AI in Outpatient Practice for Diagnosing Aortic Stenosis and Diastolic Dysfunction

Part of paid clinical trials in Rochester, Minnesota.

Sponsor
Mayo Clinic
Study ID
NCT06580158
Status
Recruiting
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Conditions

  • Aortic Stenosis
  • Diastolic Dysfunction

Eligibility Criteria

Sex
ALL
Age
60 Years - N/A
Healthy Volunteers
Not accepted

Interventions

  • 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.

Study Details

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.

Key Dates

Start date
Nov 8, 2024
Status verified
Jan 2026
Primary completion
Mar 31, 2027
Completion
Mar 31, 2027

Study Design

Enrollment
2,000 participants (estimated)

Arms

  • Arm: Patients who are completing an outpatient electrocardiogram (ECG) at the Mayo Clinic.

Primary Outcome Measure

Number of patients with positive AI-ECG [ Time Frame: Baseline ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
Mayo ClinicRochesterMinnesota55905
Jae Oh, M.D.
[email protected]

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Mayo Clinic· Rochester, MN

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