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Lisi C, Catapano F, Brilli F, Scialò V, Corghi E, Figliozzi S, Cozzi OF, Monti L, Stefanini GG, Francone M. CT imaging post-TAVI: Murphy's first law in action-preparing to recognize the unexpected. Insights Imaging 2024; 15:157. [PMID: 38900378 PMCID: PMC11189851 DOI: 10.1186/s13244-024-01729-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 05/16/2024] [Indexed: 06/21/2024] Open
Abstract
Transfemoral aortic valve implantation (TAVI) has been long considered the standard of therapy for high-risk patients with severe aortic-stenosis and is now effectively employed in place of surgical aortic valve replacement also in intermediate-risk patients. The potential lasting consequences of minor complications, which might have limited impact on elderly patients, could be more noteworthy in the longer term when occurring in younger individuals. That's why a greater focus on early diagnosis, correct management, and prevention of post-procedural complications is key to achieve satisfactory results. ECG-triggered multidetector computed tomography angiography (CTA) is the mainstay imaging modality for pre-procedural planning of TAVI and is also used for post-interventional early detection of both acute and long-term complications. CTA allows detailed morphological analysis of the valve and its movement throughout the entire cardiac cycle. Moreover, stent position, coronary artery branches, and integrity of the aortic root can be precisely evaluated. Imaging reliability implies the correct technical setting of the computed tomography scan, knowledge of valve type, normal post-interventional findings, and awareness of classic and life-threatening complications after a TAVI procedure. This educational review discusses the main post-procedural complications of TAVI with a specific imaging focus, trying to clearly describe the technical aspects of CTA Imaging in post-TAVI and its clinical applications and challenges, with a final focus on future perspectives and emerging technologies. CRITICAL RELEVANCE STATEMENT: This review undertakes an analysis of the role computed tomography angiography (CTA) plays in the assessment of post-TAVI complications. Highlighting the educational issues related to the topic, empowers radiologists to refine their clinical approach, contributing to enhanced patient care. KEY POINTS: Prompt recognition of TAVI complications, ranging from value issues to death, is crucial. Adherence to recommended scanning protocols, and the optimization of tailored protocols, is essential. CTA is central in the diagnosis of TAVI complications and functions as a gatekeeper to treatment.
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Affiliation(s)
- Costanza Lisi
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20090, Milan, Pieve Emanuele, Italy
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Milan, Rozzano, Italy
| | - Federica Catapano
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20090, Milan, Pieve Emanuele, Italy.
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Milan, Rozzano, Italy.
| | - Federica Brilli
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20090, Milan, Pieve Emanuele, Italy
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Milan, Rozzano, Italy
| | - Vincenzo Scialò
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20090, Milan, Pieve Emanuele, Italy
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Milan, Rozzano, Italy
| | - Eleonora Corghi
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20090, Milan, Pieve Emanuele, Italy
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Milan, Rozzano, Italy
| | - Stefano Figliozzi
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Milan, Rozzano, Italy
| | - Ottavia Francesca Cozzi
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20090, Milan, Pieve Emanuele, Italy
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Milan, Rozzano, Italy
| | - Lorenzo Monti
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Milan, Rozzano, Italy
| | - Giulio Giuseppe Stefanini
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20090, Milan, Pieve Emanuele, Italy
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Milan, Rozzano, Italy
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20090, Milan, Pieve Emanuele, Italy
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Milan, Rozzano, Italy
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Jacquemyn X, Van Onsem E, Dufendach K, Brown JA, Kliner D, Toma C, Serna-Gallegos D, Sá MP, Sultan I. Machine-learning approaches for risk prediction in transcatheter aortic valve implantation: Systematic review and meta-analysis. J Thorac Cardiovasc Surg 2024:S0022-5223(24)00448-3. [PMID: 38815806 DOI: 10.1016/j.jtcvs.2024.05.017] [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: 04/30/2024] [Revised: 05/20/2024] [Accepted: 05/21/2024] [Indexed: 06/01/2024]
Abstract
