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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] [What about the content of this article? (0)] [Affiliation(s)] [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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van de Leur RR, van Sleuwen MTGM, Zwetsloot PPM, van der Harst P, Doevendans PA, Hassink RJ, van Es R. Automatic triage of twelve-lead electrocardiograms using deep convolutional neural networks: a first implementation study. Eur Heart J Digit Health 2024; 5:89-96. [PMID: 38264701 PMCID: PMC10802816 DOI: 10.1093/ehjdh/ztad070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 10/10/2023] [Accepted: 11/07/2023] [Indexed: 01/25/2024]
Abstract
Aims Expert knowledge to correctly interpret electrocardiograms (ECGs) is not always readily available. An artificial intelligence (AI)-based triage algorithm (DELTAnet), able to support physicians in ECG prioritization, could help reduce current logistic burden of overreading ECGs and improve time to treatment for acute and life-threatening disorders. However, the effect of clinical implementation of such AI algorithms is rarely investigated. Methods and results Adult patients at non-cardiology departments who underwent ECG testing as a part of routine clinical care were included in this prospective cohort study. DELTAnet was used to classify 12-lead ECGs into one of the following triage classes: normal, abnormal not acute, subacute, and acute. Performance was compared with triage classes based on the final clinical diagnosis. Moreover, the associations between predicted classes and clinical outcomes were investigated. A total of 1061 patients and ECGs were included. Performance was good with a mean concordance statistic of 0.96 (95% confidence interval 0.95-0.97) when comparing DELTAnet with the clinical triage classes. Moreover, zero ECGs that required a change in policy or referral to the cardiologist were missed and there was a limited number of cases predicted as acute that did not require follow-up (2.6%). Conclusion This study is the first to prospectively investigate the impact of clinical implementation of an ECG-based AI triage algorithm. It shows that DELTAnet is efficacious and safe to be used in clinical practice for triage of 12-lead ECGs in non-cardiology hospital departments.
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Affiliation(s)
- Rutger R van de Leur
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Meike T G M van Sleuwen
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Peter-Paul M Zwetsloot
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Pieter A Doevendans
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
- Central Military Hospital, Utrecht, The Netherlands
| | - Rutger J Hassink
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - René van Es
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
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王 星, 李 茜, 马 彩, 张 铄, 林 钰, 李 建, 刘 澄. [Artificial intelligence in wearable electrocardiogram monitoring]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2023; 40:1084-1092. [PMID: 38151930 PMCID: PMC10753313 DOI: 10.7507/1001-5515.202301032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 10/31/2023] [Indexed: 12/29/2023]
Abstract
Electrocardiogram (ECG) monitoring owns important clinical value in diagnosis, prevention and rehabilitation of cardiovascular disease (CVD). With the rapid development of Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI) and other advanced technologies, wearable ECG is playing an increasingly important role. With the aging process of the population, it is more and more urgent to upgrade the diagnostic mode of CVD. Using AI technology to assist the clinical analysis of long-term ECGs, and thus to improve the ability of early detection and prediction of CVD has become an important direction. Intelligent wearable ECG monitoring needs the collaboration between edge and cloud computing. Meanwhile, the clarity of medical scene is conducive for the precise implementation of wearable ECG monitoring. This paper first summarized the progress of AI-related ECG studies and the current technical orientation. Then three cases were depicted to illustrate how the AI in wearable ECG cooperate with the clinic. Finally, we demonstrated the two core issues-the reliability and worth of AI-related ECG technology and prospected the future opportunities and challenges.
