1
|
Dadon Z, Rav Acha M, Orlev A, Carasso S, Glikson M, Gottlieb S, Alpert EA. Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction. Diagnostics (Basel) 2024; 14:767. [PMID: 38611680 PMCID: PMC11011323 DOI: 10.3390/diagnostics14070767] [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: 03/11/2024] [Revised: 03/28/2024] [Accepted: 03/31/2024] [Indexed: 04/14/2024] Open
Abstract
INTRODUCTION Point-of-care ultrasound has become a universal practice, employed by physicians across various disciplines, contributing to diagnostic processes and decision-making. AIM To assess the association of reduced (<50%) left-ventricular ejection fraction (LVEF) based on prospective point-of-care ultrasound operated by medical students using an artificial intelligence (AI) tool and 1-year primary composite outcome, including mortality and readmission for cardiovascular-related causes. METHODS Eight trained medical students used a hand-held ultrasound device (HUD) equipped with an AI-based tool for automatic evaluation of the LVEF of non-selected patients hospitalized in a cardiology department from March 2019 through March 2020. RESULTS The study included 82 patients (72 males aged 58.5 ± 16.8 years), of whom 34 (41.5%) were diagnosed with AI-based reduced LVEF. The rates of the composite outcome were higher among patients with reduced systolic function compared to those with preserved LVEF (41.2% vs. 16.7%, p = 0.014). Adjusting for pertinent variables, reduced LVEF independently predicted the composite outcome (HR 2.717, 95% CI 1.083-6.817, p = 0.033). As compared to those with LVEF ≥ 50%, patients with reduced LVEF had a longer length of stay and higher rates of the secondary composite outcome, including in-hospital death, advanced ventilatory support, shock, and acute decompensated heart failure. CONCLUSION AI-based assessment of reduced systolic function in the hands of medical students, independently predicted 1-year mortality and cardiovascular-related readmission and was associated with unfavorable in-hospital outcomes. AI utilization by novice users may be an important tool for risk stratification for hospitalized patients.
Collapse
Affiliation(s)
- Ziv Dadon
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Moshe Rav Acha
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Amir Orlev
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Shemy Carasso
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Michael Glikson
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Shmuel Gottlieb
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Evan Avraham Alpert
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
- Department of Emergency Medicine, Hadassah Medical Center—Ein Kerem, Jerusalem 9112001, Israel
| |
Collapse
|
2
|
Suzuki S, Motogi J, Umemoto T, Hirota N, Nakai H, Matsuzawa W, Takayanagi T, Hyodo A, Satoh K, Arita T, Yagi N, Kishi M, Semba H, Kano H, Matsuno S, Kato Y, Otsuka T, Hori T, Matsuhama M, Iida M, Uejima T, Oikawa Y, Yajima J, Yamashita T. Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography. Circ Rep 2024; 6:46-54. [PMID: 38464990 PMCID: PMC10920024 DOI: 10.1253/circrep.cr-23-0068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 01/23/2024] [Accepted: 01/25/2024] [Indexed: 03/12/2024] Open
Abstract
Background: We developed a convolutional neural network (CNN) model to detect atrial fibrillation (AF) using the sinus rhythm ECG (SR-ECG). However, the diagnostic performance of the CNN model based on different ECG leads remains unclear. Methods and Results: In this retrospective analysis of a single-center, prospective cohort study, we identified 616 AF cases and 3,412 SR cases for the modeling dataset among new patients (n=19,170). The modeling dataset included SR-ECGs obtained within 31 days from AF-ECGs in AF cases and SR cases with follow-up ≥1,095 days. We evaluated the CNN model's performance for AF detection using 8-lead (I, II, and V1-6), single-lead, and double-lead ECGs through 5-fold cross-validation. The CNN model achieved an area under the curve (AUC) of 0.872 (95% confidence interval (CI): 0.856-0.888) and an odds ratio of 15.24 (95% CI: 12.42-18.72) for AF detection using the eight-lead ECG. Among the single-lead and double-lead ECGs, the double-lead ECG using leads I and V1 yielded an AUC of 0.871 (95% CI: 0.856-0.886) with an odds ratio of 14.34 (95% CI: 11.64-17.67). Conclusions: We assessed the performance of a CNN model for detecting AF using eight-lead, single-lead, and double-lead SR-ECGs. The model's performance with a double-lead (I, V1) ECG was comparable to that of the 8-lead ECG, suggesting its potential as an alternative for AF screening using SR-ECG.