OBJECTIVES With the expanding integration of artificial intelligence (AI) and machine learning (ML) into the structural heart domain, numerous ML models have emerged for the prediction of adverse outcomes after transcatheter aortic valve implantation (TAVI). We aim to identify, describe, and critically appraise ML prediction models for adverse outcomes after TAVI. Key objectives consisted in summarizing model performance, evaluating adherence to reporting guidelines, and transparency. METHODS We searched PubMed, SCOPUS, and Embase through August 2023. We selected published machine learning models predicting TAVI outcomes. Two reviewers independently screened articles, extracted data, and assessed the study quality according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Outcomes included summary C-statistics and model risk of bias assessed with the Prediction Model Risk of Bias Assessment Tool. C-statistics were pooled using a random-effects model. RESULTS Twenty-one studies (118,153 patients) employing various ML algorithms (76 models) were included in the systematic review. Predictive ability of models varied: 11.8% inadequate (C-statistic <0.60), 26.3% adequate (C-statistic 0.60-0.70), 31.6% acceptable (C-statistic 0.70-0.80), and 30.3% demonstrated excellent (C-statistic >0.80) performance. Meta-analyses revealed excellent predictive performance for early mortality (C-statistic: 0.81; 95% confidence interval [CI], 0.65-0.91), acceptable performance for 1-year mortality (C-statistic: 0.76; 95% CI, 0.67-0.84), and acceptable performance for predicting permanent pacemaker implantation (C-statistic: 0.75; 95% CI, 0.51-0.90). CONCLUSIONS ML models for TAVI outcomes exhibit adequate-to-excellent performance, suggesting potential clinical utility. We identified concerns in methodology and transparency, emphasizing the need for improved scientific reporting standards.
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Affiliation(s)
- Xander Jacquemyn
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
| | | | - Keith Dufendach
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | - James A Brown
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | - Dustin Kliner
- UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa; Department of Interventional Cardiology, University of Pittsburgh, Pittsburgh, Pa
| | - Catalin Toma
- UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa; Department of Interventional Cardiology, University of Pittsburgh, Pittsburgh, Pa
| | - Derek Serna-Gallegos
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | - Michel Pompeu Sá
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | - Ibrahim Sultan
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
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Chrysostomidis G, Apostolos A, Papanikolaou A, Konstantinou K, Tsigkas G, Koliopoulou A, Chamogeorgakis T. The Application of Precision Medicine in Structural Heart Diseases: A Step towards the Future. J Pers Med 2024; 14:375. [PMID: 38673001 PMCID: PMC11051532 DOI: 10.3390/jpm14040375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/20/2024] [Accepted: 03/23/2024] [Indexed: 04/28/2024] Open
Abstract
The personalized applications of 3D printing in interventional cardiology and cardiac surgery represent a transformative paradigm in the management of structural heart diseases. This review underscores the pivotal role of 3D printing in enhancing procedural precision, from preoperative planning to procedural simulation, particularly in valvular heart diseases, such as aortic stenosis and mitral regurgitation. The ability to create patient-specific models contributes significantly to predicting and preventing complications like paravalvular leakage, ensuring optimal device selection, and improving outcomes. Additionally, 3D printing extends its impact beyond valvular diseases to tricuspid regurgitation and non-valvular structural heart conditions. The comprehensive synthesis of the existing literature presented here emphasizes the promising trajectory of individualized approaches facilitated by 3D printing, promising a future where tailored interventions based on precise anatomical considerations become standard practice in cardiovascular care.
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Affiliation(s)
- Grigorios Chrysostomidis
- Second Department of Adult Cardiac Surgery—Heart and Lung Transplantation, Onassis Cardiac Surgery Center, 176 74 Athens, Greece; (G.C.); (A.K.); (T.C.)
| | - Anastasios Apostolos
- First Department of Cardiology, National and Kapodistrian University of Athens, Hippocration General Hospital, 115 27 Athens, Greece;
| | - Amalia Papanikolaou
- First Department of Cardiology, National and Kapodistrian University of Athens, Hippocration General Hospital, 115 27 Athens, Greece;
| | - Konstantinos Konstantinou
- Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London 26504, UK;
| | - Grigorios Tsigkas
- Department of Cardiology, University Hospital of Patras, 265 04 Patras, Greece;
| | - Antigoni Koliopoulou
- Second Department of Adult Cardiac Surgery—Heart and Lung Transplantation, Onassis Cardiac Surgery Center, 176 74 Athens, Greece; (G.C.); (A.K.); (T.C.)
| | - Themistokles Chamogeorgakis
- Second Department of Adult Cardiac Surgery—Heart and Lung Transplantation, Onassis Cardiac Surgery Center, 176 74 Athens, Greece; (G.C.); (A.K.); (T.C.)
