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Berry-Noronha A, Bonavia L, Song E, Grose D, Johnson D, Maylin E, Oqueli E, Sahathevan R. ECG predictors of AF: A systematic review (predicting AF in ischaemic stroke-PrAFIS). Clin Neurol Neurosurg 2024; 237:108164. [PMID: 38377651 DOI: 10.1016/j.clineuro.2024.108164] [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: 08/24/2023] [Revised: 09/13/2023] [Accepted: 02/05/2024] [Indexed: 02/22/2024]
Abstract
In 25% of patients presenting with embolic stroke, a cause is not determined. Atrial fibrillation (AF) is a commonly identified mechanism of stroke in this population, particularly in older patients. Conventional investigations are used to detect AF, but can we predict AF in this population and generally? We performed a systematic review to identify potential predictors of AF on 12-lead electrocardiogram (ECG). METHOD We conducted a search of EMBASE and Medline databases for prospective and retrospective cohorts, meta-analyses or case-control studies of ECG abnormalities in sinus rhythm predicting subsequent atrial fibrillation. We assessed quality of studies based on the Newcastle-Ottawa scale and data were extracted according to PRISMA guidelines. RESULTS We identified 44 studies based on our criteria. ECG patterns that predicted the risk of developing AF included interatrial block, P-wave terminal force lead V1, P-wave dispersion, abnormal P-wave-axis, abnormal P-wave amplitude, prolonged PR interval, left ventricular hypertrophy, QT prolongation, ST-T segment abnormalities and atrial premature beats. Furthermore, we identified that factors such as increased age, high CHADS-VASC, chronic renal disease further increase the positive-predictive value of some of these parameters. Several of these have been successfully incorporated into clinical scoring systems to predict AF. CONCLUSION There are several ECG abnormalities that can predict AF both independently, and with improved predictive value when combined with clinical risk factors, and if incorporated into clinical risk scores. Improved and validated predictive models could streamline selection of patients for cardiac monitoring and initiation of oral anticoagulants.
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Affiliation(s)
| | - Luke Bonavia
- Department of Neurology, Royal Hobart Hospital, Australia
| | - Edmund Song
- Department of Medicine, Grampians Health Ballarat, Australia
| | - Daniel Grose
- Department of Medicine, Grampians Health Ballarat, Australia
| | - Damian Johnson
- Department of Medicine, Werribee Mercy Hospital, Australia
| | - Erin Maylin
- Department of Medicine, Monash Health (Clayton), Australia
| | - Ernesto Oqueli
- Department of Medicine, Grampians Health Ballarat, Australia; School of Medicine, Deakin University, Australia
| | - Ramesh Sahathevan
- Department of Medicine, Grampians Health Ballarat, Australia; School of Medicine, Deakin University, Australia
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2
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Lampert J, Power D, Havaldar S, Govindarajulu U, Kawamura I, Maan A, Miller MA, Menon K, Koruth J, Whang W, Bagiella E, Bayes-Genis A, Musikantow D, Turagam M, Bayes de Luna A, Halperin J, Dukkipati SR, Vaid A, Nadkarni G, Glicksberg B, Fuster V, Reddy VY. Interatrial Block Association With Adverse Cardiovascular Outcomes in Patients Without a History of Atrial Fibrillation. JACC Clin Electrophysiol 2023; 9:1804-1815. [PMID: 37354170 DOI: 10.1016/j.jacep.2023.04.006] [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: 12/19/2022] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 06/26/2023]
Abstract
BACKGROUND Interatrial block (IAB) is associated with thromboembolism and atrial arrhythmias. However, prior studies included small patient cohorts so it remains unclear whether IAB predicts adverse outcomes particularly in context of atrial fibrillation (AF)/atrial flutter (AFL). OBJECTIVES This study sought to determine whether IAB portends increased stroke risk in a large cohort in the presence or absence of AFAF/AFL. METHODS We performed a 5-center retrospective analysis of 4,837,989 electrocardiograms (ECGs) from 1,228,291 patients. IAB was defined as P-wave duration ≥120 ms in leads II, III, or aVF. Measurements were extracted as .XML files. After excluding patients with prior AF/AFL, 1,825,958 ECGs from 458,994 patients remained. Outcomes were analyzed using restricted mean survival time analysis and restricted mean time lost. RESULTS There were 86,317 patients with IAB and 355,032 patients without IAB. IAB prevalence