Literature DB >> 33400971

External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction.

Itzhak Zachi Attia1, Andrew S Tseng1, Ernest Diez Benavente2, Jose R Medina-Inojosa1, Taane G Clark3, Sofia Malyutina4, Suraj Kapa1, Henrik Schirmer5, Alexander V Kudryavtsev6, Peter A Noseworthy1, Rickey E Carter7, Andrew Ryabikov4, Pablo Perel8, Paul A Friedman1, David A Leon9, Francisco Lopez-Jimenez10.   

Abstract

OBJECTIVE: To validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population.
BACKGROUND: LVSD, even when asymptomatic, confers increased morbidity and mortality. We recently derived AI-ECG to detect LVSD using ECGs based on a large sample of patients treated at the Mayo Clinic.
METHODS: We performed an external validation study with subjects from the Know Your Heart Study, a cross-sectional study of adults aged 35-69 years residing in two cities in Russia, who had undergone both ECG and transthoracic echocardiography. LVSD was defined as left ventricular ejection fraction ≤ 35%. We assessed the performance of the AI-ECG to identify LVSD in this distinct patient population.
RESULTS: Among 4277 subjects in this external population-based validation study, 0.6% had LVSD (compared to 7.8% of the original clinical derivation study). The overall performance of the AI-ECG to detect LVSD was robust with an area under the receiver operating curve of 0.82. When using the LVSD probability cut-off of 0.256 from the original derivation study, the sensitivity, specificity, and accuracy in this population were 26.9%, 97.4%, 97.0%, respectively. Other probability cut-offs were analysed for different sensitivity values.
CONCLUSIONS: The AI-ECG detected LVSD with robust test performance in a population that was very different from that used to develop the algorithm. Population-specific cut-offs may be necessary for clinical implementation. Differences in population characteristics, ECG and echocardiographic data quality may affect test performance.
Copyright © 2020. Published by Elsevier B.V.

Entities:  

Keywords:  Artificial intelligence; Electrocardiogram; Left ventricular systolic dysfunction; Machine learning

Year:  2021        PMID: 33400971      PMCID: PMC7955278          DOI: 10.1016/j.ijcard.2020.12.065

Source DB:  PubMed          Journal:  Int J Cardiol        ISSN: 0167-5273            Impact factor:   4.164


  13 in total

1.  Asymptomatic Left Ventricular Dysfunction: To Screen or Not to Screen?

Authors:  Véronique L Roger
Journal:  JACC Heart Fail       Date:  2016-02-10       Impact factor: 12.035

Review 2.  2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America.

Authors:  Clyde W Yancy; Mariell Jessup; Biykem Bozkurt; Javed Butler; Donald E Casey; Monica M Colvin; Mark H Drazner; Gerasimos S Filippatos; Gregg C Fonarow; Michael M Givertz; Steven M Hollenberg; JoAnn Lindenfeld; Frederick A Masoudi; Patrick E McBride; Pamela N Peterson; Lynne Warner Stevenson; Cheryl Westlake
Journal:  J Card Fail       Date:  2017-04-28       Impact factor: 5.712

3.  Left ventricular dysfunction, natriuretic peptides, and mortality in an urban population.

Authors:  T A McDonagh; A D Cunningham; C E Morrison; J J McMurray; I Ford; J J Morton; H J Dargie
Journal:  Heart       Date:  2001-07       Impact factor: 5.994

4.  Global Public Health Burden of Heart Failure.

Authors:  Gianluigi Savarese; Lars H Lund
Journal:  Card Fail Rev       Date:  2017-04

5.  The utility of clinical, electrocardiographic, and roentgenographic variables in the prediction of left ventricular function.

Authors:  C S Rihal; K B Davis; J W Kennedy; B J Gersh
Journal:  Am J Cardiol       Date:  1995-02-01       Impact factor: 2.778

6.  Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction.

Authors:  Zachi I Attia; Suraj Kapa; Xiaoxi Yao; Francisco Lopez-Jimenez; Tarun L Mohan; Patricia A Pellikka; Rickey E Carter; Nilay D Shah; Paul A Friedman; Peter A Noseworthy
Journal:  J Cardiovasc Electrophysiol       Date:  2019-03-10

7.  Electrical surrogate for detection of severe left ventricular systolic dysfunction.

Authors:  Kyndaron Reinier; Aapo L Aro; Audrey Uy-Evanado; Carmen Rusinaru; Harpriya S Chugh; Takahiro Shiota; Jonathan Jui; Sumeet S Chugh
Journal:  Ann Noninvasive Electrocardiol       Date:  2018-08-20       Impact factor: 1.468

8.  Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone.

