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Patrascanu OS, Tutunaru D, Musat CL, Dragostin OM, Fulga A, Nechita L, Ciubara AB, Piraianu AI, Stamate E, Poalelungi DG, Dragostin I, Iancu DCE, Ciubara A, Fulga I. Future Horizons: The Potential Role of Artificial Intelligence in Cardiology. J Pers Med 2024; 14:656. [PMID: 38929877 PMCID: PMC11204977 DOI: 10.3390/jpm14060656] [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: 05/27/2024] [Revised: 06/11/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024] Open
Abstract
Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases.
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Affiliation(s)
- Octavian Stefan Patrascanu
- Department of Cardiology, University Emergency Hospital of Bucharest, 169 Splaiul Independentei St, 050098 Bucharest, Romania; (O.S.P.); (E.S.)
| | - Dana Tutunaru
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Carmina Liana Musat
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Oana Maria Dragostin
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Luiza Nechita
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Alexandru Bogdan Ciubara
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Alin Ionut Piraianu
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Elena Stamate
- Department of Cardiology, University Emergency Hospital of Bucharest, 169 Splaiul Independentei St, 050098 Bucharest, Romania; (O.S.P.); (E.S.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Diana Gina Poalelungi
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Ionut Dragostin
- Emergency County Clinical Hospital, 2 Buzaului St, 810325 Braila, Romania;
| | | | - Anamaria Ciubara
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania; (D.T.); (C.L.M.); (O.M.D.); (A.B.C.); (A.I.P.); (D.G.P.); (A.C.); (I.F.)
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Ose B, Sattar Z, Gupta A, Toquica C, Harvey C, Noheria A. Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review. Curr Cardiol Rep 2024; 26:561-580. [PMID: 38753291 DOI: 10.1007/s11886-024-02062-1] [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] [Accepted: 04/17/2024] [Indexed: 06/26/2024]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovascular screening in the population. AI can be applied to the standard 12-lead resting ECG and single-lead ECGs in external monitors, implantable devices, and direct-to-consumer smart devices. We summarize the current state of the literature on AI-ECG. RECENT FINDINGS Rhythm classification was the first application of AI-ECG. Subsequently, AI-ECG models have been developed for screening structural heart disease including hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, pulmonary hypertension, and left ventricular systolic dysfunction. Further, AI models can predict future events like development of systolic heart failure and atrial fibrillation. AI-ECG exhibits potential in acute cardiac events and non-cardiac applications, including acute pulmonary embolism, electrolyte abnormalities, monitoring drugs therapy, sleep apnea, and predicting all-cause mortality. Many AI models in the domain of cardiac monitors and smart watches have received Food and Drug Administration (FDA) clearance for rhythm classification, while others for identification of cardiac amyloidosis, pulmonary hypertension and left ventricular dysfunction have received breakthrough device designation. As AI-ECG models continue to be developed, in addition to regulatory oversight and monetization challenges, thoughtful clinical implementation to streamline workflows, avoiding information overload and overwhelming of healthcare systems with false positive results is necessary. Research to demonstrate and validate improvement in healthcare efficiency and improved patient outcomes would be required before widespread adoption of any AI-ECG model.
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Affiliation(s)
- Benjamin Ose
- The University of Kansas School of Medicine, Kansas City, KS, USA
| | - Zeeshan Sattar
- Division of General and Hospital Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amulya Gupta
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | | | - Chris Harvey
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amit Noheria
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA.
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA.
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Haverkamp W, Strodthoff N. [Artificial intelligence-enhanced electrocardiography : Will it revolutionize diagnosis and management of our patients?]. Herzschrittmacherther Elektrophysiol 2024; 35:104-110. [PMID: 38361131 DOI: 10.1007/s00399-024-00997-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 01/23/2024] [Indexed: 02/17/2024]
Abstract
The use of artificial intelligence (AI) in healthcare has made significant progress in the last 10 years. Many experts believe that utilization of AI technologies, especially deep learning, will bring about drastic changes in how physicians understand, diagnose, and treat diseases. One aspect of this development is AI-enhanced electrocardiography (ECG) analysis. It involves not only optimizing the traditional ECG analysis by the physician and improving the accuracy of automatic interpretation by the ECG device but also introducing entirely new diagnostic options enabled by AI. Examples include assessing left ventricular function, predicting atrial fibrillation, and diagnosing both cardiac and noncardiac conditions. Through AI, the ECG becomes a comprehensive tool for screening, diagnosis, and patient management, potentially revolutionizing clinical practices. This paper provides an overview of the current state of this development, discusses existing limitations, and explores the challenges that may arise for healthcare professionals in this context.