in the cohort was 19.6% and was most common in Black (26.1%), White (20.9%), and Hispanic (18.5%) patients and least prevalent in Native Americans (9.2%). IAB was independently associated with increased stroke probability (restricted mean time lost ratio coefficient [RMTLRC]: 1.43; 95% CI: 1.35-1.51; tau = 1,895), mortality (RMTLRC: 1.14; 95% CI: 1.07-1.21; tau = 1,924), heart failure (RMTLRC: 1.94; 95% CI: 1.83-2.04; tau = 1,921), systemic thromboembolism (RMTLRC: 1.62; 95% CI: 1.53-1.71; tau = 1,897), and incident AF/AFL (RMTLRC: 1.16; 95% CI: 1.10-1.22; tau = 1,888). IAB was not associated with stroke in patients with pre-existing AF/AFL. CONCLUSIONS IAB is independently associated with stroke in patients with no history of AF/AFL even after adjustment for incident AF/AFL and CHA2DS2-VASc score. Patients are at increased risk of stroke even when AF/AFL is not identified.
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Affiliation(s)
- Joshua Lampert
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA. https://twitter.com/joshuamlampertmd
| | - David Power
- Mount Sinai Heart, Mount Sinai Hospital, New York, New York, USA
| | - Shreyas Havaldar
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Usha Govindarajulu
- Center for Biostatistics, Department of Population Health, Mount Sinai Hospital, New York, New York, USA
| | - Iwanari Kawamura
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - Abhishek Maan
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - Marc A Miller
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - Kartikeya Menon
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jacob Koruth
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - William Whang
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - Emilia Bagiella
- Center for Biostatistics, Department of Population Health, Mount Sinai Hospital, New York, New York, USA
| | - Antoni Bayes-Genis
- Heart Institute, Hospital Universitario Germans trias I Pujol, Badalona, Spain
| | - Daniel Musikantow
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - Mohit Turagam
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - Antoni Bayes de Luna
- Cardiovascular Research Foundation, Cardiovascular ICCC-Program, Research Institute Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, Barcelona, Spain
| | | | - Srinivas R Dukkipati
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - Akhil Vaid
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Benjamin Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Valentin Fuster
- Mount Sinai Heart, Mount Sinai Hospital, New York, New York, USA
| | - Vivek Y Reddy
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA.
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3
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Chousou PA, Chattopadhyay R, Tsampasian V, Vassiliou VS, Pugh PJ. Electrocardiographic Predictors of Atrial Fibrillation. Med Sci (Basel) 2023; 11:medsci11020030. [PMID: 37092499 PMCID: PMC10123668 DOI: 10.3390/medsci11020030] [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: 03/09/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND Atrial fibrillation (AF) is the most common pathological arrhythmia, and its complications lead to significant morbidity and mortality. However, patients with AF can often go undetected, especially if they are asymptomatic or have a low burden of paroxysms. Identification of those at high risk of AF development may help refine screening and management strategies. METHODS PubMed and Embase databases were systematically searched for studies looking at electrocardiographic predictors of AF from inception to August 2021. RESULTS A total of 115 studies were reported which examined a combination of atrial and ventricular parameters that could be electrocardiographic predictors of AF. Atrial predictors include conduction parameters, such as the PR interval, p-wave index and dispersion, and partial interatrial or advanced interatrial block, or morphological parameters, such as p-wave axis, amplitude and terminal force. Ventricular predictors include abnormalities in QRS amplitude, morphology or duration, QT interval duration, r-wave progression and ST segment, i.e., t-wave abnormalities. CONCLUSIONS There has been significant interest in electrocardiographic prediction of AF, especially in populations at high risk of atrial AF, such as those with an embolic stroke of undetermined source. This review highlights the breadth of possible predictive parameters, and possible pathological bases for the predictive role of each parameter are proposed.
