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Cited by 14ECG Monitoring and AnalysisArtificial Intelligence in HealthcareHeart Failure Treatment and Management

Artificial Intelligence-Enabled Electrocardiogram Model for Predicting Heart Failure with Preserved Ejection Fraction: A Single-Center Study

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Hong, David, Song, Sung-Hee, Shin, Heayoung, Bak, Minjung, Kim, Juwon, Kim, Darae, Kim, Ju Youn, Yang, Jeong Hoon +4 more·2025·European Heart Journal - Digital Health
DOI 10.1093/ehjdh/ztaf080PMC PMC12450526
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Abstract

Aims: Heart failure with preserved ejection fraction (HFpEF) is difficult to diagnose due to the lack of a definitive diagnostic marker; multiple tests are required, including advanced evaluations. This study aimed to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model for predicting HFpEF. Methods and results: This retrospective cohort study included patients from a single tertiary centre who underwent echocardiography, N-terminal prohormone of B-type natriuretic peptide measurement, and ECG within a defined timeframe. Patients were classified as HFpEF (HFA-PEFF score ≥5) or control (HFA-PEFF score <5). Patients were divided into training, validation, and test subsets at a 7:1:2 ratio for model development and validation. Using the collected ECGs, a convolutional neural network was trained to predict HFpEF; its performance was assessed using the area under the receiver operating characteristic curve (AUROC). Among the 13 081 patients included, 5795 (44.3%) were classified as HFpEF and 7286 (55.7%) were classified as control. The AI-enabled ECG model demonstrated good discriminative performance [AUROC 0.81; 95% confidence interval (CI) 0.79–0.82]. Subgroup analyses stratified by HFpEF risk factors confirmed consistent model performance. Prognostic evaluation revealed that patients with a positive AI-ECG classification experienced significantly worse outcomes relative to those with a negative classification, including higher risks of cardiac death (1.1% vs. 0.1%; hazard ratio 9.56; 95% CI 1.24–73.53; P = 0.030) and heart failure hospitalization (2.8% vs. 0.6%; hazard ratio 5.91; 95% CI 2.08–16.81; P = 0.001) at 5 year. Conclusion: The AI-ECG model is a reliable tool for predicting HFpEF, as defined by the HFA-PEFF score, and effectively stratifies patients according to prognosis. Integration of this model into clinical practice may simplify and enhance the diagnostic process for HFpEF.

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Research works printed but not linked: 22

