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      Wide Complex Tachycardia Differentiation: A Reappraisal of the State‐of‐the‐Art

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          Abstract

          The primary goal of the initial ECG evaluation of every wide complex tachycardia is to determine whether the tachyarrhythmia has a ventricular or supraventricular origin. The answer to this question drives immediate patient care decisions, ensuing clinical workup, and long‐term management strategies. Thus, the importance of arriving at the correct diagnosis cannot be understated and has naturally spurred rigorous research, which has brought forth an ever‐expanding abundance of manually applied and automated methods to differentiate wide complex tachycardias. In this review, we provide an in‐depth analysis of traditional and more contemporary methods to differentiate ventricular tachycardia and supraventricular wide complex tachycardia. In doing so, we: (1) review hallmark wide complex tachycardia differentiation criteria, (2) examine the conceptual and structural design of standard wide complex tachycardia differentiation methods, (3) discuss practical limitations of manually applied ECG interpretation approaches, and (4) highlight recently formulated methods designed to differentiate ventricular tachycardia and supraventricular wide complex tachycardia automatically.

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          Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram

          Asymptomatic left ventricular dysfunction (ALVD) is present in 3-6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found1-4. An inexpensive, noninvasive screening tool for ALVD in the doctor's office is not available. We tested the hypothesis that application of artificial intelligence (AI) to the electrocardiogram (ECG), a routine method of measuring the heart's electrical activity, could identify ALVD. Using paired 12-lead ECG and echocardiogram data, including the left ventricular ejection fraction (a measure of contractile function), from 44,959 patients at the Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7%, respectively. In patients without ventricular dysfunction, those with a positive AI screen were at 4 times the risk (hazard ratio, 4.1; 95% confidence interval, 3.3 to 5.0) of developing future ventricular dysfunction compared with those with a negative screen. Application of AI to the ECG-a ubiquitous, low-cost test-permits the ECG to serve as a powerful screening tool in asymptomatic individuals to identify ALVD.
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            Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs

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              Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram

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                Author and article information

                Contributors
                may.adam@wustl.edu
                Journal
                J Am Heart Assoc
                J Am Heart Assoc
                10.1002/(ISSN)2047-9980
                JAH3
                ahaoa
                Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
                John Wiley and Sons Inc. (Hoboken )
                2047-9980
                03 June 2020
                02 June 2020
                : 9
                : 11 ( doiID: 10.1002/jah3.v9.11 )
                : e016598
                Affiliations
                [ 1 ] Department of Medicine Mayo Clinic Rochester MN
                [ 2 ] Department of Cardiovascular Diseases Mayo Clinic Rochester MN
                [ 3 ] Cardiovascular Division Washington University School of Medicine St. Louis MO
                Author notes
                [*] [* ]Correspondence to: Adam M. May, MD, 660 South Euclid Avenue, CB 8086, St. Louis, MO 63110. E‐mail: may.adam@ 123456wustl.edu
                Article
                JAH35132
                10.1161/JAHA.120.016598
                7428989
                32427020
                11165ffd-4e65-4053-b325-53932ef43ce2
                © 2020 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 19 March 2019
                : 13 April 2020
                Page count
                Figures: 2, Tables: 0, Pages: 9, Words: 6140
                Funding
                Funded by: Department of Cardiovascular Diseases at Mayo Clinic
                Categories
                Mini‐Review
                Mini‐Review
                Custom metadata
                2.0
                02 June 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.5 mode:remove_FC converted:19.07.2020

                Cardiovascular Medicine
                ecg,supraventricular tachycardia,ventricular tachycardia,wide complex tachycardia,electrophysiology

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