The authors declare that they have no conflicts of interest.
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent to publication
Not applicable.
Availability of data and materials
The primary data for this meta-analysis were sourced online from databases listed in the methods. Referenced articles are accessible on PubMed, Cochrane, CTG and Google Scholar.
Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.
References
Savarese G, Becher PM, Lund LH, Seferovic P, Rosano GMC, Coats AJS. Global burden of heart failure: a comprehensive and updated review of epidemiology.Cardiovasc Res. 2023;118:3272–87. [DOI] [PubMed]
Escobar C, Palacios B, Varela L, Gutiérrez 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. [DOI] [PubMed] [PMC]
Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, et al.; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association.Circulation. 2020;141:e139–596. [DOI] [PubMed]
Escobar C, Palacios B, Varela L, Gutiérrez M, Duong M, Chen H, et al. Prevalence, Characteristics, Management and Outcomes of Patients with Heart Failure with Preserved, Mildly Reduced, and Reduced Ejection Fraction in Spain.J Clin Med. 2022;11:5199. [DOI] [PubMed] [PMC]
Iyngkaran P, Thomas MC, Neil C, Jelinek M, Cooper M, Horowitz JD, et al. The Heart Failure with Preserved Ejection Fraction Conundrum-Redefining the Problem and Finding Common Ground?Curr Heart Fail Rep. 2020;17:34–42. [DOI] [PubMed]
Budde H, Hassoun R, Mügge A, Kovács Á, Hamdani N. Current Understanding of Molecular Pathophysiology of Heart Failure With Preserved Ejection Fraction.Front Physiol. 2022;13:928232. [DOI] [PubMed] [PMC]
Anker SD, Butler J, Filippatos G, Ferreira JP, Bocchi E, Böhm M, et al.; EMPEROR-Preserved Trial Investigators. Empagliflozin in Heart Failure with a Preserved Ejection Fraction.N Engl J Med. 2021;385:1451–61. [DOI] [PubMed]
Solomon SD, McMurray JJV, Claggett B, de Boer RA, DeMets D, Hernandez AF, et al.; DELIVER Trial Committees and Investigators. Dapagliflozin in Heart Failure with Mildly Reduced or Preserved Ejection Fraction.N Engl J Med. 2022;387:1089–98. [DOI] [PubMed]
Hagendorff A, Helfen A, Brandt R, Altiok E, Breithardt O, Haghi D, et al. Expert proposal to characterize cardiac diseases with normal or preserved left ventricular ejection fraction and symptoms of heart failure by comprehensive echocardiography.Clin Res Cardiol. 2023;112:1–38. [DOI] [PubMed] [PMC]
McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M, et al.; ESC Scientific Document Group. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure.Eur Heart J. 2021;42:3599–726. [DOI] [PubMed]
Almeida DR, Andrade FA. Invasive Hemodynamic Monitoring in the Diagnosis of Heart Failure with Preserved Ejection Fraction. ABC Heart Fail.Cardiomyop. 2022;2:290–5. [DOI]
He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine.Nat Med. 2019;25:30–6. [DOI] [PubMed] [PMC]
Harmon DM, Witt DR, Friedman PA, Attia ZI. Diagnosis and treatment of new heart failure with reduced ejection fraction by the artificial intelligence-enhanced electrocardiogram.Cardiovasc Digit Health J. 2021;2:282–4. [DOI] [PubMed] [PMC]
Zhao X, Zhang J, Gong Y, Xu L, Liu H, Wei S, et al. Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram.Front Physiol. 2022;13:854191. [DOI] [PubMed] [PMC]
Attia ZI, Harmon DM, Behr ER, Friedman PA. Application of artificial intelligence to the electrocardiogram.Eur Heart J. 2021;42:4717–30. [DOI] [PubMed] [PMC]
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews.BMJ. 2021;372:n71. [DOI] [PubMed] [PMC]
Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al.; QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.Ann Intern Med. 2011;155:529–36. [DOI] [PubMed]
Kwon JM, Kim KH, Eisen HJ, Cho Y, Jeon KH, Lee SY, et al. Artificial intelligence assessment for early detection of heart failure with preserved ejection fraction based on electrocardiographic features.Eur Heart J Digit Health. 2021;2:106–16. [DOI] [PubMed] [PMC]
Sengupta PP, Kulkarni H, Narula J. Prediction of Abnormal Myocardial Relaxation From Signal Processed Surface ECG.J Am Coll Cardiol. 2018;71:1650–60. [DOI] [PubMed]
Kuznetsova N, Gubina A, Sagirova Z, Dhif I, Gognieva D, Melnichuk A, et al. Left Ventricular Diastolic Dysfunction Screening by a Smartphone-Case Based on Single Lead ECG.Clin Med Insights Cardiol. 2022;16:11795468221120088. [DOI] [PubMed] [PMC]
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;76:930–41. [DOI] [PubMed]
Lee E, Ito S, Miranda WR, Lopez-Jimenez F, Kane GC, Asirvatham SJ, et al. Artificial intelligence-enabled ECG for left ventricular diastolic function and filling pressure.NPJ Digit Med. 2024;7:4. [DOI] [PubMed] [PMC]
Sabovčik F, Cauwenberghs N, Kouznetsov D, Haddad F, Alonso-Betanzos A, Vens C, et al. Applying machine learning to detect early stages of cardiac remodelling and dysfunction.Eur Heart J Cardiovasc Imaging. 2021;22:1208–17. [DOI] [PubMed]
Unterhuber M, Rommel KP, Kresoja KP, Lurz J, Kornej J, Hindricks G, et al. Deep learning detects heart failure with preserved ejection fraction using a baseline electrocardiogram.Eur Heart J Digit Health. 2021;2:699–703. [DOI] [PubMed] [PMC]
Schlesinger DE, Alam R, Ringel R, Pomerantsev E, Devireddy S, Shah P, et al. Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor.Commun Med (Lond). 2025;5:4. [DOI] [PubMed] [PMC]
Gao Z, Yang Y, Yang Z, Zhang X, Liu C. Electrocardiograph analysis for risk assessment of heart failure with preserved ejection fraction: A deep learning model.ESC Heart Fail. 2025;12:631–9. [DOI] [PubMed] [PMC]