Artificial Intelligence–Enhanced Electrocardiography Identifies Patients with Normal Ejection Fraction at Risk of Worse Outcomes
Open accessAbstract
Background: An artificial intelligence (AI)-based electrocardiogram (ECG) model identifies patients with a higher likelihood of low ejection fraction (EF). Patients with an abnormal AI-ECG score but normal EF (false positives; FP) more often developed future low EF. Objective: The purpose of this study was to evaluate echocardiographic characteristics and all-cause mortality risk in FP patients. Methods: Patients with transthoracic echocardiography and ECG were classified retrospectively into FP, true negatives (TN) (EF ≥50%, normal AI-ECG), true positives (TP) (EF <50%, abnormal AI-ECG), or false negatives (FN) (EF <50%, normal AI-ECG). Echocardiographic abnormalities included systolic and diastolic left ventricular function, valve disease, estimated pulmonary pressures, and right heart parameters. Cox regression was used to assess factors associated with all-cause mortality. Results: Of 100,586 patients (median age 63 years; 45.5% females), 79% were TN, 7% FP, 5% FN, and 8% TP. FPs had more echocardiographic abnormalities than TN but less than FN or TP patients. An echocardiographic abnormality was present in 97% of FPs. Over median 2.7 years, FPs had increased mortality risk (age and sex-adjusted HR: 1.64 [95% CI: 1.55-1.73]) vs TN. Age and sex-adjusted mortality was higher in FP with abnormal echocardiography than FP with normal echocardiography and to TN regardless of echocardiography result; FP with normal echocardiography had comparable mortality risk to TN with abnormal echocardiography. Conclusions: FP patients were more likely than TNs to have echocardiographic abnormalities with 97% of exams showing an abnormality. FP patients had higher mortality rates, especially when their echocardiograms also had an abnormality; the concomitant use of AI ECG and echocardiography helps in stratifying risk in patients with normal LVEF.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI-ECG may identify a broader latent cardiac phenotype rather than merely detect current low ejection fraction.
“This suggests that AI-ECG may identify a broader latent cardiac phenotype rather than merely detect current low ejection fraction.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [22]
Patients with abnormal AI-ECG results despite normal contemporaneous systolic function were more likely to have other structural abnormalities or worse outcomes compared with true-negative patients.
“In outcome-focused analyses, patients with abnormal AI-ECG results despite normal contemporaneous systolic function were more likely to have other structural abnormalities or worse outcomes compared with true-negative patients [22].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [22]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
James, S. L., Abate, D., Abate, K. H., Abay, S. M., Abbafati, C., Abbasi, N., Abbastabar, H., Abd-Allah, F., Abdela, J., Abdelalim, A., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(18)32279-7 · Cited by 13,867 · see citing works
Lang, R., Badano, L., Mor-Avi, V., Afilalo, J., Armstrong, A., Ernande, L., Flachskampf, F., Foster, E., Goldstein, S., Kuznetsova, T., et al. (2015) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2014.10.003 · Cited by 10,093 · see citing works
Rudski, L., Lai, W. W., Afilalo, J., Hua, L., Handschumacher, M. D., Chandrasekaran, K., Solomon, S. D., Louie, E. K., & Schiller, N. B. (2010) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2010.05.010 · Cited by 7,080 · see citing works
