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Srinivas S, Vignesh Rk B, Ayinapudi VN, Govindarajan A, Sundaram SS, Priyathersini N. Neurological Consequences of Cardiac Arrhythmias: Relationship Between Stroke, Cognitive Decline, and Heart Rhythm Disorders. Cureus 2024; 16:e57159. [PMID: 38681361 PMCID: PMC11056008 DOI: 10.7759/cureus.57159] [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] [Accepted: 03/29/2024] [Indexed: 05/01/2024] Open
Abstract
Cardiac arrhythmias are one of the most common disorders with high morbidity and mortality. The effect of cardiac arrhythmias on the brain is very pronounced due to the high sensitivity of the brain to oxygen and blood supply. This mortality is preventable by early diagnosis and treatment which improves the patient's quality of life. Intervening at the right time, post arrhythmia is significant in preventing deaths and improving patient outcomes. Multiple pathophysiological mechanisms are studied for the brain-axis implications, that have the potential to be targeted by novel therapies. In this review, we describe the pathophysiological mechanisms and recent advances in detail to understand the functional aspects of the brain-heart axis and neurological implications post-stroke, caused by cardiac disorders. This paper aims to discuss the current literature on the neurological consequences of cardiac arrhythmias and delve into a deeper understanding of the brain-heart axis, imbalances, and decline, with the aim of summarizing everything and all about the neurological consequences of cardiac arrhythmias.
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Affiliation(s)
- Swathi Srinivas
- Medicine, Sri Ramachandra Institute of Higher Education and Research, Chennai, IND
| | - Bharath Vignesh Rk
- Medicine, Sri Ramachandra Institute of Higher Education and Research, Chennai, IND
| | | | | | | | - N Priyathersini
- Pathology, Sri Ramachandra Medical College and Research Institute, Chennai, IND
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Chen B, Maslove DM, Curran JD, Hamilton A, Laird PR, Mousavi P, Sibley S. A deep learning model for the classification of atrial fibrillation in critically ill patients. Intensive Care Med Exp 2023; 11:2. [PMID: 36635373 PMCID: PMC9837355 DOI: 10.1186/s40635-022-00490-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 12/27/2022] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Atrial fibrillation (AF) is the most common cardiac arrhythmia in the intensive care unit and is associated with increased morbidity and mortality. New-onset atrial fibrillation (NOAF) is often initially paroxysmal and fleeting, making it difficult to diagnose, and therefore difficult to understand the true burden of disease. Automated algorithms to detect AF in the ICU have been advocated as a means to better quantify its true burden. RESULTS We used a publicly available 12-lead ECG dataset to train a deep learning model for the classification of AF. We then conducted an external independent validation of the model using continuous telemetry data from 984 critically ill patients collected in our institutional database. Performance metrics were stratified by signal quality, classified as either clean or noisy. The deep learning model was able to classify AF with an overall sensitivity of 84%, specificity of 89%, positive predictive value (PPV) of 55%, and negative predictive value of 97%. Performance was improved in clean data as compared to noisy data, most notably with respect to PPV and specificity. CONCLUSIONS This model demonstrates that computational detection of AF is currently feasible and effective. This approach stands to improve the efficiency of retrospective and prospective research into AF in the ICU by automating AF detection, and enabling precise quantification of overall AF burden.
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Affiliation(s)
- Brian Chen
- grid.410356.50000 0004 1936 8331School of Computing, Queen’s University, Kingston, Canada
| | - David M. Maslove
- grid.410356.50000 0004 1936 8331Department of Critical Care Medicine, Queen’s University, 76 Stuart Street, Kingston, ON K7L 2V7 Canada
| | - Jeffrey D. Curran
- grid.410356.50000 0004 1936 8331Department of Critical Care Medicine, Queen’s University, 76 Stuart Street, Kingston, ON K7L 2V7 Canada
| | - Alexander Hamilton
- grid.410356.50000 0004 1936 8331Centre for Health Innovation, Queen’s University, Kingston, Canada
| | - Philip R. Laird
- grid.410356.50000 0004 1936 8331Department of Critical Care Medicine, Queen’s University, 76 Stuart Street, Kingston, ON K7L 2V7 Canada
| | - Parvin Mousavi
- grid.410356.50000 0004 1936 8331School of Computing, Queen’s University, Kingston, Canada
| | - Stephanie Sibley
- grid.410356.50000 0004 1936 8331Department of Critical Care Medicine, Queen’s University, 76 Stuart Street, Kingston, ON K7L 2V7 Canada
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Haverkamp W, Strodthoff N, Israel C. [Artificial intelligence-based ECG analysis: current status and future perspectives : Part 2: Recent studies and future]. Herzschrittmacherther Elektrophysiol 2022; 33:305-311. [PMID: 35552487 PMCID: PMC9411078 DOI: 10.1007/s00399-022-00855-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 04/05/2022] [Indexed: 11/28/2022]
Abstract