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Affiliation(s)
- 星尧 王
- 东南大学 仪器科学与工程学院(南京 210096)School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 生物电子学国家重点实验室(南京 210096)State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China
| | - 茜 李
- 东南大学 仪器科学与工程学院(南京 210096)School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 生物电子学国家重点实验室(南京 210096)State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China
| | - 彩云 马
- 东南大学 仪器科学与工程学院(南京 210096)School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 生物电子学国家重点实验室(南京 210096)State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China
| | - 铄 张
- 东南大学 仪器科学与工程学院(南京 210096)School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 生物电子学国家重点实验室(南京 210096)State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China
| | - 钰洁 林
- 东南大学 仪器科学与工程学院(南京 210096)School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 生物电子学国家重点实验室(南京 210096)State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China
| | - 建清 李
- 东南大学 仪器科学与工程学院(南京 210096)School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 生物电子学国家重点实验室(南京 210096)State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China
| | - 澄玉 刘
- 东南大学 仪器科学与工程学院(南京 210096)School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 生物电子学国家重点实验室(南京 210096)State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, P. R. China
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Seoni S, Jahmunah V, Salvi M, Barua PD, Molinari F, Acharya UR. Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013-2023). Comput Biol Med 2023; 165:107441. [PMID: 37683529 DOI: 10.1016/j.compbiomed.2023.107441] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023]
Abstract
Uncertainty estimation in healthcare involves quantifying and understanding the inherent uncertainty or variability associated with medical predictions, diagnoses, and treatment outcomes. In this era of Artificial Intelligence (AI) models, uncertainty estimation becomes vital to ensure safe decision-making in the medical field. Therefore, this review focuses on the application of uncertainty techniques to machine and deep learning models in healthcare. A systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our analysis revealed that Bayesian methods were the predominant technique for uncertainty quantification in machine learning models, with Fuzzy systems being the second most used approach. Regarding deep learning models, Bayesian methods emerged as the most prevalent approach, finding application in nearly all aspects of medical imaging. Most of the studies reported in this paper focused on medical images, highlighting the prevalent application of uncertainty quantification techniques using deep learning models compared to machine learning models. Interestingly, we observed a scarcity of studies applying uncertainty quantification to physiological signals. Thus, future research on uncertainty quantification should prioritize investigating the application of these techniques to physiological signals. Overall, our review highlights the significance of integrating uncertainty techniques in healthcare applications of machine learning and deep learning models. This can provide valuable insights and practical solutions to manage uncertainty in real-world medical data, ultimately improving the accuracy and reliability of medical diagnoses and treatment recommendations.
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Affiliation(s)
- Silvia Seoni
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | | | - Massimo Salvi
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, QLD, 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
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Chen B, Javadi G, Hamilton A, Sibley S, Laird P, Abolmaesumi P, Maslove D, Mousavi P. Quantifying deep neural network uncertainty for atrial fibrillation detection with limited labels. Sci Rep 2022; 12:20140. [PMID: 36418604 PMCID: PMC9684456 DOI: 10.1038/s41598-022-24574-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 11/17/2022] [Indexed: 11/25/2022] Open
Abstract
Atrial fibrillation (AF) is the most common arrhythmia found in the intensive care unit (ICU), and is associated with many adverse outcomes. Effective handling of AF and similar arrhythmias is a vital part of modern critical care, but obtaining knowledge about both disease burden and effective interventions often requires costly clinical trials. A wealth of continuous, high frequency physiological data such as the waveforms derived from electrocardiogram telemetry are promising sources for enriching clinical research. Automated detection using machine learning and in particular deep learning has been explored as a solution for processing these data. However, a lack of labels, increased presence of noise, and inability to assess the quality and trustworthiness of many machine learning model predictions pose challenges to interpretation. In this work, we propose an approach for training deep AF models on limited, noisy data and report uncertainty in their predictions. Using techniques from the fields of weakly supervised learning, we leverage a surrogate model trained on non-ICU data to create imperfect labels for a large ICU telemetry dataset. We combine these weak labels with techniques to estimate model uncertainty without the need for extensive human data annotation. AF detection models trained using this process demonstrated higher classification performance (0.64-0.67 F1 score) and improved calibration (0.05-0.07 expected calibration error).
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Affiliation(s)
- Brian Chen
- grid.410356.50000 0004 1936 8331School of Computing, Queen’s University, Kingston, ON Canada
| | - Golara Javadi
- grid.17091.3e0000 0001 2288 9830Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC Canada
| | - Alexander Hamilton
- grid.410356.50000 0004 1936 8331School of Computing, Queen’s University, Kingston, ON Canada
| | - Stephanie Sibley
- grid.410356.50000 0004 1936 8331Department of Critical Care Medicine, Queen’s University, Kingston, ON Canada
| | - Philip Laird
- grid.410356.50000 0004 1936 8331Department of Critical Care Medicine, Queen’s University, Kingston, ON Canada
| | - Purang Abolmaesumi
- grid.17091.3e0000 0001 2288 9830Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC Canada
| | - David Maslove
- grid.410356.50000 0004 1936 8331Department of Critical Care Medicine, Queen’s University, Kingston, ON Canada
| | - Parvin Mousavi
- grid.410356.50000 0004 1936 8331School of Computing, Queen’s University, Kingston, ON Canada
