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Cited by 21ECG Monitoring and AnalysisArtificial Intelligence in HealthcareArtificial Intelligence in Healthcare and Education

Artificial Intelligence-Based Identification of Left Ventricular Systolic Dysfunction from 12-Lead Electrocardiograms: External Validation and Advanced Application of an Existing Model

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König, Sebastian, Hohenstein, Sven, Nitsche, Anne, Pellissier, Vincent, Leiner, Johannes, Stellmacher, Lars, Hindricks, Gerhard, Bollmann, Andreas·2024·European Heart Journal - Digital Health
DOI 10.1093/ehjdh/ztad081PMC PMC10944686
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Abstract

Aims: The diagnostic application of artificial intelligence (AI)-based models to detect cardiovascular diseases from electrocardiograms (ECGs) evolves, and promising results were reported. However, external validation is not available for all published algorithms. The aim of this study was to validate an existing algorithm for the detection of left ventricular systolic dysfunction (LVSD) from 12-lead ECGs. Methods and results: Patients with digitalized data pairs of 12-lead ECGs and echocardiography (at intervals of ≤7 days) were retrospectively selected from the Heart Center Leipzig ECG and electronic medical records databases. A previously developed AI-based model was applied to ECGs and calculated probabilities for LVSD. The area under the receiver operating characteristic curve (AUROC) was computed overall and in cohorts stratified for baseline and ECG characteristics. Repeated echocardiography studies recorded ≥3 months after index diagnostics were used for follow-up (FU) analysis. At baseline, 42 291 ECG-echocardiography pairs were analysed, and AUROC for LVSD detection was 0.88. Sensitivity and specificity were 82% and 77% for the optimal LVSD probability cut-off based on Youden's J. AUROCs were lower in ECG subgroups with tachycardia, atrial fibrillation, and wide QRS complexes. In patients without LVSD at baseline and available FU, model-generated high probability for LVSD was associated with a four-fold increased risk of developing LVSD during FU. Conclusion: We provide the external validation of an existing AI-based ECG-analysing model for the detection of LVSD with robust performance metrics. The association of false positive LVSD screenings at baseline with a deterioration of ventricular function during FU deserves a further evaluation in prospective trials.

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  • An existing AI-ECG algorithm externally validated in a European cohort had good overall discrimination for left ventricular systolic dysfunction.

    “König et al. externally validated an existing AI-ECG algorithm in a European cohort and found good overall discrimination for left ventricular systolic dysfunction, although performance was lower in certain subgroups, including patients with tachycardia, atrial fibrillation, or wide QRS complexes [21].”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [21]

  • The AI-ECG algorithm's performance was lower in certain subgroups, including patients with tachycardia, atrial fibrillation, or wide QRS complexes.

    “König et al. externally validated an existing AI-ECG algorithm in a European cohort and found good overall discrimination for left ventricular systolic dysfunction, although performance was lower in certain subgroups, including patients with tachycardia, atrial fibrillation, or wide QRS complexes [21].”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [21]

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Research works printed but not linked: 36

