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NCT Number: NCT06580158

AI in Outpatient Practice for Diagnosing Aortic Stenosis and Diastolic Dysfunction

Two recently developed artificial intelligence-enabled electrocardiogram (AI-ECG) models have been developed to detect aortic stenosis (AS) and diastolic dysfunction (DD). AI-ECG for AS has a sensitivity of 78% and specificity of 74%, and AI-ECG for DD has a sensitivity of 83% and specificity of 80%. However, these models have never been prospectively applied to diagnose AS or DD, which may be useful for patients and providers from a diagnostic and prognostic perspective and especially in settings where access to higher- level medical care is limited. In this study, we aim to determine the clinical utility of these AI-ECG models by prospectively applying them to an outpatient cohort and then completing a focused point-of-care ultrasound to evaluate those who are AI-ECG positive for AS and DD.

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Key information

Conditions

Aortic Stenosis Aortic Valve Disease Aortic Valve Stenosis Cardiovascular Diseases Diastolic Dysfunction Heart Diseases Heart Valve Diseases Ventricular Outflow Obstruction

Age range

60 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Mayo Clinic

Rochester, Minnesota, 55905, United States

Location status: Recruiting

Location contact

Jae Oh, M.D.

CONTACT

[email protected]

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • ≥ 60 years of age must have a clinical scheduled ECG performed.

Exclusion criteria

  • < 59 years of age
  • Is not scheduled for a clinical ECG
  • Unable to provide consent.

Treatment and study plan

AI-ECG Dashboard

Device

Patients standard of care ECG's will be processed through the AI-ECG Dashboard

Point of care ultrasound (POCUS)

Diagnostic Test

Patients will undergo a ultrasound to confirm diagnosis of atrial stenosis or diastolic dysfunction.

Primary outcomes

  1. Number of patients with positive AI-ECG

    Time frame: Baseline

    Positive AI-ECG will be determined by the sensitivity, specificity, positive predictive value, and negative predictive value.

  2. Number of studies with reasonable image quality in patients with positive AI-ECG

    Time frame: Baseline

    Image quality will be determined by sonographers at the time of imaging and will be scored on a scale from 1-4:

    • Excellent , sufficient for publication
    • Good, sufficient for data analysis
    • Fair, just enough for data analysis without complete views
    • Poor, not usable for data analysis

Secondary outcomes

  1. Number of times the AI ECG and TTE (transthoracic echocardiogram) are statistically comparative

    Time frame: Baseline

    Will be compared using parametric (2-sample t-test) and non-parametric tests (Wilcoxon rank sum test) for continuous variables, and the χ2 test or Fisher exact test for nominal variables. A p-value of < 0.05 will be categorized as significant for the statistical analysis

Study contacts

Contact information is provided by the study sponsor or research team.

Brian Rudquist

CONTACT

[email protected]

(507) 538-5146

Jae Oh, M.D.

CONTACT

[email protected]

Sponsors and collaborators

Lead sponsor

Mayo Clinic

Other

Registry information

Official study title

The Clinical Utility of Artificial Intelligence-enabled Electrocardiograms in the Outpatient Practice - Diagnosing Aortic Stenosis and Diastolic Dysfunction

Important dates

Study start
2024
Primary completion
2027
Study completion
2027
First posted
Aug 30, 2024
Registry last updated
Mar 4, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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