Authors:  Omar Z Yasin; Zachi Attia; John J Dillon; Christopher V DeSimone; Yehu Sapir; Jennifer Dugan; Virend K Somers; Michael J Ackerman; Samuel J Asirvatham; Christopher G Scott; Kevin E Bennet; Dorothy J Ladewig; Dan Sadot; Amir B Geva; Paul A Friedman
Journal:  J Electrocardiol       Date:  2017-06-08       Impact factor: 1.438

9.  Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram.

Authors:  Zachi I Attia; Suraj Kapa; Francisco Lopez-Jimenez; Paul M McKie; Dorothy J Ladewig; Gaurav Satam; Patricia A Pellikka; Maurice Enriquez-Sarano; Peter A Noseworthy; Thomas M Munger; Samuel J Asirvatham; Christopher G Scott; Rickey E Carter; Paul A Friedman
Journal:  Nat Med       Date:  2019-01-07       Impact factor: 53.440

10.  Stress testing and non-invasive coronary angiography in patients with suspected coronary artery disease: time for a new paradigm.

Authors:  Armin Arbab-Zadeh
Journal:  Heart Int       Date:  2012-02-08
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  9 in total

Review 1.  Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools.

Authors:  Demilade A Adedinsewo; Amy W Pollak; Sabrina D Phillips; Taryn L Smith; Anna Svatikova; Sharonne N Hayes; Sharon L Mulvagh; Colleen Norris; Veronique L Roger; Peter A Noseworthy; Xiaoxi Yao; Rickey E Carter
Journal:  Circ Res       Date:  2022-02-17       Impact factor: 23.213

2.  Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study.

Authors:  Patrik Bachtiger; Camille F Petri; Francesca E Scott; Se Ri Park; Mihir A Kelshiker; Harpreet K Sahemey; Bianca Dumea; Regine Alquero; Pritpal S Padam; Isobel R Hatrick; Alfa Ali; Maria Ribeiro; Wing-See Cheung; Nina Bual; Bushra Rana; Matthew Shun-Shin; Daniel B Kramer; Alex Fragoyannis; Daniel Keene; Carla M Plymen; Nicholas S Peters
Journal:  Lancet Digit Health       Date:  2022-01-05

3.  Detecting cardiomyopathies in pregnancy and the postpartum period with an electrocardiogram-based deep learning model.

Authors:  Demilade A Adedinsewo; Patrick W Johnson; Erika J Douglass; Itzhak Zachi Attia; Sabrina D Phillips; Rohan M Goswami; Mohamad H Yamani; Heidi M Connolly; Carl H Rose; Emily E Sharpe; Lori Blauwet; Francisco Lopez-Jimenez; Paul A Friedman; Rickey E Carter; Peter A Noseworthy
Journal:  Eur Heart J Digit Health       Date:  2021-08-27

4.  Diagnosis and treatment of new heart failure with reduced ejection fraction by the artificial intelligence-enhanced electrocardiogram.

Authors:  David M Harmon; Daniel R Witt; Paul A Friedman; Zachi I Attia
Journal:  Cardiovasc Digit Health J       Date:  2021-08-24

5.  Artificial Intelligence-Enabled Electrocardiogram Predicted Left Ventricle Diameter as an Independent Risk Factor of Long-Term Cardiovascular Outcome in Patients With Normal Ejection Fraction.

Authors:  Hung-Yi Chen; Chin-Sheng Lin; Wen-Hui Fang; Chia-Cheng Lee; Ching-Liang Ho; Chih-Hung Wang; Chin Lin
Journal:  Front Med (Lausanne)       Date:  2022-04-11

6.  Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular Events.

Authors:  Yung-Tsai Lee; Chin-Sheng Lin; Wen-Hui Fang; Chia-Cheng Lee; Ching-Liang Ho; Chih-Hung Wang; Dung-Jang Tsai; Chin Lin
Journal:  Front Cardiovasc Med       Date:  2022-06-13

Review 7.  State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.

Authors:  Georgios Petmezas; Leandros Stefanopoulos; Vassilis Kilintzis; Andreas Tzavelis; John A Rogers; Aggelos K Katsaggelos; Nicos Maglaveras
Journal:  JMIR Med Inform       Date:  2022-08-15

8.  Real-world performance, long-term efficacy, and absence of bias in the artificial intelligence enhanced electrocardiogram to detect left ventricular systolic dysfunction.

Authors:  David M Harmon; Rickey E Carter; Michal Cohen-Shelly; Anna Svatikova; Demilade A Adedinsewo; Peter A Noseworthy; Suraj Kapa; Francisco Lopez-Jimenez; Paul A Friedman; Zachi I Attia
Journal:  Eur Heart J Digit Health       Date:  2022-05-17

9.  Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis.

Authors:  Hung-Yi Chen; Chin-Sheng Lin; Wen-Hui Fang; Yu-Sheng Lou; Cheng-Chung Cheng; Chia-Cheng Lee; Chin Lin
Journal:  J Pers Med       Date:  2022-03-13
  9 in total

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