artery Disease Evolution. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:4556-4559. [PMID: 30441365 DOI: 10.1109/embc.2018.8513131] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
SMARTool aims to perform accurate risk stratification of coronary artery disease patients as well as to provide early diagnosis and prediction of disease progression. This is achieved by the acquisition of data from about 263 patients including computed tomography angiographic images, clinical, molecular, biohumoral, exposome, inflammatory and omics data. Data are collected in two time points with a followup period of approximately 5 years. In the first step, data mining techniques are implemented for the estimation of risk stratification. In the next step, patients, who are classified as medium to high risk are considered for coronary imaging and computational modelling of blood flow, plaque growth and stenosis severity assessment. Additionally, patients with increased stenosis are selected for stent deployment. All the above modules are integrated in a cloud-based platform for the clinical decision support (CDSS) of patients with coronary artery disease. The work presents preliminary results employing the SMARTool dataset as well as the concept and architecture of the under development platform.
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Sakellarios A, Bourantas C, Papadopoulou SL, Kitslaar P, Girasis C, Stone G, Reiber J, Michalis L, Serruys P, de Feyter P, Garcia-Garcia H, Fotiadis D. The effect of coronary bifurcation and haemodynamics in prediction of atherosclerotic plaque development: a serial computed tomographic coronary angiographic study. EUROINTERVENTION 2017; 13:e1084-e1091. [DOI: 10.4244/eij-d-16-00929] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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15
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Sakellarios AI, Rigas G, Kigka V, Siogkas P, Tsompou P, Karanasiou G, Exarchos T, Andrikos I, Tachos N, Pelosi G, Parodi O, Fotiaids DI. SMARTool: A tool for clinical decision support for the management of patients with coronary artery disease based on modeling of atherosclerotic plaque process. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:96-99. [PMID: 29059819 DOI: 10.1109/embc.2017.8036771] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
SMARTool aims to the development of a clinical decision support system (CDSS) for the management and stratification of patients with coronary artery disease (CAD). This will be achieved by performing computational modeling of the main processes of atherosclerotic plaque growth. More specifically, computed tomography coronary angiography (CTCA) is acquired and 3-dimensional (3D) reconstruction is performed for the arterial trees. Then, blood flow and plaque growth modeling is employed simulating the major processes of atherosclerosis, such as the estimation of endothelial shear stress (ESS), the lipids transportation, low density lipoprotein (LDL) oxidation, macrophages migration and plaque development. The plaque growth model integrates information from genetic and biological data of the patients. The SMARTool system enables also the calculation of the virtual functional assessment index (vFAI), an index equivalent to the invasively measured fractional flow reserve (FFR), to provide decision support for patients with stenosed arteries. Finally, it integrates modeling of stent deployment. In this work preliminary results are presented. More specifically, the reconstruction methodology has mean value of Dice Coefficient and Hausdorff Distance is 0.749 and 1.746, respectively, while low ESS and high LDL concentration can predict plaque progression.
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Patel K, Tarkin J, Serruys PW, Tenekecioglu E, Foin N, Zhang YJ, Crake T, Moon J, Mathur A, Bourantas CV. Invasive or non-invasive imaging for detecting high-risk coronary lesions? Expert Rev Cardiovasc Ther 2017; 15:165-179. [PMID: 28256179 DOI: 10.1080/14779072.2017.1297231] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Advances in our understanding about atherosclerotic evolution have enabled us to identify specific plaque characteristics that are associated with coronary plaque vulnerability and cardiovascular events. With constant improvements in signal and image processing an arsenal of invasive and non-invasive imaging modalities have been developed that are capable of identifying these features allowing in vivo assessment of plaque vulnerability. Areas covered: This review article presents the available and emerging imaging modalities introduced to assess plaque morphology and biology, describes the evidence from the first large scale studies that evaluated the efficacy of invasive and non-invasive imaging in detecting lesions that are likely to progress and cause cardiovascular events and discusses the potential implications of the in vivo assessment of coronary artery pathology in the clinical setting. Expert commentary: Invasive imaging, with its high resolution, and in particular hybrid intravascular imaging appears as the ideal approach to study the mechanisms regulating atherosclerotic disease progression; whereas non-invasive imaging is expected to enable complete assessment of coronary tree pathology, detection of high-risk lesions, more accurate risk stratification and thus to allow a personalized treatment of vulnerable patients.
