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Related Experiment Video

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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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Understanding Transient Left Ventricular Ejection Fraction Reduction During Atrial Fibrillation With Artificial

Neal Yuan1,2, Gloria J Hong3, Amey Vrudhula3

  • 1School of Medicine University of California San Francisco CA USA.

Journal of the American Heart Association
|May 13, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can improve the diagnosis of reduced left ventricular ejection fraction (LVEF) during atrial fibrillation (AF). AI accurately identifies transient LVEF reduction, a phenotype linked to higher heart failure hospitalization risk.

Keywords:
atrial fibrillationdeep learningechocardiographyheart failure

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Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Echocardiography

Background:

  • Atrial fibrillation (AF) can cause a temporary reduction in left ventricular ejection fraction (LVEF) that typically resolves with the restoration of sinus rhythm.
  • Beat-to-beat variability in AF can lead to mismeasurement of LVEF, complicating accurate assessment.
  • The prognostic significance of true transient LVEF reduction during AF requires further investigation.

Purpose of the Study:

  • To determine the frequency of mismeasured LVEF in patients with AF due to beat-to-beat variability.
  • To assess whether genuine transient LVEF reduction during AF has prognostic implications for heart failure hospitalization.

Main Methods:

  • Observational study analyzing patients with echocardiograms in AF and subsequent sinus rhythm within 90 days.
  • Classification of patients based on LVEF changes: no reduction, transient reduction, or persistent reduction.
  • Utilized an AI algorithm for automated multicycle LVEF measurement to reclassify patients and Fine-Gray hazard modeling for 1-year heart failure hospitalization risk.

Main Results:

  • Of 810 patients, 34.6% had persistent LVEF reduction, 8.8% had transient reduction, and 56.7% had no reduction.
  • AI reassessment increased AF-LVEF by 8.2%, reclassifying 28.2% of patients.
  • Transient AF-LVEF reduction, as identified by AI, was associated with a significantly higher 1-year heart failure hospitalization risk (HR 2.28).

Conclusions:

  • Artificial intelligence may reduce misdiagnosis of reduced LVEF during AF.
  • AI can more accurately identify true transient AF-LVEF reduction.
  • Transient AF-LVEF reduction represents a potentially high-risk phenotype requiring further clinical attention.