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Makimoto H, Kohro T. Adopting artificial intelligence in cardiovascular medicine: a scoping review. Hypertens Res 2024; 47:685-699. [PMID: 37907600 DOI: 10.1038/s41440-023-01469-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/03/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.
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Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
| | - Takahide Kohro
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan
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Aziz D, Maganti K, Yanamala N, Sengupta P. The Role of Artificial Intelligence in Echocardiography: A Clinical Update. Curr Cardiol Rep 2023; 25:1897-1907. [PMID: 38091196 DOI: 10.1007/s11886-023-02005-2] [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] [Accepted: 11/21/2023] [Indexed: 01/26/2024]
Abstract
PURPOSE OF REVIEW In echocardiography, there has been robust development of artificial intelligence (AI) tools for image recognition, automated measurements, image segmentation, and patient prognostication that has created a monumental shift in the study of AI and machine learning models. However, integrating these measurements into complex disease recognition and therapeutic interventions remains challenging. While the tools have been developed, there is a lack of evidence regarding implementing heterogeneous systems for guiding clinical decision-making and therapeutic action. RECENT FINDINGS Newer AI modalities have shown concrete positive data in terms of user-guided image acquisition and processing, precise determination of both basic and advanced quantitative echocardiographic features, and the potential to construct predictive models, all with the possibility of seamless integration into clinical decision support systems. AI in echocardiography is a powerful and ever-growing tool with the potential for revolutionary effects on the practice of cardiology. In this review article, we explore the growth of AI and its applications in echocardiography, along with clinical implications and the associated regulatory, legal, and ethical considerations.
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Affiliation(s)
- Daniel Aziz
- Department of Internal Medicine, Rutgers - Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Kameswari Maganti
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA
| | - Naveena Yanamala
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA
| | - Partho Sengupta
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, 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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7
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Winter PD, Chico TJA. Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) Framework to Identify Barriers and Facilitators for the Implementation of Digital Twins in Cardiovascular Medicine. SENSORS (BASEL, SWITZERLAND) 2023; 23:6333. [PMID: 37514627 PMCID: PMC10385429 DOI: 10.3390/s23146333] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/26/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023]
Abstract
A digital twin is a computer-based "virtual" representation of a complex system, updated using data from the "real" twin. Digital twins are established in product manufacturing, aviation, and infrastructure and are attracting significant attention in medicine. In medicine, digital twins hold great promise to improve prevention of cardiovascular diseases and enable personalised health care through a range of Internet of Things (IoT) devices which collect patient data in real-time. However, the promise of such new technology is often met with many technical, scientific, social, and ethical challenges that need to be overcome-if these challenges are not met, the technology is therefore less likely on balance to be adopted by stakeholders. The purpose of this work is to identify the facilitators and barriers to the implementation of digital twins in cardiovascular medicine. Using, the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, we conducted a document analysis of policy reports, industry websites, online magazines, and academic publications on digital twins in cardiovascular medicine, identifying potential facilitators and barriers to adoption. Our results show key facilitating factors for implementation: preventing cardiovascular disease, in silico simulation and experimentation, and personalised care. Key barriers to implementation included: establishing real-time data exchange, perceived specialist skills required, high demand for patient data, and ethical risks related to privacy and surveillance. Furthermore, the lack of empirical research on the attributes of digital twins by different research groups, the characteristics and behaviour of adopters, and the nature and extent of social, regulatory, economic, and political contexts in the planning and development process of these technologies is perceived as a major hindering factor to future implementation.