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Affiliation(s)
- Kush Patel
- a Barts Heart Centre, Barts Health NHS Trust , London , UK
| | - Jason Tarkin
- a Barts Heart Centre, Barts Health NHS Trust , London , UK.,b Division of Cardiovascular Medicine , University of Cambridge , Cambridge , UK
| | - Patrick W Serruys
- c Thoraxcenter , Erasmus Medical Centre , Rotterdam , The Netherlands.,d Faculty of Medicine , National Heart & Lung Institute, Imperial College , London , UK
| | | | - Nicolas Foin
- e National Heart Centre Singapore , Duke-NUS Medical School , Singapore
| | - Yao-Jun Zhang
- f Nanjing First Hospital , Nanjing Medical University , Nanjing , China
| | - Tom Crake
- a Barts Heart Centre, Barts Health NHS Trust , London , UK
| | - James Moon
- a Barts Heart Centre, Barts Health NHS Trust , London , UK
| | - Anthony Mathur
- a Barts Heart Centre, Barts Health NHS Trust , London , UK
| | - Christos V Bourantas
- a Barts Heart Centre, Barts Health NHS Trust , London , UK.,g Institute of Cardiovascular Sciences , University College London , London , UK
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Collet C, Sotomi Y, Cavalcante R, Asano T, Miyazaki Y, Tenekecioglu E, Kistlaar P, Zeng Y, Suwanasson P, de Winter RJ, Nieman K, Serruys PW, Onuma Y. Accuracy of coronary computed tomography angiography for bioresorbable scaffold luminal investigation: a comparison with optical coherence tomography. Int J Cardiovasc Imaging 2016; 33:431-439. [DOI: 10.1007/s10554-016-1018-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 11/08/2016] [Indexed: 11/28/2022]
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18
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Sakellarios AI, Raber L, Bourantas CV, Exarchos TP, Athanasiou LS, Pelosi G, Koskinas KC, Parodi O, Naka KK, Michalis LK, Serruys PW, Garcia-Garcia HM, Windecker S, Fotiadis DI. Prediction of Atherosclerotic Plaque Development in an In Vivo Coronary Arterial Segment Based on a Multilevel Modeling Approach. IEEE Trans Biomed Eng 2016; 64:1721-1730. [PMID: 28113248 DOI: 10.1109/tbme.2016.2619489] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The aim of this study is to explore major mechanisms of atherosclerotic plaque growth, presenting a proof-of-concept numerical model. METHODS To this aim, a human reconstructed left circumflex coronary artery is utilized for a multilevel modeling approach. More specifically, the first level consists of the modeling of blood flow and endothelial shear stress (ESS) computation. The second level includes the modeling of low-density lipoprotein (LDL) and high-density lipoprotein and monocytes transport through the endothelial membrane to vessel wall. The third level comprises of the modeling of LDL oxidation, macrophages differentiation, and foam cells formation. All modeling levels integrate experimental findings to describe the major mechanisms that occur in the arterial physiology. In order to validate the proposed approach, we utilize a patient specific scenario by comparing the baseline computational results with the changes in arterial wall thickness, lumen diameter, and plaque components using follow-up data. RESULTS The results of this model show that ESS and LDL concentration have a good correlation with the changes in plaque area [R2 = 0.365 (P = 0.029, adjusted R2 = 0.307) and R2 = 0.368 (P = 0.015, adjusted R2 = 0.342), respectively], whereas the introduction of the variables of oxidized LDL, macrophages, and foam cells as independent predictors improves the accuracy in predicting regions potential for atherosclerotic plaque development [R2 = 0.847 (P = 0.009, adjusted R2 = 0.738)]. CONCLUSION Advanced computational models can be used to increase the accuracy to predict regions which are prone to plaque development. SIGNIFICANCE Atherosclerosis is one of leading causes of death worldwide. For this purpose computational models have to be implemented to predict disease progression.