  1. [1]Escobar C, Palacios B, Varela L, Gutierrez M, Duong M, Chen H, et al Healthcare resource utilization and costs among patients with heart failure with preserved, mildly reduced, and reduced ejection fraction in Spain. BMC Health Serv Res 2022; 22: 1241. 36209120 PMC9547468
  2. [2]Odegaard KM, Hallen J, Lirhus SS, Melberg HO, Halvorsen S. Incidence, prevalence, and mortality of heart failure: a nationwide registry study from 2013 to 2016. ESC Heart Fail 2020; 7: 1917– 1926. 32530563 PMC7373922
  3. [3]McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Bohm M, et al 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J 2021; 42: 3599– 3726. 34447992
  4. [4]Averina M, Stylidis M, Brox J, Schirmer H. NT-ProBNP and high-sensitivity troponin T as screening tests for subclinical chronic heart failure in a general population. ESC Heart Fail 2022; 9: 1954– 1962. 35322586 PMC9065856
  5. [5]Adedinsewo D, Carter RE, Attia Z, Johnson P, Kashou AH, Dugan JL, et al Artificial intelligence-enabled ECG algorithm to identify patients with left ventricular systolic dysfunction presenting to the emergency department with dyspnea. Circ Arrhythm Electrophysiol 2020; 13: e008437. 32986471
  6. [6]Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, et al Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med 2019; 25: 70– 74. 30617318
  7. [7]Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol 2021; 18: 465– 478. 33526938 PMC7848866
  8. [8]Somani S, Russak AJ, Richter F, Zhao S, Vaid A, Chaudhry F, et al Deep learning and the electrocardiogram: review of the current state-of-the-art. Europace 2021; 23: 1179– 1191. 33564873 PMC8350862
  9. [9]Sun JY, Qiu Y, Guo HC, Hua Y, Shao B, Qiao YC, et al A method to screen left ventricular dysfunction through ECG based on convolutional neural network. J Cardiovasc Electrophysiol 2021; 32: 1095– 1102. 33565217
  10. [10]Attia ZI, Harmon DM, Behr ER, Friedman PA. Application of artificial intelligence to the electrocardiogram. Eur Heart J 2021; 42: 4717– 4730. 34534279 PMC8500024
  11. [12]Chen HY, Lin CS, Fang WH, Lou YS, Cheng CC, Lee CC, et al Artificial intelligence-enabled electrocardiography predicts left ventricular dysfunction and future cardiovascular outcomes: a retrospective analysis. J Pers Med 2022; 12: 455. 35330455 PMC8950054
  12. [13]Chung CT, Lee S, King E, Liu T, Armoundas AA, Bazoukis G, et al Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis. Int J Arrhythmia 2022; 23: 24. 36212507 PMC9525157
  13. [14]Yagi R, Goto S, Katsumata Y, MacRae CA, Deo RC. Importance of external validation and subgroup analysis of artificial intelligence in the detection of low ejection fraction from electrocardiograms. Eur Heart J Digit Health 2022; 3: 654– 657. 36710903 PMC9779862
  14. [15]Christopoulos G, Graff-Radford J, Lopez CL, Yao X, Attia ZI, Rabinstein AA, et al Artificial intelligence-electrocardiography to predict incident atrial fibrillation: a population-based study. Circ Arrhythm Electrophysiol 2020; 13: e009355. 33185118 PMC8127001
  15. [16]Noseworthy PA, Attia ZI, Brewer LC, Hayes SN, Yao X, Kapa S, et al Assessing and mitigating bias in medical artificial intelligence: the effects of race and ethnicity on a deep learning model for ECG analysis. Circ Arrhythm Electrophysiol 2020; 13: e007988. 32064914 PMC7158877
  16. [17]Elias P, Poterucha TJ, Rajaram V, Moller LM, Rodriguez V, Bhave S, et al Deep learning electrocardiographic analysis for detection of left-sided valvular heart disease. J Am Coll Cardiol 2022; 80: 613– 626. 35926935
  17. [18]Thiele C, Hirschfeld G. cutpointr: improved estimation and validation of optimal cutpoints in R. J Stat Softw 2021; 98: 27.
  18. [19]Attia IZ, Tseng AS, Benavente ED, Medina-Inojosa JR, Clark TG, Malyutina S, et al External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. Int J Cardiol 2021; 329: 130– 135. 33400971 PMC7955278
  19. [20]Katsushika S, Kodera S, Nakamoto M, Ninomiya K, Inoue S, Sawano S, et al The effectiveness of a deep learning model to detect left ventricular systolic dysfunction from electrocardiograms. Int Heart J 2021; 62: 1332– 1341. 34853226
  20. [21]Cho J, Lee B, Kwon JM, Lee Y, Park H, Oh BH, et al Artificial intelligence algorithm for screening heart failure with reduced ejection fraction using electrocardiography. ASAIO J 2021; 67: 314– 321. 33627606
  21. [22]Kwon JM, Kim KH, Jeon KH, Kim HM, Kim MJ, Lim SM, et al Development and validation of deep-learning algorithm for electrocardiography-based heart failure identification. Korean Circ J 2019; 49: 629– 639. 31074221 PMC6597456
  22. [23]Vaid A, Johnson KW, Badgeley MA, Somani SS, Bicak M, Landi I, et al Using deep-learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram. JACC Cardiovasc Imaging 2021; 15: 395– 410. 34656465 PMC8917975