  1. [1]Hamo CE, DeJong C, Hartshorne-Evans N, Lund LH, Shah SJ, Solomon S, et al Heart failure with preserved ejection fraction. Nat Rev Dis Primers 2024; 10: 55. 39143132
  2. [2]Tribouilloy C, Rusinaru D, Mahjoub H, Soulière V, Lévy F, Peltier M, et al Prognosis of heart failure with preserved ejection fraction: a 5 year prospective population-based study. Eur Heart J 2008; 29: 339– 347. 18156618
  3. [3]Verbrugge FH, Reddy YNV, Sorimachi H, Omote K, Carter RE, Borlaug BA. Diagnostic scores predict morbidity and mortality in patients hospitalized for heart failure with preserved ejection fraction. Eur J Heart Fail 2021; 23: 954– 963. 33634544
  4. [4]McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm 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
  5. [5]Pieske B, Tschöpe C, de Boer RA, Fraser AG, Anker SD, Donal E, et al How to diagnose heart failure with preserved ejection fraction: the HFA-PEFF diagnostic algorithm: a consensus recommendation from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC). Eur Heart J 2019; 40: 3297– 3317. 31504452
  6. [6]Reddy YNV, Carter RE, Obokata M, Redfield MM, Borlaug BA. A simple, evidence-based approach to help guide diagnosis of heart failure with preserved ejection fraction. Circulation 2018; 138: 861– 870. 29792299 PMC6202181
  7. [7]Barandiarán Aizpurua A, Sanders-van Wijk S, Brunner-La Rocca HP, Henkens M, Heymans S, Beussink-Nelson L, et al Validation of the HFA-PEFF score for the diagnosis of heart failure with preserved ejection fraction. Eur J Heart Fail 2020; 22: 413– 421. 31472035
  8. [8]Tada A, Nagai T, Omote K, Iwano H, Tsujinaga S, Kamiya K, et al Performance of the H(2)FPEF and the HFA-PEFF scores for the diagnosis of heart failure with preserved ejection fraction in Japanese patients: a report from the Japanese multicenter registry. Int J Cardiol 2021; 342: 43– 48. 34364907
  9. [9]Lang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L, et al Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr 2015; 28: 1– 39.e14. 25559473
  10. [10]Nagueh SF, Smiseth OA, Appleton CP, Byrd BF, Dokainish H, Edvardsen T, et al Recommendations for the evaluation of left ventricular diastolic function by echocardiography: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr 2016; 29: 277– 314. 27037982
  11. [11]Huang G, Liu Z, Pleiss G, Maaten LV, Weinberger KQ. Convolutional networks with dense connectivity. IEEE Trans Pattern Anal Mach Intell 2022; 44: 8704– 8716. 31135351
  12. [12]Abraham WT, Psotka MA, Fiuzat M, Filippatos G, Lindenfeld J, Mehran R, et al Standardized definitions for evaluation of heart failure therapies: scientific expert panel from the heart failure collaboratory and academic research consortium. JACC Heart Fail 2020; 8: 961– 972. 33199251
  13. [13]Selvaraj S, Myhre PL, Vaduganathan M, Claggett BL, Matsushita K, Kitzman DW, et al Application of diagnostic algorithms for heart failure with preserved ejection fraction to the community. JACC Heart Fail 2020; 8: 640– 653. 32535127 PMC8030634
  14. [14]Anker SD, Butler J, Filippatos G, Ferreira JP, Bocchi E, Böhm M, et al Empagliflozin in heart failure with a preserved ejection fraction. N Engl J Med 2021; 385: 1451– 1461. 34449189
  15. [18]Reddy YNV, Carter RE, Sundaram V, Kaye DM, Handoko ML, Tedford RJ, et al An evidence-based screening tool for heart failure with preserved ejection fraction: the HFpEF-ABA score. Nat Med 2024; 30: 2258– 2264. 38997608 PMC11570987
  16. [19]Tromp J, Seekings PJ, Hung CL, Iversen MB, Frost MJ, Ouwerkerk W, et al Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study. Lancet Digit Health 2022; 4: e46– e54. 34863649
  17. [20]Akerman AP, Porumb M, Scott CG, Beqiri A, Chartsias A, Ryu AJ, et al Automated echocardiographic detection of heart failure with preserved ejection fraction using artificial intelligence. JACC Adv 2023; 2: 100452. 38939447 PMC11198161
  18. [21]Khan MS, Arshad MS, Greene SJ, Van Spall HGC, Pandey A, Vemulapalli S, et al Artificial intelligence and heart failure: a state-of-the-art review. Eur J Heart Fail 2023; 25: 1507– 1525. 37560778
  19. [22]Kagiyama N, Piccirilli M, Yanamala N, Shrestha S, Farjo PD, Casaclang-Verzosa G, et al Machine learning assessment of left ventricular diastolic function based on electrocardiographic features. J Am Coll Cardiol 2020; 76: 930– 941. 32819467
  20. [24]Reddy YNV, Obokata M, Verbrugge FH, Lin G, Borlaug BA. Atrial dysfunction in patients with heart failure with preserved ejection fraction and atrial fibrillation. J Am Coll Cardiol 2020; 76: 1051– 1064. 32854840 PMC7455760
  21. [25]Redfield MM, Borlaug BA. Heart failure with preserved ejection fraction: a review. JAMA 2023; 329: 827– 838. 36917048
  22. [26]Heidenreich PA, Bozkurt B, Aguilar D, Allen LA, Byun JJ, Colvin MM, et al 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: a Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 2022; 145: e895– e1032. 35363499

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Authors
Hong, D., Song, S.-H., Shin, H., Bak, M., Kim, J., Kim, D., Kim, J. Y., Yang, J. H., Park, S.-J., Choi, J.-O., On, Y. K., & Park, K.-M.
Affiliations
  • Samsung Medical Center (KR)
  • Wellysis Corp. , Seoul ,
  • Division of Cardiology, Department of Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine , 81 Irwon-ro, Gangnam-gu, Seoul 06351 ,
Corresponding author
Park, Kyoung-Min
ORCID
Venue
European Heart Journal - Digital Health
Publisher
Oxford University Press (OUP)
Published
2025-07-17
Volume
6
Issue
5
Pages
959-968
ISSN
2634-3916 (ISSN-L)
DOI
10.1093/ehjdh/ztaf080
PMC
PMC12450526
OpenAlex
W4413073860
Cited by
14
References
4
Citations by year
2026: 13 · 2025: 1
FWCI
9.78
Type
journal article
Language
en
Open access
Licence
Locations
  • European Heart Journal - Digital Health (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
Funders
  • Korea Medical Device Development Fund of the Korean government (1711174270, RS-2021-KD000008)
  • Basic Science Research Program through the National Research Foundation of Korea (NRF-2022R1A2C2004868)
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 › Cardiology and Cardiovascular Medicine › Heart Failure Treatment and Management
Keywords
heart failure with preserved ejection fraction, electrocardiogram, artificial intelligence, convolutional neural networks, HFA-PEFF score, cardiovascular mortality, heart failure hospitalization, prognostic stratification, diagnostic biomarkers

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