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Sakellarios A, Bourantas CV, Papadopoulou SL, Tsirka Z, de Vries T, Kitslaar PH, Girasis C, Naka KK, Fotiadis DI, Veldhof S, Stone GW, Reiber JHC, Michalis LK, Serruys PW, de Feyter PJ, Garcia-Garcia HM. Prediction of atherosclerotic disease progression using LDL transport modelling: a serial computed tomographic coronary angiographic study. Eur Heart J Cardiovasc Imaging 2016; 18:11-18. [PMID: 26985077 DOI: 10.1093/ehjci/jew035] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Accepted: 02/10/2016] [Indexed: 12/15/2022] Open
Abstract
AIM To investigate the efficacy of low-density lipoprotein (LDL) transport simulation in reconstructed arteries derived from computed tomography coronary angiography (CTCA) to predict coronary segments that are prone to progress. METHODS AND RESULTS Thirty-two patients admitted with an acute coronary event who underwent 64-slice CTCA after percutaneous coronary intervention and at 3-year follow-up were included in the analysis. The CTCA data were used to reconstruct the coronary anatomy of the untreated vessels at baseline and follow-up, and LDL transport simulation was performed in the baseline models. The computed endothelial shear stress (ESS), LDL concentration, and CTCA-derived plaque characteristics were used to identify predictors of substantial disease progression (defined as an increase in the plaque burden at follow-up higher than two standard deviations of the intra-observer variability of the expert who performed the analysis). Fifty-eight vessels were analysed. High LDL concentration [odds ratio (OR): 2.16; 95% confidence interval (CI): 1.64-2.84; P = 0.0054], plaque burden (OR: 1.40; 95% CI: 1.13-1.72; P = 0.0017), and plaque area (OR: 3.46; 95% CI: 2.20-5.44; P≤ 0.0001) were independent predictors of a substantial disease progression at follow-up. The ESS appears as a predictor of disease progression in univariate analysis but was not an independent predictor when the LDL concentration was entered into the multivariate model. The accuracy of the model that included the LDL concentration was higher than the accuracy of the model that included the ESS (65.1 vs. 62.5%). CONCLUSIONS LDL transport modelling appears a better predictor of atherosclerotic disease progression than the ESS, and combined with the atheroma characteristics provided by CTCA is able to detect with a moderate accuracy segments that will exhibit a significant plaque burden increase at mid-term follow-up.
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Affiliation(s)
- Antonis Sakellarios
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Christos V Bourantas
- Department of Cardiovascular Sciences, University College London, London, UK.,Department of Cardiology, Barts Health NHS Foundation Trust, London, UK
| | - Stella-Lida Papadopoulou
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Zeta Tsirka
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Ton de Vries
- Department of Interventional Cardiology, Erasmus University Medical Centre, Thoraxcenter, z120 Dr Molerwaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Pieter H Kitslaar
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Chrysafios Girasis
- Department of Interventional Cardiology, Erasmus University Medical Centre, Thoraxcenter, z120 Dr Molerwaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Katerina K Naka
- Department of Cardiology, Medical School, University of Ioannina, Ioannina, Greece
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | | | - Greg W Stone
- Columbia University Medical Center, New York, NY, USA
| | - Johan H C Reiber
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Lampros K Michalis
- Department of Cardiology, Medical School, University of Ioannina, Ioannina, Greece
| | - Patrick W Serruys
- Department of Interventional Cardiology, Erasmus University Medical Centre, Thoraxcenter, z120 Dr Molerwaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Pim J de Feyter
- Department of Interventional Cardiology, Erasmus University Medical Centre, Thoraxcenter, z120 Dr Molerwaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Hector M Garcia-Garcia
- Department of Interventional Cardiology, Erasmus University Medical Centre, Thoraxcenter, z120 Dr Molerwaterplein 40, 3015 GD Rotterdam, The Netherlands
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Bourantas CV, Garcia-Garcia HM, Torii R, Zhang YJ, Westwood M, Crake T, Serruys PW. Vulnerable plaque detection: an unrealistic quest or a feasible objective with a clinical value? Heart 2016; 102:581-9. [DOI: 10.1136/heartjnl-2015-309060] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2015] [Accepted: 12/14/2015] [Indexed: 01/03/2023] Open
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