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Kong B, Fei H, Cheng S, Ma H, Yin H, Li M, Liu Q, Liu Y, Bai B, Liu F, Guo L, Geng Q. Mental Stress-Induced Myocardial Ischemia Detected by Global Longitudinal Strain and Quantitative Myocardial Contrast Echocardiography in Women With Nonobstructive Coronary Artery Disease. J Am Soc Echocardiogr 2024; 37:894-905. [PMID: 38761987 DOI: 10.1016/j.echo.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 05/03/2024] [Accepted: 05/04/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND The utility of radionuclide myocardial perfusion imaging including positron emission tomography (PET) for diagnosing mental stress-induced myocardial ischemia (MSIMI) is clinically restricted. This study aims to assess the diagnostic performance of novel echocardiographic techniques, including automated strain and quantitative myocardial contrast echocardiography (MCE) with dedicated software and deep neural network model, for MSIMI detection. The secondary objective was to explore the correlation between changes in myocardial blood flow and MSIMI. METHODS Seventy-two female patients ages 18 to 75 with angina and nonobstructive coronary artery disease (ANOCA) and 23 healthy controls were prospectively recruited. Both echocardiography with contrast agent and PET imaging were performed during structured mental stress testing. Mental stress-induced myocardial ischemia was defined as a summed difference score ≥3 on PET. Echocardiographic parameters including left ventricular global longitudinal strain, β, and A × β were obtained, and their trends during mental stress testing were observed. ΔGLS was defined as the ratio of difference between global longitudinal strain values at stress and rest to the rest data. β reserve and A×β reserve were respectively calculated. RESULTS Thirty-two ANOCA patients (44%) and 1 control (4%) were diagnosed with MSIMI (P < .01). For ANOCA patients with MSIMI, left ventricular GLS, β, and A × β declined to varied extents during mental stress testing compared with those without MSIMI and the controls (P < .05). Bland-Altman plots demonstrated good consistency between β reserve and A × β reserve output by the deep neural network model and iMCE software. Receiver operating characteristic curve analyses showed that ΔGLS, β reserve, and A × β reserve demonstrated favorable ability to predict MSIMI, especially the combination of A × β reserve using iMCE analysis and ΔGLS (area under the curve, 0.94; sensitivity, 83%; specificity, 97%). CONCLUSIONS Novel technologies in echocardiography exhibit the potential to be a clinical alternative to cardiac PET for effectively detecting MSIMI. Attenuated myocardial blood flow response during structured mental stress testing was correlated with MSIMI, providing a reasonable explanation for the chest discomfort persisting in ANOCA women.
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Affiliation(s)
- Bo Kong
- Department of Echocardiography, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Hongwen Fei
- Department of Echocardiography, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Shiyao Cheng
- Department of Cardiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huan Ma
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Han Yin
- Department of Cardiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Mingqi Li
- Department of Ultrasonography, Renmin Hospital of Wuhan University, Wuhan, China
| | - Quanjun Liu
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yuting Liu
- Department of Cardiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Bingqing Bai
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Fengyao Liu
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Lan Guo
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Qingshan Geng
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Department of Cardiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China.