Collapse
Affiliation(s)
- Shinya Suzuki
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | | | | | - Naomi Hirota
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Hiroshi Nakai
- Information System Division, The Cardiovascular Institute Tokyo Japan
| | | | | | | | | | - Takuto Arita
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Naoharu Yagi
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Mikio Kishi
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Hiroaki Semba
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Hiroto Kano
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Shunsuke Matsuno
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Yuko Kato
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Takayuki Otsuka
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Takayuki Hori
- Department of Cardiovascular Surgery, The Cardiovascular Institute Tokyo Japan
| | - Minoru Matsuhama
- Department of Cardiovascular Surgery, The Cardiovascular Institute Tokyo Japan
| | - Mitsuru Iida
- Department of Cardiovascular Surgery, The Cardiovascular Institute Tokyo Japan
| | - Tokuhisa Uejima
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Yuji Oikawa
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Junji Yajima
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Takeshi Yamashita
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| |
Collapse
|
3
|
Kawamura Y, Vafaei Sadr A, Abedi V, Zand R. Many Models, Little Adoption-What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection? J Clin Med 2024; 13:1313. [PMID: 38592138 PMCID: PMC10932407 DOI: 10.3390/jcm13051313] [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: 01/16/2024] [Revised: 02/19/2024] [Accepted: 02/23/2024] [Indexed: 04/10/2024] Open
Abstract
(1) Background: Atrial fibrillation (AF) is a major risk factor for stroke and is often underdiagnosed, despite being present in 13-26% of ischemic stroke patients. Recently, a significant number of machine learning (ML)-based models have been proposed for AF prediction and detection for primary and secondary stroke prevention. However, clinical translation of these technological innovations to close the AF care gap has been scant. Herein, we sought to systematically examine studies, employing ML models to predict incident AF in a population without prior AF or to detect paroxysmal AF in stroke cohorts to identify key reasons for the lack of translation into the clinical workflow. We conclude with a set of recommendations to improve the clinical translatability of ML-based models for AF. (2) Methods: MEDLINE, Embase, Web of Science, Clinicaltrials.gov, and ICTRP databases were searched for relevant articles from the inception of the databases up to September 2022 to identify peer-reviewed articles in English that used ML methods to predict incident AF or detect AF after stroke and reported adequate performance metrics. The search yielded 2815 articles, of which 16 studies using ML models to predict incident AF and three studies focusing on ML models to detect AF post-stroke were included. (3) Conclusions: This study highlights that (1) many models utilized only a limited subset of variables available from patients' health records; (2) only 37% of models were externally validated, and stratified analysis was often lacking; (3) 0% of models and 53% of datasets were explicitly made available, limiting reproducibility and transparency; and (4) data pre-processing did not include bias mitigation and sufficient details, leading to potential selection bias. Low generalizability, high false alarm rate, and lack of interpretability were identified as additional factors to be addressed before ML models can be widely deployed in the clinical care setting. Given these limitations, our recommendations to improve the uptake of ML models for better AF outcomes include improving generalizability, reducing potential systemic biases, and investing in external validation studies whilst developing a transparent modeling pipeline to ensure reproducibility.
Collapse
Affiliation(s)
- Yuki Kawamura
- School of Clinical Medicine, University of Cambridge, Cambridge CB3 0SP, UK
| | - Alireza Vafaei Sadr
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA (V.A.)
| | - Vida Abedi
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA (V.A.)
| | - Ramin Zand
- Department of Neurology, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA
| |
Collapse
|
4
|
Ciccarelli M, Giallauria F, Carrizzo A, Visco V, Silverio A, Cesaro A, Calabrò P, De Luca N, Mancusi C, Masarone D, Pacileo G, Tourkmani N, Vigorito C, Vecchione C. Artificial intelligence in cardiovascular prevention: new ways will open new doors. J Cardiovasc Med (Hagerstown) 2023; 24:e106-e115. [PMID: 37186561 DOI: 10.2459/jcm.0000000000001431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.