Während grundlegende Aspekte der Anwendung von künstlicher Intelligenz (KI) zur Elektrokardiogramm(EKG)-Analyse in Teil 1 dieser Übersicht behandelt wurden, beschäftigt sich die vorliegende Arbeit (Teil 2) mit einer Besprechung von aktuellen Studien zum praktischen Einsatz dieser neuen Technologien und Aspekte ihrer aktuellen und möglichen zukünftigen Anwendung. Die Anzahl der zum Thema KI-basierte EKG-Analyse publizierten Studien steigt seit 2017 rasant an. Dies gilt vor allem für Untersuchungen, die Deep Learning (DL) mit künstlichen neuronalen Netzen (KNN) einsetzen. Inhaltlich geht es nicht nur darum, die Schwächen der klassischen EKG-Diagnostik mit Hilfe von KI zu überwinden und die diagnostische Güte des Verfahrens zu verbessern, sondern auch die Funktionalität des EKGs zu erweitern. Angestrebt wird die Erkennung spezieller kardiologischer und nichtkardiologischer Krankheitsbilder sowie die Vorhersage zukünftiger Krankheitszustände, z. B. die zukünftige Entwicklung einer linksventrikulären Dysfunktion oder das zukünftige Auftreten von Vorhofflimmern. Möglich wird dies, indem KI mittels DL in riesigen EKG-Datensätzen subklinische Muster findet und für die Algorithmen-Entwicklung nutzt. Die KI-unterstützte EKG-Analyse wird somit zu einem Screening-Instrument und geht weit darüber hinaus, nur besser als ein Kardiologe zu sein. Die erzielten Fortschritte sind bemerkenswert und sorgen in Fachwelt und Öffentlichkeit für Aufmerksamkeit und Euphorie. Bei den meisten Studien handelt es sich allerdings um Proof-of-Concept-Studien. Häufig werden private (institutionseigene) Daten verwendet, deren Qualität unklar ist. Bislang ist nur selten eine klinische Validierung der entwickelten Algorithmen in anderen Kollektiven und Szenarien erfolgt. Besonders problematisch ist, dass der Weg, wie KI eine Lösung findet, bislang meistens verborgen bleibt (Blackbox-Charakter). Damit steckt die KI-basierte Elektrokardiographie noch in den Kinderschuhen. Unbestritten ist aber schon absehbar, dass das EKG als einfach anzuwendendes und beliebig oft wiederholbares diagnostisches Verfahren auch in Zukunft nicht nur weiterhin unverzichtbar sein wird, sondern durch KI an klinischer Bedeutung gewinnen wird.
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Affiliation(s)
- Wilhelm Haverkamp
- Abteilung für Kardiologie und Metabolismus. Medizinische Klinik mit Schwerpunkt Kardiologie, Campus Virchow-Klinikum, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Deutschland. .,Berlin Institute of Health Center for Regenerative Therapies (BCRT), Berlin, Deutschland.
| | - Nils Strodthoff
- Department für Versorgungsforschung, Fakultät VI - Medizin und Gesundheitswissenschaften, Universität Oldenburg, Oldenburg, Deutschland
| | - Carsten Israel
- Klinik für Innere Medizin - Kardiologie, Diabetologie und Nephrologie, Evangelisches Klinikum Bethel, Bielefeld, Deutschland
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Premont A, Balthes S, Marr CM, Jeevaratnam K. Fundamentals of arrhythmogenic mechanisms and treatment strategies for equine atrial fibrillation. Equine Vet J 2021; 54:262-282. [PMID: 34564902 DOI: 10.1111/evj.13518] [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: 12/21/2020] [Revised: 09/13/2021] [Accepted: 09/16/2021] [Indexed: 11/26/2022]
Abstract
Atrial fibrillation (AF) is the most common pathological arrhythmia in horses. Although it is not usually a life-threatening condition on its own, it can cause poor performance and make the horse unsafe to ride. It is a complex multifactorial disease influenced by both genetic and environmental factors including exercise training, comorbidities or ageing. The interactions between all these factors in horses are still not completely understood and the pathophysiology of AF remains poorly defined. Exciting progress has been recently made in equine cardiac electrophysiology in terms of diagnosis and documentation methods such as cardiac mapping, implantable electrocardiogram (ECG) recording devices or computer-based ECG analysis that will hopefully improve our understanding of this disease. The available pharmaceutical and electrophysiological treatments have good efficacy and lead to a good prognosis for AF, but recurrence is a frequent issue that veterinarians have to face. This review aims to summarise our current understanding of equine cardiac electrophysiology and pathophysiology of equine AF while providing an overview of the mechanism of action for currently available treatments for equine AF.
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Affiliation(s)
- Antoine Premont
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Samantha Balthes
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Celia M Marr
- Rossdales Equine Hospital and Diagnostic Centre, Newmarket, UK
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van de Leur RR, Boonstra MJ, Bagheri A, Roudijk RW, Sammani A, Taha K, Doevendans PA, van der Harst P, van Dam PM, Hassink RJ, van Es R, Asselbergs FW. Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology. Arrhythm Electrophysiol Rev 2020; 9:146-154. [PMID: 33240510 PMCID: PMC7675143 DOI: 10.15420/aer.2020.26] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.
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Affiliation(s)
- Rutger R van de Leur
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Machteld J Boonstra
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Ayoub Bagheri
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Department of Methodology and Statistics, Utrecht University, Utrecht, the Netherlands
| | - Rob W Roudijk
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Netherlands Heart Institute, Utrecht, the Netherlands
| | - Arjan Sammani
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Karim Taha
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Netherlands Heart Institute, Utrecht, the Netherlands
| | - Pieter Afm Doevendans
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Netherlands Heart Institute, Utrecht, the Netherlands.,Central Military Hospital Utrecht, Ministerie van Defensie, Utrecht, the Netherlands
| | - Pim van der Harst
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Peter M van Dam
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Rutger J Hassink
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - René van Es
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.,Health Data Research UK and Institute of Health Informatics, University College London, London, UK
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