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Fooladgar F, Jamzad A, Connolly L, Santilli A, Kaufmann M, Ren K, Abolmaesumi P, Rudan JF, McKay D, Fichtinger G, Mousavi P. Uncertainty estimation for margin detection in cancer surgery using mass spectrometry. Int J Comput Assist Radiol Surg 2022. [PMID: 36175747 DOI: 10.1007/s11548-022-02764-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 09/19/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technology for clinical margin detection. Deployment of REIMS depends on construction of reliable deep learning models that can categorize tissue according to its metabolomic signature. Challenges associated with developing these models include the presence of noise during data acquisition and the variance in tissue signatures between patients. In this study, we propose integration of uncertainty estimation in deep models to factor predictive confidence into margin detection in cancer surgery. METHODS iKnife is used to collect 693 spectra of cancer and healthy samples acquired from 91 patients during basal cell carcinoma resection. A Bayesian neural network and two baseline models are trained on these data to perform classification as well as uncertainty estimation. The samples with high estimated uncertainty are then removed, and new models are trained using the clean data. The performance of proposed and baseline models, with different ratios of filtered data, is then compared. RESULTS The data filtering does not improve the performance of the baseline models as they cannot provide reliable estimations of uncertainty. In comparison, the proposed model demonstrates a statistically significant improvement in average balanced accuracy (75.2%), sensitivity (74.1%) and AUC (82.1%) after removing uncertain training samples. We also demonstrate that if highly uncertain samples are predicted and removed from the test data, sensitivity further improves to 88.2%. CONCLUSIONS This is the first study that applies uncertainty estimation to inform model training and deployment for tissue recognition in cancer surgery. Uncertainty estimation is leveraged in two ways: by factoring a measure of input noise in training the models and by including predictive confidence in reporting the outputs. We empirically show that considering uncertainty for model development can help improve the overall accuracy of a margin detection system using REIMS.
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Hong S, Zhang W, Sun C, Zhou Y, Li H. Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020. Front Physiol 2022; 12:811661. [PMID: 35095568 PMCID: PMC8795785 DOI: 10.3389/fphys.2021.811661] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/21/2021] [Indexed: 11/13/2022] Open
Abstract
Cardiovascular diseases (CVDs) are one of the most fatal disease groups worldwide. Electrocardiogram (ECG) is a widely used tool for automatically detecting cardiac abnormalities, thereby helping to control and manage CVDs. To encourage more multidisciplinary researches, PhysioNet/Computing in Cardiology Challenge 2020 (Challenge 2020) provided a public platform involving multi-center databases and automatic evaluations for ECG classification tasks. As a result, 41 teams successfully submitted their solutions and were qualified for rankings. Although Challenge 2020 was a success, there has been no in-depth methodological meta-analysis of these solutions, making it difficult for researchers to benefit from the solutions and results. In this study, we aim to systematically review the 41 solutions in terms of data processing, feature engineering, model architecture, and training strategy. For each perspective, we visualize and statistically analyze the effectiveness of the common techniques, and discuss the methodological advantages and disadvantages. Finally, we summarize five practical lessons based on the aforementioned analysis: (1) Data augmentation should be employed and adapted to specific scenarios; (2) Combining different features can improve performance; (3) A hybrid design of different types of deep neural networks (DNNs) is better than using a single type; (4) The use of end-to-end architectures should depend on the task being solved; (5) Multiple models are better than one. We expect that our meta-analysis will help accelerate the research related to ECG classification based on machine-learning models.
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Affiliation(s)
- Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Wenrui Zhang
- Department of Mathematics, National University of Singapore, Singapore, Singapore
| | - Chenxi Sun
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China
- School of Electronics Engineering and Computer Science, Peking University, Beijing, China
| | - Yuxi Zhou
- School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China
- RIIT, TNList, Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Hongyan Li
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China
- School of Electronics Engineering and Computer Science, Peking University, Beijing, China
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Abstract
Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.
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Affiliation(s)
- Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - David M Harmon
- Department of Internal Medicine, Mayo Clinic School of Graduate Medical Education, 200 First Street SW, Rochester, MN 55905, USA
| | - Elijah R Behr
- Cardiology Research Center and Cardiovascular Clinical Academic Group, Molecular and Clinical Sciences Institute, St. George’s University of London and St. George’s University Hospitals NHS Foundation Trust, Blackshaw Rd, London SW17 0QT, UK
- Mayo Clinic Healthcare, 15 Portland Pl, London W1B 1PT, UK
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
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de Jaegere P, Lumens J, Bruining N. The 12-lead surface electrocardiogram: a sheet of paper or a realm of concealed information asking for deep learning analysis. Eur Heart J Digit Health 2021; 2:356-357. [PMID: 36713605 PMCID: PMC9708034 DOI: 10.1093/ehjdh/ztab066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Indexed: 02/01/2023]
Affiliation(s)
- Peter de Jaegere
- Department of Cardiology, Erasmus MC, Dr Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Joost Lumens
- CARIM School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Nico Bruining
- Department of Cardiology, Erasmus MC, Dr Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
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