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Affiliation(s)
- Panagiota Anna Chousou
- Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Rahul Chattopadhyay
- Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Vasiliki Tsampasian
- Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Norfolk and Norwich University Hospital NHS Foundation Trust, Norwich NR4 7UY, UK
| | - Vassilios S Vassiliou
- Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Norfolk and Norwich University Hospital NHS Foundation Trust, Norwich NR4 7UY, UK
| | - Peter John Pugh
- Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
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Lopez Perales CR, Perez Guerrero A, Grados Saso D, Salvador Casabona JM. Advanced interatrial block as a predictor of cardioembolic stroke: is it time to change our clinical practice? Neurologia 2022; 37:413-415. [PMID: 34518026 DOI: 10.1016/j.nrl.2021.06.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 06/06/2021] [Accepted: 06/29/2021] [Indexed: 01/13/2023] Open
Affiliation(s)
- C R Lopez Perales
- Servicio de Cardiología, Hospital de Barbastro, Huesca, España; Servicio de Cardiología, Unidad de Electrofisiología Cardíaca, Hospital Universitario Miguel Servet, Zaragoza, España.
| | - A Perez Guerrero
- Servicio de Cardiología, Hospital de Barbastro, Huesca, España; Servicio de Cardiología, Unidad de Hemodinámica, Hospital Universitario Miguel Servet, Zaragoza, España
| | - D Grados Saso
- Servicio de Cardiología, Hospital de Barbastro, Huesca, España
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López Perales CR, Pérez Guerrero A, Grados Saso D, Salvador Casabona JM. Advanced interatrial block as a predictor of cardioembolic stroke: is it time to change our clinical practice? NEUROLOGÍA (ENGLISH EDITION) 2022; 37:413-415. [PMID: 35599161 DOI: 10.1016/j.nrleng.2021.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 06/29/2021] [Indexed: 11/24/2022] Open
Affiliation(s)
- C R López Perales
- Servicio de Cardiología, Hospital de Barbastro, Huesca, Spain; Servicio de Cardiología, Unidad de Electrofisiología Cardíaca, Hospital Universitario Miguel Servet, Zaragoza, Spain.
| | - A Pérez Guerrero
- Servicio de Cardiología, Hospital de Barbastro, Huesca, Spain; Servicio de Cardiología, Unidad de Hemodinámica, Hospital Universitario Miguel Servet, Zaragoza, Spain
| | - D Grados Saso
- Servicio de Cardiología, Hospital de Barbastro, Huesca, Spain
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Power DA, Lampert J, Camaj A, Bienstock SW, Kocovic N, Bayes-Genis A, Miller MA, Bayés-de-Luna A, Fuster V. Cardiovascular Complications of Interatrial Conduction Block: JACC State-of-the-Art Review. J Am Coll Cardiol 2022; 79:1199-1211. [PMID: 35331415 DOI: 10.1016/j.jacc.2022.01.030] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 01/25/2022] [Indexed: 12/11/2022]
Abstract
Interatrial block (IAB) is an electrocardiographic pattern describing the conduction delay between the right and left atria. IAB is classified into 3 degrees of block that correspond to decreasing conduction in the region of Bachmann's bundle. Although initially considered benign in nature, specific subsets of IAB have been associated with atrial arrhythmias, elevated thromboembolic stroke risk, cognitive impairment, and mortality. As the pathophysiologic relationships between IAB and stroke are reinforced, investigation has now turned to the potential benefit of early detection, atrial imaging, cardiovascular risk factor modification, antiarrhythmic pharmacotherapy, and stroke prevention with oral anticoagulation. This review provides a contemporary overview of the epidemiology, pathophysiology, diagnosis, and management of IAB, with a focus on future directions.
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Affiliation(s)
- David A Power
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
| | - Joshua Lampert
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Anton Camaj
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Solomon W Bienstock
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Nikola Kocovic
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Antoni Bayes-Genis
- Heart Institute, Hospital Universitario Germans Trias I Pujol, Badalona, Spain
| | - Marc A Miller
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Antoni Bayés-de-Luna
- Cardiovascular Research Foundation, Cardiovascular ICCC-Program, Research Institute Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, Barcelona, Spain
| | - Valentin Fuster
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
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