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August 11, 2025European Heart Journal - Digital HealthOpen Access

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

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Authors

DHDavid HongSSSung‐Hee SongHSHeayoung Shin

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Overview

Retrospective cohort study developed an AI-enabled ECG model predicting heart failure in patients, suggesting improved diagnostics.

Key Points

  • The AI-enabled electrocardiogram model predicts heart failure with preserved ejection fraction effectively.
  • It achieved an area under the receiver operating characteristic curve of 0.81, indicating good model performance.
  • Patients classified as HFpEF showed significantly worse outcomes, increasing hospitalizations and risks of cardiac death.
  • This model may enhance the diagnostic accuracy for heart failure with preserved ejection fraction in clinical settings.

Cite This Study

Hong et al. (2025) studied this question.

synapsesocial.com/papers/68a35ee30a429f7973327c0fhttps://doi.org/10.1093/ehjdh/ztaf080
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1An Artificial Intelligence Model for ECG-Based Prediction of Heart Failure with Preserved Ejection Fraction Diagnosis2026
  2. 2Electrocardiography-based artificial intelligence predicts the upcoming future of heart failure with mildly reduced ejection fraction2025 · 3 citations
  3. 3Twelve-lead electrocardiography-based artificial intelligence predicts the upcoming future of patients with heart failure with mildly reduced ejection fraction2024
  4. 4Artificial Intelligence–Enhanced Electrocardiography for the Diagnosis of Heart Failure With Preserved Ejection Fraction: A Systematic Review and Meta‐Analysis2026
  5. 5Detection of hospital-admitted heart failure regardless of ejection fraction using artificial intelligence-enhanced electrocardiograms2025 · 1 citations