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Affiliation(s)
- Peter D Winter
- School of Sociology, Politics, and International Studies (SPAIS), University of Bristol, Bristol BS8 1TU, UK
| | - Timothy J A Chico
- Department of Infection, Immunity and Cardiovascular Disease (IICD), University of Sheffield, Sheffield S10 2RX, UK
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8
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Dahle G, Walther T. The 10 Commandments for Transapical TAVI. INNOVATIONS-TECHNOLOGY AND TECHNIQUES IN CARDIOTHORACIC AND VASCULAR SURGERY 2023; 18:223-228. [PMID: 37309902 PMCID: PMC10315863 DOI: 10.1177/15569845231177052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Gry Dahle
- Department of Cardiothoracic Surgery, Oslo University Hospital, Norway
| | - Thomas Walther
- Klinik für -Herz- und Gefässchirurgie, Universitätsklinikum Frankfurt, Germany
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9
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Coorey G, Figtree GA, Fletcher DF, Snelson VJ, Vernon ST, Winlaw D, Grieve SM, McEwan A, Yang JYH, Qian P, O'Brien K, Orchard J, Kim J, Patel S, Redfern J. The health digital twin to tackle cardiovascular disease-a review of an emerging interdisciplinary field. NPJ Digit Med 2022; 5:126. [PMID: 36028526 PMCID: PMC9418270 DOI: 10.1038/s41746-022-00640-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
Potential benefits of precision medicine in cardiovascular disease (CVD) include more accurate phenotyping of individual patients with the same condition or presentation, using multiple clinical, imaging, molecular and other variables to guide diagnosis and treatment. An approach to realising this potential is the digital twin concept, whereby a virtual representation of a patient is constructed and receives real-time updates of a range of data variables in order to predict disease and optimise treatment selection for the real-life patient. We explored the term digital twin, its defining concepts, the challenges as an emerging field, and potentially important applications in CVD. A mapping review was undertaken using a systematic search of peer-reviewed literature. Industry-based participants and patent applications were identified through web-based sources. Searches of Compendex, EMBASE, Medline, ProQuest and Scopus databases yielded 88 papers related to cardiovascular conditions (28%, n = 25), non-cardiovascular conditions (41%, n = 36), and general aspects of the health digital twin (31%, n = 27). Fifteen companies with a commercial interest in health digital twin or simulation modelling had products focused on CVD. The patent search identified 18 applications from 11 applicants, of which 73% were companies and 27% were universities. Three applicants had cardiac-related inventions. For CVD, digital twin research within industry and academia is recent, interdisciplinary, and established globally. Overall, the applications were numerical simulation models, although precursor models exist for the real-time cyber-physical system characteristic of a true digital twin. Implementation challenges include ethical constraints and clinical barriers to the adoption of decision tools derived from artificial intelligence systems.
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Affiliation(s)
- Genevieve Coorey
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia. .,The George Institute for Global Health, Sydney, NSW, Australia.
| | - Gemma A Figtree
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, NSW, Australia
| | - David F Fletcher
- University of Sydney, School of Chemical and Biomolecular Engineering, Sydney, NSW, Australia
| | - Victoria J Snelson
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Stephen Thomas Vernon
- Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, NSW, Australia.,Department of Cardiology, Royal North Shore Hospital, Sydney, NSW, Australia
| | - David Winlaw
- Cincinnati Children's Hospital Medical Cente, Cincinnati, OH, USA
| | - Stuart M Grieve
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Alistair McEwan
- The University of Sydney, School of Biomedical Engineering, Sydney, NSW, Australia
| | - Jean Yee Hwa Yang
- University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Pierre Qian
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Westmead Applied Research Centre, Westmead Hospital, Sydney, NSW, Australia
| | - Kieran O'Brien
- Siemens Healthcare Pty Ltd; and Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Jessica Orchard
- University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Jinman Kim
- University of Sydney, School of Computer Science, Sydney, NSW, Australia
| | - Sanjay Patel
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Royal Prince Alfred Hospital, Sydney, NSW, Australia.,Heart Research Institute, Sydney, NSW, Australia
| | - Julie Redfern
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia
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10