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Cui R, Liang S, Zhao W, Liu Z, Lin Z, He W, He Y, Du C, Peng J, Huang H. A Shape-Consistent Deep-Learning Segmentation Architecture for Low-Quality and High-Interference Myocardial Contrast Echocardiography. ULTRASOUND IN MEDICINE & BIOLOGY 2024:S0301-5629(24)00235-7. [PMID: 39147622 DOI: 10.1016/j.ultrasmedbio.2024.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 05/31/2024] [Accepted: 06/04/2024] [Indexed: 08/17/2024]
Abstract
OBJECTIVE Myocardial contrast echocardiography (MCE) plays a crucial role in diagnosing ischemia, infarction, masses and other cardiac conditions. In the realm of MCE image analysis, accurate and consistent myocardial segmentation results are essential for enabling automated analysis of various heart diseases. However, current manual diagnostic methods in MCE suffer from poor repeatability and limited clinical applicability. MCE images often exhibit low quality and high noise due to the instability of ultrasound signals, while interference structures can further disrupt segmentation consistency. METHODS To overcome these challenges, we proposed a deep-learning network for the segmentation of MCE. This architecture leverages dilated convolutions to capture high-scale information without sacrificing positional accuracy and modifies multi-head self-attention to enhance global context and ensure consistency, effectively overcoming issues related to low image quality and interference. Furthermore, we also adapted the cascade application of transformers with convolutional neural networks for improved segmentation in MCE. RESULTS In our experiments, our architecture achieved the best Dice score of 84.35% for standard MCE views compared with that of several state-of-the-art segmentation models. For non-standard views and frames with interfering structures (mass), our models also attained the best Dice scores of 83.33% and 83.97%, respectively. CONCLUSION These studies proved that our architecture is of excellent shape consistency and robustness, which allows it to deal with segmentation of various types of MCE. Our relatively precise and consistent myocardial segmentation results provide fundamental conditions for the automated analysis of various heart diseases, with the potential to discover underlying pathological features and reduce healthcare costs.
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Affiliation(s)
- Rongpu Cui
- College of Computer Science, Sichuan University, Chengdu, China
| | - Shichu Liang
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China
| | - Weixin Zhao
- College of Computer Science, Sichuan University, Chengdu, China
| | - Zhiyue Liu
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhicheng Lin
- College of Computer Science, Sichuan University, Chengdu, China
| | - Wenfeng He
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yujun He
- College of Computer Science, Sichuan University, Chengdu, China
| | - Chaohui Du
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jian Peng
- College of Computer Science, Sichuan University, Chengdu, China.
| | - He Huang
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China
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Zhang J, Ma M, Li H, Pu Z, Liu H, Huang T, Cheng H, Gong Y, Chu Y, Wang Z, Jiang J, Xia L. Early diagnosis of coronary microvascular dysfunction by myocardial contrast stress echocardiography. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:7845-7858. [PMID: 37161175 DOI: 10.3934/mbe.2023339] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Coronary microvascular dysfunction (CMD) is one of the basic mechanisms of myocardial ischemia. Myocardial contrast echocardiography (MCE) is a bedside technique that utilises microbubbles which remain entirely within the intravascular space and denotes the status of microvascular perfusion within that region. Some pilot studies suggested that MCE may be used to diagnose CMD, but without further validation. This study is aimed to investigate the diagnostic performance of MCE for the evaluation of CMD. MCE was performed at rest and during adenosine triphosphate stress. ECG triggered real-time frames were acquired in the apical 4-chamber, 3-chamber, 2-chamber, and long-axis imaging planes. These images were imported into Narnar for further processing. Eighty-two participants with suspicion of coronary disease and absence of significant epicardial lesions were prospectively investigated. Thermodilution was used as the gold standard to diagnose CMD. CMD was present in 23 (28%) patients. Myocardial blood flow reserve (MBF) was assessed using MCE. CMD was defined as MBF reserve < 2. The MCE method had a high sensitivity (88.1%) and specificity (95.7%) in the diagnosis of CMD. There was strong agreement with thermodilution (Kappa coefficient was 0.727; 95% CI: 0.57-0.88, p < 0.001). However, the correlation coefficient (r = 0.376; p < 0.001) was not high.
