Detection of Left Atrial Myopathy Using Artificial Intelligence-Enabled Electrocardiography

Frederik H Verbrugge, Yogesh N V Reddy, Zachi I Attia, Paul A Friedman, Peter A Noseworthy, Francisco Lopez-Jimenez, Suraj Kapa, Barry A Borlaug

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

BACKGROUND: Left atrial (LA) myopathy is common in patients with heart failure and preserved ejection fraction and leads to the development of atrial fibrillation (AF). We investigated whether the likelihood of LA remodeling, LA dysfunction, altered hemodynamics, and risk for incident AF could be identified from a single 12-lead ECG using a novel artificial intelligence (AI)-enabled ECG analysis.

METHODS: Patients with heart failure and preserved ejection fraction (n=613) underwent AI-enabled ECG analysis, echocardiography, and cardiac catheterization. Individuals were grouped by AI-enabled ECG probability of contemporaneous AF, taken as an indicator of underlying LA myopathy.

RESULTS: Structural heart disease was more severe in patients with higher AI-probability of AF, with more left ventricular hypertrophy, larger LA volumes, and lower LA reservoir and booster strain. Cardiac filling pressures and pulmonary artery pressures were higher in patients with higher AI-probability, while cardiac output reserve was more impaired during exercise. Among patients with sinus rhythm and no prior AF, each 10% increase in AI-probability was associated with a 31% greater risk of developing new-onset AF (hazard ratio, 1.31 [95% CI, 1.20-1.42]; P<0.001). In the population as a whole, each 10% increase in AI-probability was associated with a 12% greater risk of death (hazard ratio, 1.12 [95% CI, 1.08-1.17]; P<0.001) during long-term follow-up, which was no longer significant after adjustments for baseline characteristics.

CONCLUSIONS: A novel AI-enabled score derived from a single 12-lead ECG identifies the presence of underlying LA myopathy in patients with heart failure and preserved ejection fraction as evidenced by structural, functional, and hemodynamic abnormalities, as well as long-term risk for incident AF. Further research is required to determine the role of the AI-enabled ECG in the evaluation and care of patients with heart failure and preserved ejection fraction.

Original languageEnglish
Article numbere008176
JournalCirculation: Heart Failure
Volume15
Issue number1
Early online date16 Dec 2021
DOIs
Publication statusPublished - Jan 2022

Bibliographical note

Funding Information:
Dr Verbrugge is supported by a Fellowship of the Belgian American Educational Foundation and by the Special Research Fund (BOF) of Hasselt University (BOF19PD04). Dr Borlaug is supported by R01 HL128526 from the National Institutes of Health.

Publisher Copyright:
© 2021 American Heart Association, Inc.

Keywords

  • atrial fibrillation
  • echocardiography
  • exercise
  • heart failure
  • probability

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