Nagueh, S. F., Smiseth, O. A., Appleton, C. P., Byrd, B. F., Dokainish, H., Edvardsen, T., Flachskampf, F. A., Gillebert, T. C., Klein, A. L., Lancellotti, P., et al. (2016) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2016.01.011 · Cited by 6,790 · see citing works
Self-citationHeidenreich, P. A., Bozkurt, B., Aguilar, D., Allen, L. A., Byun, J. J., Colvin, M. M., Deswal, A., Drazner, M. H., Dunlay, S. M., Evers, L. R., et al. (2022) · Circulation · doi:10.1161/cir.0000000000001063 · Cited by 4,922 · see citing works
Global burden of heart failure: a comprehensive and updated review of epidemiology
Savarese, G., Becher, P. M., Lund, L. H., Seferovic, P. M., Rosano, G. M. C., & Coats, A. J. S. (2022) · Cardiovascular Research · doi:10.1093/cvr/cvac013 · Cited by 3,482 · see citing works
Mitchell, C., Rahko, P. S., Blauwet, L. A., Canaday, B., Finstuen, J. A., Foster, M., Horton, K. D., Ogunyankin, K. O., Palma, R. A., & Velazquez, E. J. (2018) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2018.06.004 · Cited by 2,571 · see citing works
Attia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., Pellikka, P. A., Enriquez-Sarano, M., Noseworthy, P. A., Munger, T. M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0240-2 · Cited by 1,389 · see citing works
Self-citationNatural history of asymptomatic left ventricular systolic dysfunction in the community
Wang, T. J., Evans, J. C., Benjamin, E. J., Levy, D., LeRoy, E. C., & Vasan, R. S. (2003) · Circulation · doi:10.1161/01.cir.0000085166.44904.79 · Cited by 633 · see citing works
Pagidipati, N. J., & Gaziano, T. A. (2013) · Circulation · doi:10.1161/circulationaha.112.128413 · Cited by 504 · see citing works
Yao, X., Rushlow, D. R., Inselman, J. W., McCoy, R. G., Thacher, T. D., Behnken, E. M., Bernard, M. E., Rosas, S. L., Akfaly, A., Misra, A., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01335-4 · Cited by 424 · see citing works
Self-citationProfile for Estimating Risk of Heart Failure
Kannel, W. B., D’Agostino, R. B., Silbershatz, H., Belanger, A. J., Wilson, P. W. F., & Levy, D. A. (1999) · Archives of Internal Medicine · doi:10.1001/archinte.159.11.1197 · Cited by 422 · see citing works
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
Raghunath, S., Ulloa Cerna, A. E., Jing, L., vanMaanen, D. P., Stough, J., Hartzel, D. N., Leader, J. B., Kirchner, H. L., Stumpe, M. C., Hafez, A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0870-z · Cited by 314 · see citing works
Prediction of Incident Heart Failure in General Practice
Agarwal, S. K., Chambless, L. E., Ballantyne, C. M., Astor, B. C., Bertoni, A. G., Chang, P. P., Folsom, A. R., He, M. M., Hoogeveen, R. C., Ni, H., et al. (2012) · Circulation Heart Failure · doi:10.1161/circheartfailure.111.964841 · Cited by 267 · see citing works
Incident Heart Failure Prediction in the Elderly
Butler, J., Kalogeropoulos, A. P., Georgiopoulou, V. V., BeLue, R., Rodondi, N., García, M., Bauer, D. C., Satterfield, S., Smith, A. L., Vaccarino, V., et al. (2008) · Circulation Heart Failure · doi:10.1161/circheartfailure.108.768457 · Cited by 257 · see citing works
National Trends in Heart Failure Hospitalizations and Readmissions From 2010 to 2017
Agarwal, M. A., Fonarow, G. C., & Ziaeian, B. (2021) · JAMA Cardiology · doi:10.1001/jamacardio.2020.7472 · Cited by 200 · see citing works
10-Year Risk Equations for Incident Heart Failure in the General Population
Khan, S. S., Ning, H., Shah, S. J., Yancy, C. W., Carnethon, M. R., Berry, J. D., Mentz, R. J., O'Brien, E. C., Correa, A., Suthahar, N., et al. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.02.057 · Cited by 199 · see citing works
Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction
Attia, Z. I., Harmon, D. M., Dugan, J., Manka, L., Lopez-Jimenez, F., Lerman, A., Siontis, K. C., Noseworthy, P. A., Yao, X., Klavetter, E. W., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02053-1 · Cited by 154 · see citing works