Collapse
Affiliation(s)
- Michele Ciccarelli
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Francesco Giallauria
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | - Albino Carrizzo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli
| | - Valeria Visco
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Angelo Silverio
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Arturo Cesaro
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Paolo Calabrò
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Nicola De Luca
- Department of Advanced Biomedical Sciences, Federico II University, Naples, Italy
| | - Costantino Mancusi
- Department of Advanced Biomedical Sciences, Federico II University, Naples, Italy
| | - Daniele Masarone
- Heart Failure Unit, Department of Cardiology, AORN dei Colli-Monaldi Hospital Naples, Naples, Italy
| | - Giuseppe Pacileo
- Heart Failure Unit, Department of Cardiology, AORN dei Colli-Monaldi Hospital Naples, Naples, Italy
| | - Nidal Tourkmani
- Cardiology and Cardiac Rehabilitation Unit, 'Mons. Giosuè Calaciura Clinic', Catania, Italy
- ABL, Guangzhou, China
| | - Carlo Vigorito
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | - Carmine Vecchione
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli
| |
Collapse
|
5
|
Sivanandarajah P, Wu H, Bajaj N, Khan S, Ng FS. Is machine learning the future for atrial fibrillation screening? CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2022; 3:136-145. [PMID: 35720677 PMCID: PMC9204790 DOI: 10.1016/j.cvdhj.2022.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Atrial fibrillation (AF) is the most common arrhythmia and causes significant morbidity and mortality. Early identification of AF may lead to early treatment of AF and may thus prevent AF-related strokes and complications. However, there is no current formal, cost-effective strategy for population screening for AF. In this review, we give a brief overview of targeted screening for AF, AF risk score models used for screening and describe the different screening tools. We then go on to extensively discuss the potential applications of machine learning in AF screening.
Collapse
Affiliation(s)
- Pavidra Sivanandarajah
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Huiyi Wu
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Nikesh Bajaj
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Sadia Khan
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| |
Collapse
|
6
|
Intelligent Algorithm-Based Electrocardiography to Predict Atrial Fibrillation after Coronary Artery Bypass Grafting in the Elderly. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4596552. [PMID: 35309845 PMCID: PMC8926521 DOI: 10.1155/2022/4596552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/19/2022] [Accepted: 02/21/2022] [Indexed: 11/17/2022]
Abstract
The objective of this study was to explore the predictive value of electrocardiogram (ECG) based on intelligent analysis algorithm for atrial fibrillation (AF) in elderly patients undergoing coronary artery bypass grafting (CABG). Specifically, 106 elderly patients with coronary heart disease who underwent CABG in the hospital were selected, including 52 patients with postoperative AF (AF group) and 54 patients without arrhythmia (control group). Within 1-3 weeks after operation, the dynamic ECG monitoring system based on Gentle AdaBoost algorithm constructed in this study was adopted. After the measurement of the 12-lead P wave duration, the maximum P wave duration (Pmax) and minimum P wave duration (Pmin) were recorded. As for simulation experiments, the same data was used as the back-propagation algorithm. The results showed that for the detection accuracy of the test samples, the Gentle AdaBoost algorithm showed 93.7% accuracy after the first iteration, and the Gentle AdaBoost algorithm was 16.1% higher than the back-propagation algorithm. Compared with the control group, the detection rate of arrhythmia in patients after CABG was significantly lower (
). Bivariate logistic regression analysis on Pmax and Pmin showed as follows: Pmax: 95% confidential interval (CI): 1.024-1.081,
; Pmin: 95% CI: 1.036-1.117,
. The sensitivity of Pmax and Pmin in predicting paroxysmal AF was 78.2% and 73.4%, respectively; the specificity of them was 80.1% and 85.6%, respectively; the positive predictive value was 81.2% and 83.4%, respectively; and the negative predictive value was 79.5% and 75.3%, respectively. In conclusion, the generalization ability of Gentle AdaBoost algorithm was better than that of back-propagation algorithm, and it can identify arrhythmia better. Pmax and Pmin were important indicators of AF after CABG.
Collapse
|
7
|
Suzuki S, Motogi J, Nakai H, Matsuzawa W, Takayanagi T, Umemoto T, Hirota N, Hyodo A, Satoh K, Otsuka T, Arita T, Yagi N, Yamashita T. Identifying patients with atrial fibrillation during sinus rhythm on ECG: Significance of the labeling in the artificial intelligence algorithm. IJC HEART & VASCULATURE 2022; 38:100954. [PMID: 35059494 PMCID: PMC8760502 DOI: 10.1016/j.ijcha.2022.100954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/27/2021] [Accepted: 01/03/2022] [Indexed: 11/26/2022]
Abstract
High performance of AI algorithm to detect AF using SR-ECG was confirmed in patients without structural heart disease. The performance of AI-enabled ECG to detect AF was high especially when the algorithm included SR-ECG taken after the index AF-ECG. A similar tendency was observed when the performance was tested in patients with structural heart diseases.