  23. [24]Attia ZI, Kapa S, Yao X, Lopez-Jimenez F, Mohan TL, Pellikka PA, et al Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction. J Cardiovasc Electrophysiol 2019; 30: 668– 674. 30821035
  24. [25]Kashou AH, Medina-Inojosa JR, Noseworthy PA, Rodeheffer RJ, Lopez-Jimenez F, Attia IZ, et al Artificial intelligence-augmented electrocardiogram detection of left ventricular systolic dysfunction in the general population. Mayo Clin Proc 2021; 96: 2576– 2586. 34120755 PMC9904428
  25. [26]Perez-Downes J, Fitzgerald P, Adedinsewo D, Carter RE, Noseworthy PA, Kusumoto F. Impact of ECG characteristics on the performance of an artificial intelligence enabled ECG for predicting left ventricular dysfunction. Circ Arrhythm Electrophysiol 2021; 14: e009871. 33993719
  26. [27]Yao X, Rushlow DR, Inselman JW, McCoy RG, Thacher TD, Behnken EM, et al Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med 2021; 27: 815– 819. 33958795
  27. [28]Johnson K, Neilson S, To A, Amir N, Cave A, Scott T, et al Advanced electrocardiography identifies left ventricular systolic dysfunction in non-ischemic cardiomyopathy and tracks serial change over time. J Cardiovasc Dev Dis 2015; 2: 93– 107. 29371514 PMC5753097
  28. [29]Rautaharju PM, Kooperberg C, Larson JC, LaCroix A. Electrocardiographic predictors of incident congestive heart failure and all-cause mortality in postmenopausal women: the Women’s Health Initiative. Circulation 2006; 113: 481– 489. 16449727
  29. [30]Rautaharju PM, Zhang ZM, Haisty WK Jr, Prineas RJ, Kucharska-Newton AM, Rosamond WD, et al Electrocardiographic predictors of incident heart failure in men and women free from manifest cardiovascular disease (from the Atherosclerosis Risk in Communities [ARIC] study). Am J Cardiol 2013; 112: 843– 849. 23768456 PMC3782380
  30. [31]Schlegel TT, Kulecz WB, Feiveson AH, Greco EC, DePalma JL, Starc V, et al Accuracy of advanced versus strictly conventional 12-lead ECG for detection and screening of coronary artery disease, left ventricular hypertrophy and left ventricular systolic dysfunction. BMC Cardiovasc Disord 2010; 10: 28. 20565702 PMC2894002
  31. [32]Katsushika S, Kodera S, Sawano S, Shinohara H, Setoguchi N, Tanabe K, et al An explainable artificial intelligence-enabled electrocardiogram analysis model for the classification of reduced left ventricular function. Eur Heart J Digit Health 2023; 4: 254– 264. 37265859 PMC10232279
  32. [33]van de Leur RR, Bos MN, Taha K, Sammani A, Yeung MW, van Duijvenboden S, et al Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders. Eur Heart J Digit Health 2022; 3: 390– 404. 36712164 PMC9707974
  33. [34]Harmon DM, Carter RE, Cohen-Shelly M, Svatikova A, Adedinsewo DA, Noseworthy PA, et al Real-world performance, long-term efficacy, and absence of bias in the artificial intelligence enhanced electrocardiogram to detect left ventricular systolic dysfunction. Eur Heart J Digit Health 2022; 3: 238– 244. 36247412 PMC9558265
  34. [35]Jentzer JC, Kashou AH, Attia ZI, Lopez-Jimenez F, Kapa S, Friedman PA, et al Left ventricular systolic dysfunction identification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients. Int J Cardiol 2021; 326: 114– 123. 33152415
  35. [36]Golany T, Radinsky K, Kofman N, Litovchik I, Young R, Monayer A, et al Physicians and machine-learning algorithm performance in predicting left-ventricular systolic dysfunction from a standard 12-lead-electrocardiogram. J Clin Med 2022; 11: 6767. 36431244 PMC9699306
  36. [37]Huang YC, Hsu YC, Liu ZY, Lin CH, Tsai R, Chen JS, et al Artificial intelligence-enabled electrocardiographic screening for left ventricular systolic dysfunction and mortality risk prediction. Front Cardiovasc Med 2023; 10: 1070641. 36960474 PMC10029758

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Authors
König, S., Hohenstein, S., Nitsche, A., Pellissier, V., Leiner, J., Stellmacher, L., Hindricks, G., & Bollmann, A.
Affiliations
  • Leipzig Heart Institute (DE)
  • Helios Health Institute, Real World Evidence & Health Technology Assessment , Schwanebecker Chaussee 50, 13125 Berlin , Germany
Corresponding author
König, Sebastian
ORCID
Venue
European Heart Journal - Digital Health
Publisher
Oxford University Press (OUP)
Published
2023-12-20
Volume
5
Issue
2
Pages
144-151
ISSN
2634-3916 (ISSN-L)
DOI
10.1093/ehjdh/ztad081
PMC
PMC10944686
OpenAlex
W4390028987
Cited by
21
References
1
Citations by year
2026: 6 · 2025: 11 · 2024: 4
FWCI
4.13
Type
journal article
Language
en
Open access
Licence
Locations
  • European Heart Journal - Digital Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Brigham and Women's Hospital
  • Keio University
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
left ventricular systolic dysfunction, 12-lead ECG, artificial intelligence, external validation, echocardiographic follow-up, atrial fibrillation, tachycardia, wide QRS complex, false-positive screening results

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