AI in Outpatient Practice for Diagnosing Aortic Stenosis and Diastolic Dysfunction

Mayo Clinic

Status: Enrolling

Conditions: Diastolic Dysfunction; Aortic Stenosis

Treatments: Device: AI-ECG Dashboard; Diagnostic Test: Point of care ultrasound (POCUS)

Study type: Observational

Funder types: Other

Identifiers

NCT06580158

24-000100

Details and patient eligibility

About

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.

Enrollment: 2,000 estimated patients

Sex: All

Ages: 60+ years old

Volunteers: No Healthy Volunteers

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.

Trial design

2,000 participants in 1 patient group

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

Treatment:

Diagnostic Test: Point of care ultrasound (POCUS)

Device: AI-ECG Dashboard