Background This study aimed to increase the knowledge on how to enhance the performance of artificial intelligence (AI)-enabled electrocardiography (ECG) to detect atrial fibrillation (AF) on sinus rhythm ECG (SR-ECG). Methods It is a retrospective analysis of a single-center, prospective cohort study (Shinken Database). We developed AI-enabled ECG using SR-ECG to predict AF with a convolutional neural network (CNN). Among new patients in our hospital (n = 19,170), 276 AF label (having ECG on AF [AF-ECG] in the ECG database) and 1896 SR label with following three conditions were identified in the derivation dataset: (1) without structural heart disease, (2) in AF label, SR-ECG was taken within 31 days from AF-ECG, and (3) in SR label, follow-up ≥ 1,095 days. Three patterns of AF label were analyzed by timing of SR-ECG to AF-ECG (before/after/before-or-after, CNN algorithm 1 to 3). The outcome measurement was area under the curve (AUC), sensitivity, specificity, accuracy, and F1 score. As an extra-testing dataset, the performance of AI-enabled ECG was tested in patients with structural heart disease. Results The AUC of AI-enabled ECG with CNN algorithm 1, 2, and 3 in the derivation dataset was 0.83, 0.88, and 0.86, respectively; when tested in patients with structural heart disease, 0.75, 0.81, and 0.78, respectively. Conclusion We confirmed high performance of AI-enabled ECG to detect AF on SR-ECG in patients without structural heart disease. The performance enhanced especially when SR-ECG after index AF-ECG was included in the algorithm, which was consistent in patients with structural heart disease.
Collapse
|
8
|
Hirota N, Suzuki S, Arita T, Yagi N, Otsuka T, Kishi M, Semba H, Kano H, Matsuno S, Kato Y, Uejima T, Oikawa Y, Matsuhama M, Iida M, Inoue T, Yajima J, Yamashita T. Relationship between resting 12-lead electrocardiogram and all-cause death in patients without structural heart disease: Shinken Database analysis. BMC Cardiovasc Disord 2021; 21:83. [PMID: 33568066 PMCID: PMC7874456 DOI: 10.1186/s12872-021-01864-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 01/11/2021] [Indexed: 12/03/2022] Open
Abstract
Background Resting 12-lead electrocardiography is widely used for the detection of cardiac diseases. Electrocardiogram readings have been reported to be affected by aging and, therefore, can predict patient mortality. Methods A total of 12,837 patients without structural heart disease who underwent electrocardiography at baseline were identified in the Shinken Database among those registered between 2010 and 2017 (n = 19,170). Using 438 electrocardiography parameters, predictive models for all-cause death and cardiovascular (CV) death were developed by a support vector machine (SVM) algorithm. Results During the observation period of 320.4 days, 55 all-cause deaths and 23 CV deaths were observed. In the SVM prediction model, the mean c-statistics of 10 cross-validation models with training and testing datasets were 0.881 ± 0.027 and 0.927 ± 0.101, respectively, for all-cause death and 0.862 ± 0.029 and 0.897 ± 0.069, respectively for CV death. For both all-cause and CV death, high values of permutation importance in the ECG parameters were concentrated in the QRS complex and ST-T segment. Conclusions Parameters acquired from 12-lead resting electrocardiography could be applied to predict the all-cause and CV deaths of patients without structural heart disease. The ECG parameters that greatly contributed to the prediction were concentrated in the QRS complex and ST-T segment.
Collapse
Affiliation(s)
- Naomi Hirota
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan.
| | - Shinya Suzuki
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Takuto Arita
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Naoharu Yagi
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Takayuki Otsuka
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Mikio Kishi
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Hiroaki Semba
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Hiroto Kano
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Shunsuke Matsuno
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Yuko Kato
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Tokuhisa Uejima
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Yuji Oikawa
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Minoru Matsuhama
- Department of Cardiovascular Surgery, The Cardiovascular Institute, Tokyo, Japan
| | - Mitsuru Iida
- Department of Cardiovascular Surgery, The Cardiovascular Institute, Tokyo, Japan
| | - Tatsuya Inoue
- Department of Cardiovascular Surgery, The Cardiovascular Institute, Tokyo, Japan
| | - Junji Yajima
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| | - Takeshi Yamashita
- Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan
| |
Collapse
|