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Affiliation(s)
- Wilhelm Haverkamp
- Abteilung für Kardiologie und Metabolismus, Medizinische Klinik mit Schwerpunkt Kardiologie, Campus Virchow-Klinikum, Deutsches Herzzentrum der Charité, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Deutschland.
| | - Nils Strodthoff
- Department für Versorgungsforschung, Fakultät VI - Medizin und Gesundheitswissenschaften, Abteilung AI4Health, Carl von Ossietzky Universität Oldenburg, Oldenburg, Deutschland
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Stamate E, Piraianu AI, Ciobotaru OR, Crassas R, Duca O, Fulga A, Grigore I, Vintila V, Fulga I, Ciobotaru OC. Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years. Diagnostics (Basel) 2024; 14:1103. [PMID: 38893630 PMCID: PMC11172021 DOI: 10.3390/diagnostics14111103] [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: 04/22/2024] [Revised: 05/12/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. RESULTS We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. CONCLUSIONS The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.
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Affiliation(s)
- Elena Stamate
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Alin-Ionut Piraianu
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Oana Roxana Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
| | - Rodica Crassas
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Oana Duca
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Ionica Grigore
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Vlad Vintila
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Clinical Department of Cardio-Thoracic Pathology, University of Medicine and Pharmacy “Carol Davila” Bucharest, 37 Dionisie Lupu Street, 4192910 Bucharest, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Octavian Catalin Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
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Gudigar A, Kadri NA, Raghavendra U, Samanth J, Maithri M, Inamdar MA, Prabhu MA, Hegde A, Salvi M, Yeong CH, Barua PD, Molinari F, Acharya UR. Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013-2023). Comput Biol Med 2024; 172:108207. [PMID: 38489986 DOI: 10.1016/j.compbiomed.2024.108207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 03/17/2024]
Abstract
Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, 576104, India
| | - M Maithri
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Mahesh Anil Inamdar
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Mukund A Prabhu
- Department of Cardiology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Ajay Hegde
- Manipal Hospitals, Bengaluru, Karnataka, 560102, India
| | - Massimo Salvi
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnicodi Torino, Turin, Italy
| | - Chai Hong Yeong
- School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500, Subang Jaya, Malaysia
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW, 2010, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, 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, Politecnicodi Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia; Centre for Health Research, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
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Cau R, Pisu F, Suri JS, Montisci R, Gatti M, Mannelli L, Gong X, Saba L. Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes. Diagnostics (Basel) 2024; 14:156. [PMID: 38248033 PMCID: PMC11154548 DOI: 10.3390/diagnostics14020156] [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: 12/12/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 01/23/2024] Open
Abstract
Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches.
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Affiliation(s)
- Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| | - Francesco Pisu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoin™, Roseville, CA 95661, USA;
| | - Roberta Montisci
- Department of Cardiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy;
| | - Marco Gatti
- Department of Radiology, Università degli Studi di Torino, 10129 Turin, Italy;
| | | | - Xiangyang Gong
- Radiology Department, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou 310014, China;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy; (R.C.); (F.P.)