Self-citationArtificial Intelligence-Enabled Electrocardiography to Screen Patients with Dilated Cardiomyopathy
Shrivastava, S., Cohen, M. S., Attia, Z. I., Rosenbaum, A. N., Wang, L., Giudicessi, J. R., Redfield, M. M., Bailey, K. R., López-Jiménez, F. A., Lin, G., et al. (2021) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2021.06.021 · Cited by 47 · see citing works
Self-citationHarmon, D. M., Carter, R. E., Cohen-Shelly, M., Svatikova, A., Adedinsewo, D. A., Noseworthy, P. A., Kapa, S., Lopez-Jimenez, F., Friedman, P. A., & Attia, Z. I. (2022) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztac028 · Cited by 35 · see citing works
Self-citationKlein, C. J., Ӧzcan, I., Attia, Z. I., Cohen, M. S., Lerman, A., Medina‐Inojosa, J. R., López-Jiménez, F. A., Friedman, P. A., Milone, M., & Shelly, S. (2022) · Mayo Clinic Proceedings Innovations Quality & Outcomes · doi:10.1016/j.mayocpiqo.2022.08.003 · Cited by 11 · see citing works
Self-citation
Flagged first, then by citations
1 of the 22 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
deFilippi, C. R., Awwad, A., Bloomfield, G. S., Weir, I. R., Ribaudo, H. J., Zanni, M. V., Fichtenbaum, C. J., Malvestutto, C., Aberg, J. A., Diggs, M. R., et al. (2025) · AIDS · doi:10.1097/qad.0000000000004243 · Cited by 3 · see citing works
AI in Cardiology: Improving Outcomes for All
Ahmad, F. S., Al-Kindi, S. G., & Steinhubl, S. R. (2024) · JACC Advances · doi:10.1016/j.jacadv.2024.101229 · Cited by 2 · see citing works
Tran, H. H.-V., Thu, A., Fuertes, A., Twayana, A. R., Mahadevaiah, A., Mehta, K. A., James, M., Basta, M., Weissman, S. I., Frishman, W. H., et al. (2025) · Cardiology in Review · doi:10.1097/crd.0000000000000975 · Cited by 4 · see citing works
Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease
Shaulian, S., Zeltser, R., & Makaryus, A. N. (2026) · Diagnostics · doi:10.3390/diagnostics16172834
Expertly used unsupervised clustering provides clinical tools as well as insight
de Bie, J. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaf015
Fairweather, D., Aung, N., & Carter, R. E. (2025) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2025.1604528
Borde, D. P., Joshi, S. S., Chidambaram, K., Jasapara, A., Thimmarayappa, A., Devarakonda, B. V., Kamat, S. S., Gandhe, U., Muniraj, G., Madanaik, S. S., et al. (2026) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2026.03.002
Diagnostic Tests for Stage B Heart Failure
Dang, H. N. N., Luong, T. V., Huynh, Q. L., & Marwick, T. H. (2026) · Current Cardiology Reports · doi:10.1007/s11886-026-02389-x
- Authors
- Naser, J. A., Lee, E., Lopez-Jimenez, F., Noseworthy, P. A., Latif, O. S., Friedman, P. A., Lin, G., Oh, J. K., Scott, C. G., Pislaru, S. V., Attia, Z. I., & Pellikka, P. A.
- Affiliations
- Mayo Clinic (US)
- Corresponding author
- Pellikka, Patricia A.
- ORCID
- Venue
- JACC: Advances
- Publisher
- Elsevier BV
- Published
- 2024-09
- Volume
- 3
- Issue
- 9
- Pages
- 101179
- ISSN
- 2772-963X (ISSN-L)
- PMC
- PMC11450892
- OpenAlex
- W4401943881
- Cited by
- 8
- References
- 22
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 1
- FWCI
- 2.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- JACC Advances (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- 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 › Cardiovascular Function and Risk Factors
- Keywords
- normal ejection fraction, false positives, all-cause mortality, echocardiographic abnormalities, left ventricular function, risk stratification, transthoracic echocardiography, left ventricular ejection fraction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
