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Artificial intelligence-enabled ECG for left ventricular diastolic function and filling pressure.

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  • 1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.

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An artificial intelligence (AI) model using electrocardiograms (ECG) can identify diastolic dysfunction and increased filling pressure. This AI-ECG shows prognostic value similar to echocardiography for cardiac disease detection.

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Left ventricular diastolic function assessment is crucial for diagnosing and predicting cardiac diseases, including heart failure with preserved ejection fraction.
  • Echocardiography is the standard for assessing diastolic function, but it can be limited in accessibility and interpretation.
  • There is a need for non-invasive, accessible tools to aid in the early detection of diastolic dysfunction.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-enabled electrocardiogram (ECG) model for identifying echocardiographically determined diastolic dysfunction and increased filling pressure.
  • To evaluate the diagnostic and prognostic performance of the AI-ECG model compared to echocardiography.
  • To assess the utility of AI-ECG in patients with indeterminate diastolic function by echocardiography.

Main Methods:

  • Trained, validated, and tested an AI-enabled ECG model on a large cohort of patients (totaling over 220,000) with concurrent ECG and echocardiographic data.
  • Evaluated model performance using the area under the curve (AUC) of the receiver operating characteristic curve for detecting increased filling pressure and different grades of diastolic dysfunction.
  • Assessed prognostic performance by comparing mortality rates in patients stratified by AI-ECG predicted filling pressure against echocardiography findings over a median follow-up of 5.9 years.

Main Results:

  • The AI-ECG model achieved high AUCs for detecting increased filling pressure (0.911) and diastolic dysfunction grades (0.847 to 0.943).
  • AI-ECG prediction of increased filling pressure demonstrated significant prognostic value for mortality, comparable to echocardiography.
  • In patients with indeterminate echocardiographic findings, AI-ECG also showed a significant association with higher mortality (HR 1.34).

Conclusions:

  • An AI-enabled ECG model effectively identifies increased filling pressure and diastolic dysfunction grades with diagnostic accuracy comparable to echocardiography.
  • The AI-ECG model possesses significant prognostic value, predicting mortality risk similarly to traditional echocardiographic assessments.
  • AI-ECG presents a promising, simple tool to enhance the early detection and risk stratification of cardiac diseases related to diastolic dysfunction.