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Affiliation(s)
- Jucheng Zhang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou 310009, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou 310009, China
| | - Minwen Ma
- Department of Clinical Engineering, School of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou 310009, China
| | - Huajun Li
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou 310009, China
| | - Zhaoxia Pu
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou 310009, China
| | - Haipeng Liu
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
| | - Tianhai Huang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou 310009, China
| | - Huan Cheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Yinglan Gong
- Institute of Wenzhou, Zhejiang University, Wenzhou 325036, China
| | - Yonghua Chu
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou 310009, China
| | - Zhikang Wang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou 310009, China
| | - Jun Jiang
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou 310009, China
| | - Ling Xia
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
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Cheng H, Zhang J, Gong Y, Pu Z, Jiang J, Chu Y, Xia L. Semantic segmentation method for myocardial contrast echocardiogram based on DeepLabV3+ deep learning architecture. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:2081-2093. [PMID: 36899523 DOI: 10.3934/mbe.2023096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Myocardial contrast echocardiography (MCE) has been proposed as a method to assess myocardial perfusion for the detection of coronary artery diseases in a non-invasive way. As a critical step of automatic MCE perfusion quantification, myocardium segmentation from the MCE frames faces many challenges due to the low image quality and complex myocardial structure. In this paper, a deep learning semantic segmentation method is proposed based on a modified DeepLabV3+ structure with an atrous convolution and atrous spatial pyramid pooling module. The model was trained separately on three chamber views (apical two-chamber view, apical three-chamber view, and apical four-chamber view) on 100 patients' MCE sequences, divided by a proportion of 7:3 into training and testing datasets. The results evaluated by using the dice coefficient (0.84, 0.84, and 0.86 for three chamber views respectively) and Intersection over Union(0.74, 0.72 and 0.75 for three chamber views respectively) demonstrated the better performance of the proposed method compared to other state-of-the-art methods, including the original DeepLabV3+, PSPnet, and U-net. In addition, we conducted a trade-off comparison between model performance and complexity in different depths of the backbone convolution network, which illustrated model application feasibility.
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Affiliation(s)
- Huan Cheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Jucheng Zhang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou 310009, China
| | - Yinglan Gong
- Institute of Wenzhou, Zhejiang University, Wenzhou 325036, China
| | - Zhaoxia Pu
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou 310009, China
| | - Jun Jiang
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou 310009, China
| | - Yonghua Chu
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou 310009, China
| | - Ling Xia
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
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Li M, Zeng D, Zhou Y, Chen J, Cao S, Song H, Hu B, Yuan W, Chen J, Yang Y, Wang H, Fei H, Shi Y, Zhou Q. A novel risk stratification model for STEMI after primary PCI: global longitudinal strain and deep neural network assisted myocardial contrast echocardiography quantitative analysis. Front Cardiovasc Med 2023; 10:1140025. [PMID: 37180792 PMCID: PMC10172492 DOI: 10.3389/fcvm.2023.1140025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 04/14/2023] [Indexed: 05/16/2023] Open
Abstract
Background In ST-segment elevation myocardial infarction (STEMI) with the restoration of TIMI 3 flow by percutaneous coronary intervention (PCI), visually defined microvascular obstruction (MVO) was shown to be the predictor of poor prognosis, but not an ideal risk stratification method. We intend to introduce deep neural network (DNN) assisted myocardial contrast echocardiography (MCE) quantitative analysis and propose a better risk stratification model. Methods 194 STEMI patients with successful primary PCI with at least 6 months follow-up were included. MCE was performed within 48 h after PCI. The major adverse cardiovascular events (MACE) were defined as cardiac death, congestive heart failure, reinfarction, stroke, and recurrent angina. The perfusion parameters were derived from a DNN-based myocardial segmentation framework. Three patterns of visual microvascular perfusion (MVP) qualitative analysis: normal, delay, and MVO. Clinical markers and imaging features, including global longitudinal strain (GLS) were analyzed. A calculator for risk was constructed and validated with bootstrap resampling. Results The time-cost for processing 7,403 MCE frames is 773 s. The correlation coefficients of microvascular blood flow (MBF) were 0.99 to 0.97 for intra-observer and inter-observer variability. 38 patients met MACE in 6-month follow-up. We proposed A risk prediction model based on MBF [HR: 0.93 (0.91-0.95)] in culprit lesion areas and GLS [HR: 0.80 (0.73-0.88)]. At the best risk threshold of 40%, the AUC was 0.95 (sensitivity: 0.84, specificity: 0.94), better than visual MVP method (AUC: 0.70, Sensitivity: 0.89, Specificity: 0.40, IDI: -0.49). The Kaplan-Meier curves showed that the proposed risk prediction model allowed for better risk stratification. Conclusion The MBF + GLS model allowed more accurate risk stratification of STEMI after PCI than visual qualitative analysis. The DNN-assisted MCE quantitative analysis is an objective, efficient and reproducible method to evaluate microvascular perfusion.