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Venn RA, Khurshid S, Grayson M, Ashburner JM, Al‐Alusi MA, Chang Y, Foulkes A, Ellinor PT, McManus DD, Singer DE, Atlas SJ, Lubitz SA. Characteristics and Attitudes of Wearable Device Users and Nonusers in a Large Health Care System. J Am Heart Assoc 2024; 13:e032126. [PMID: 38156452 PMCID: PMC10863832 DOI: 10.1161/jaha.123.032126] [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] [Received: 08/10/2023] [Accepted: 11/17/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND Consumer wearable devices with health and wellness features are increasingly common and may enhance disease detection and management. Yet studies informing relationships between wearable device use, attitudes toward device data, and comprehensive clinical profiles are lacking. METHODS AND RESULTS WATCH-IT (Wearable Activity Tracking for Comprehensive Healthcare-Integrated Technology) studied adults receiving longitudinal primary or ambulatory cardiovascular care in the Mass General Brigham health care system from January 2010 to July 2021. Participants completed a 20-question electronic survey about perceptions and use of consumer wearable devices, with responses linked to electronic health records. Multivariable logistic regression was used to identify factors associated with device use. Among 214 992 individuals receiving longitudinal primary or cardiovascular care with an active electronic portal, 11 121 responded (5.2%). Most respondents (55.8%) currently used a wearable device, and most nonusers (95.3%) would use a wearable if provided at no cost. Although most users (70.2%) had not shared device data with their doctor previously, most believed it would be very (20.4%) or moderately (34.4%) important to share device-related health information with providers. In multivariable models, older age (odds ratio [OR], 0.80 per 10-year increase [95% CI, 0.77-0.82]), male sex (OR, 0.87 [95% CI, 0.80-0.95]), and heart failure (OR, 0.75 [95% CI, 0.63-0.89]) were associated with lower odds of wearable device use, whereas higher median income (OR, 1.08 per 1-quartile increase [95% CI, 1.04-1.12]) and care in a cardiovascular medicine clinic (OR, 1.17 [95% CI, 1.05-1.30]) were associated with greater odds of device use. CONCLUSIONS Among patients in primary and cardiovascular medicine clinics, consumer wearable device use is common, and most users perceive value in wearable health data.
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Affiliation(s)
- Rachael A. Venn
- Cardiovascular Research Center, Massachusetts General HospitalBostonMAUSA
- Demoulas Center for Cardiac ArrhythmiasCardiology Division, Massachusetts General HospitalBostonMAUSA
| | - Shaan Khurshid
- Cardiovascular Research Center, Massachusetts General HospitalBostonMAUSA
- Demoulas Center for Cardiac ArrhythmiasCardiology Division, Massachusetts General HospitalBostonMAUSA
| | - Mia Grayson
- Cardiovascular Research Center, Massachusetts General HospitalBostonMAUSA
| | - Jeffrey M. Ashburner
- Division of General Internal MedicineMassachusetts General HospitalBostonMAUSA
- Department of MedicineHarvard Medical SchoolBostonMAUSA
| | - Mostafa A. Al‐Alusi
- Cardiovascular Research Center, Massachusetts General HospitalBostonMAUSA
- Cardiology Division, Massachusetts General HospitalBostonMAUSA
| | - Yuchiao Chang
- Division of General Internal MedicineMassachusetts General HospitalBostonMAUSA
- Department of MedicineHarvard Medical SchoolBostonMAUSA
| | - Andrea Foulkes
- Harvard Medical SchoolBostonMAUSA
- Biostatistics Center, Massachusetts General HospitalBostonMAUSA
| | - Patrick T. Ellinor
- Cardiovascular Research Center, Massachusetts General HospitalBostonMAUSA
- Demoulas Center for Cardiac ArrhythmiasCardiology Division, Massachusetts General HospitalBostonMAUSA
| | - David D. McManus
- Department of MedicineUniversity of Massachusetts T.H. Chan Medical SchoolWorcesterMAUSA
| | - Daniel E. Singer
- Division of General Internal MedicineMassachusetts General HospitalBostonMAUSA
- Department of MedicineHarvard Medical SchoolBostonMAUSA
| | - Steven J. Atlas
- Division of General Internal MedicineMassachusetts General HospitalBostonMAUSA
- Department of MedicineHarvard Medical SchoolBostonMAUSA
| | - Steven A. Lubitz
- Cardiovascular Research Center, Massachusetts General HospitalBostonMAUSA
- Demoulas Center for Cardiac ArrhythmiasCardiology Division, Massachusetts General HospitalBostonMAUSA
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Kamimura D, Hall ME. Can Any Electrocardiographic Indicators Reflect Myocardial Fibrosis? Am J Cardiol 2023; 206:372-374. [PMID: 37689495 DOI: 10.1016/j.amjcard.2023.08.084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 09/11/2023]
Affiliation(s)
- Daisuke Kamimura
- Department of Clinical Laboratory, Yokohama City University Hospital, Yokohama, Japan; Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi.