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Dowling C, Gooley R, McCormick L, Rashid HN, Dargan J, Khan F, Firoozi S, Brecker SJ. Patient-Specific Computer Simulation to Predict Conduction Disturbance With Current-Generation Self-Expanding Transcatheter Heart Valves. STRUCTURAL HEART : THE JOURNAL OF THE HEART TEAM 2022; 6:100010. [PMID: 37274548 PMCID: PMC10236875 DOI: 10.1016/j.shj.2022.100010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 01/19/2022] [Accepted: 02/02/2022] [Indexed: 10/18/2022]
Abstract
Background Patient-specific computer simulation may predict the development of conduction disturbance following transcatheter aortic valve replacement (TAVR). Validation of the computer simulations with current-generation devices has not been undertaken. Methods A retrospective study was performed on patients who had undergone TAVR with a current-generation self-expanding transcatheter heart valve (THV). Preprocedural computed tomography imaging was used to create finite element models of the aortic root. Procedural contrast angiography was reviewed, and finite element analysis performed using a matching THV device size and implantation depth. A region of interest corresponding to the atrioventricular bundle and proximal left bundle branch was identified. The percentage of this area (contact pressure index [CPI]) and maximum contact pressure (CPMax) exerted by THV were recorded. Postprocedural electrocardiograms were reviewed, and major conduction disturbance was defined as the development of persistent left bundle branch block or high-degree atrioventricular block. Results A total of 80 patients were included in the study. THVs were 23- to 29-mm Evolut PRO (n = 53) and 34-mm Evolut R (n = 27). Major conduction disturbance occurred in 27 patients (33.8%). CPI (28.3 ± 15.8 vs. 15.6 ± 11.2%; p < 0.001) and CPMax (0.51 ± 0.20 vs. 0.36 ± 0.24 MPa; p = 0.008) were higher in patients who developed major conduction disturbance. CPI (area under the receiver operating characteristic curve [AUC], 0.74; 95% CI, 0.63-0.86; p < 0.001) and CPMax (AUC, 0.69; 95% CI, 0.57-0.81; p = 0.006) demonstrated a discriminatory power to predict the development of major conduction disturbance. Conclusions Patient-specific computer simulation may identify patients at risk for conduction disturbance after TAVR with current-generation self-expanding THVs.
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Affiliation(s)
- Cameron Dowling
- MonashHeart, Monash Health and Monash Cardiovascular Research Centre, Monash University, Melbourne, Australia
| | - Robert Gooley
- MonashHeart, Monash Health and Monash Cardiovascular Research Centre, Monash University, Melbourne, Australia
| | - Liam McCormick
- MonashHeart, Monash Health and Monash Cardiovascular Research Centre, Monash University, Melbourne, Australia
| | - Hashrul N. Rashid
- MonashHeart, Monash Health and Monash Cardiovascular Research Centre, Monash University, Melbourne, Australia
| | - James Dargan
- Cardiovascular Clinical Academic Group, St. George’s, University of London and St. George’s University Hospitals NHS Foundation Trust, London, UK
| | - Faisal Khan
- Cardiovascular Clinical Academic Group, St. George’s, University of London and St. George’s University Hospitals NHS Foundation Trust, London, UK
| | - Sami Firoozi
- Cardiovascular Clinical Academic Group, St. George’s, University of London and St. George’s University Hospitals NHS Foundation Trust, London, UK
| | - Stephen J. Brecker
- Cardiovascular Clinical Academic Group, St. George’s, University of London and St. George’s University Hospitals NHS Foundation Trust, London, UK
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11
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On the Modeling of Transcatheter Therapies for the Aortic and Mitral Valves: A Review. PROSTHESIS 2022. [DOI: 10.3390/prosthesis4010011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Transcatheter aortic valve replacement (TAVR) has become a milestone for the management of aortic stenosis in a growing number of patients who are unfavorable candidates for surgery. With the new generation of transcatheter heart valves (THV), the feasibility of transcatheter mitral valve replacement (TMVR) for degenerated mitral bioprostheses and failed annuloplasty rings has been demonstrated. In this setting, computational simulations are modernizing the preoperative planning of transcatheter heart valve interventions by predicting the outcome of the bioprosthesis interaction with the human host in a patient-specific fashion. However, computational modeling needs to carry out increasingly challenging levels including the verification and validation to obtain accurate and realistic predictions. This review aims to provide an overall assessment of the recent advances in computational modeling for TAVR and TMVR as well as gaps in the knowledge limiting model credibility and reliability.
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OUP accepted manuscript. Cardiovasc Res 2022; 118:2037-2038. [DOI: 10.1093/cvr/cvac083] [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: 11/13/2022] Open
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