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Affiliation(s)
- Mingqi Li
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, China
| | - Dewen Zeng
- Department of Computer Science and Engineering, University of Notre Dame, South Bend, IN, United States
| | - Yanxiang Zhou
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jinling Chen
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, China
| | - Sheng Cao
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hongning Song
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, China
| | - Bo Hu
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wenyue Yuan
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jing Chen
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yuanting Yang
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hao Wang
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hongwen Fei
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yiyu Shi
- Department of Computer Science and Engineering, University of Notre Dame, South Bend, IN, United States
| | - Qing Zhou
- Department of Ultrasound Imaging, Renmin Hospital of Wuhan University, Wuhan, China
- Correspondence: Qing Zhou
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Liu X, Fan Y, Li S, Chen M, Li M, Hau WK, Zhang H, Xu L, Lee APW. Deep learning-based automated left ventricular ejection fraction assessment using 2-D echocardiography. Am J Physiol Heart Circ Physiol 2021; 321:H390-H399. [PMID: 34170197 DOI: 10.1152/ajpheart.00416.2020] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Deep learning (DL) has been applied for automatic left ventricle (LV) ejection fraction (EF) measurement, but the diagnostic performance was rarely evaluated for various phenotypes of heart disease. This study aims to evaluate a new DL algorithm for automated LVEF measurement using two-dimensional echocardiography (2DE) images collected from three centers. The impact of three ultrasound machines and three phenotypes of heart diseases on the automatic LVEF measurement was evaluated. Using 36890 frames of 2DE from 340 patients, we developed a DL algorithm based on U-Net (DPS-Net) and the biplane Simpson's method was applied for LVEF calculation. Results showed a high performance in LV segmentation and LVEF measurement across phenotypes and echo systems by using DPS-Net. Good performance was obtained for LV segmentation when DPS-Net was tested on the CAMUS data set (Dice coefficient of 0.932 and 0.928 for ED and ES). Better performance of LV segmentation in study-wise evaluation was observed by comparing the DPS-Net v2 to the EchoNet-dynamic algorithm (P = 0.008). DPS-Net was associated with high correlations and good agreements for the LVEF measurement. High diagnostic performance was obtained that the area under receiver operator characteristic curve was 0.974, 0.948, 0.968, and 0.972 for normal hearts and disease phenotypes including atrial fibrillation, hypertrophic cardiomyopathy, dilated cardiomyopathy, respectively. High performance was obtained by using DPS-Net in LV detection and LVEF measurement for heart failure with several phenotypes. High performance was observed in a large-scale dataset, suggesting that the DPS-Net was highly adaptive across different echocardiographic systems.NEW & NOTEWORTHY A new strategy of feature extraction and fusion could enhance the accuracy of automatic LVEF assessment based on multiview 2-D echocardiographic sequences. High diagnostic performance for the determination of heart failure was obtained by using DPS-Net in cases with different phenotypes of heart diseases. High performance for left ventricle segmentation was obtained by using DPS-Net, suggesting the potential for a wider range of application in the interpretation of 2DE images.
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Affiliation(s)
- Xin Liu
- Guangdong Academy Research on VR Industry, Foshan University, Guangdong, People's Republic of China
| | - Yiting Fan
- Department of Cardiology, Shanghai Chest Hospital, Shanghai JiaoTong University, Shanghai, People's Republic of China.,Laboratory of Cardiac Imaging and 3D Printing, Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Shuang Li
- General Hospital of the Southern Theatre Command, PLA and Guangdong University of Technology, Guangdong, People's Republic of China
| | - Meixiang Chen
- General Hospital of the Southern Theatre Command, PLA and The First School of Clinical Medicine, Southern Medical University, Guangdong, People's Republic of China
| | - Ming Li
- Faculty of Medicine, Imperial College London, National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - William Kongto Hau
- Division of Cardiology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Lin Xu
- General Hospital of the Southern Theatre Command, PLA and The First School of Clinical Medicine, Southern Medical University, Guangdong, People's Republic of China
| | - Alex Pui-Wai Lee
- Laboratory of Cardiac Imaging and 3D Printing, Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.,Division of Cardiology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China
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