| | - Michael E Hall
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi
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9
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Venn RA, Khurshid S, Grayson M, Ashburner JM, Al-Alusi MA, Chang Y, Foulkes A, Ellinor PT, McManus DD, Singer DE, Atlas SJ, Lubitz SA. Characteristics and Attitudes of Wearable Device Users and Non-Users in a Large Healthcare System. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.10.23293960. [PMID: 37609134 PMCID: PMC10441501 DOI: 10.1101/2023.08.10.23293960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Introduction Consumer wearable devices with health and wellness features are increasingly common and may enhance prevention and management of cardiovascular disease. However, the characteristics and attitudes of wearable device users versus non-users are poorly understood. Methods Wearable Activity Tracking for Comprehensive Healthcare-Integrated Technology (WATCH-IT) was a prospective study of adults aged ≥18 years receiving longitudinal primary or ambulatory cardiovascular care at one of eleven hospitals within the Mass General Brigham multi-institutional healthcare system between January 2010-July 2021. We invited patients, including wearable users and non-users, to participate via an electronic patient portal. Participants were asked to complete a 20-question survey regarding perceptions and use of consumer wearable devices. Responses were linked to electronic health record data. Multivariable logistic regression was used to identify factors associated with device use. Results Among 280,834 individuals receiving longitudinal primary or cardiovascular care, 65,842 did not have an active electronic portal or opted out of research contact. Of the 214,992 individuals sent a survey link, 11,121 responded (5.2%), comprising the WATCH-IT patient sample. Most respondents (55.8%) reported current use of a wearable device, and most non-users (95.3%) reported they would use a wearable device if provided at no cost. Although most users (70.2%) had not shared device data with their doctor previously, the majority believed it would be very (20.4%) or moderately (34.4%) important to share device-related health information with providers. In multivariable models, older age (odds ratio [OR] 0.80 per 10-year increase, 95% CI 0.77-0.82), male sex (0.87, 95% CI 0.80-0.95), and heart failure (0.75, 95% CI 0.63-0.89) were associated with lower odds of wearable device use, whereas higher median zip code income (1.08 per 1-quartile increase, 95% CI 1.04-1.12) and care in a cardiovascular medicine clinic (1.17, 95% CI 1.05-1.30) were associated with greater odds of device use. Nearly all respondents (98%) stated they would share device data with researchers studying health outcomes. Conclusions Within an electronically assembled cohort of patients in primary and cardiovascular medicine clinics with linkage to detailed health records, wearable device use is common. Most users perceive value in wearable data. Our platform may enable future study of the relationships between wearable technology and resource utilization, clinical outcomes, and health disparities.
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Affiliation(s)
- Rachael A. Venn
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Demoulas Center for Cardiac Arrhythmias, Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Shaan Khurshid
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Demoulas Center for Cardiac Arrhythmias, Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Mia Grayson
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jeffrey M. Ashburner
- Division of General Internal Medicine, Massachusetts General Hospital, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Mostafa A. Al-Alusi
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Yuchiao Chang
- Division of General Internal Medicine, Massachusetts General Hospital, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrea Foulkes
- Harvard Medical School, Boston, Massachusetts, United States of America
- Biostatistics Center, Massachusetts General Hospital, Boston, MA
| | - Patrick T. Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Demoulas Center for Cardiac Arrhythmias, Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - David D. McManus
- Department of Medicine, University of Massachusetts T.H. Chan Medical School, Worcester, Massachusetts, USA
| | - Daniel E. Singer
- Division of General Internal Medicine, Massachusetts General Hospital, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Steven J. Atlas
- Division of General Internal Medicine, Massachusetts General Hospital, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Steven A. Lubitz
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Demoulas Center for Cardiac Arrhythmias, Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
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