AI in Outpatient Practice for Diagnosing Aortic Stenosis and Diastolic Dysfunction

NCT06580158 | Recruiting FDA Device

• 1 other identifier: 24-000100
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Aortic Stenosis; Diastolic Dysfunction

Outcome Measures

Primary Outcomes (2)

• Number of patients with positive AI-ECG

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

  Baseline

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

  Image quality will be determined by sonographers at the time of imaging and will be scored on a scale from 1-4: 1. Excellent , sufficient for publication 2. Good, sufficient for data analysis 3. Fair, just enough for data analysis without complete views 4. Poor, not usable for data analysis

  Baseline

Secondary Outcomes (1)

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

  Baseline

Study Arms (1)

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

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

Interventions

AI-ECG Dashboard DEVICE

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

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

Point of care ultrasound (POCUS) DIAGNOSTIC_TEST

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

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

Eligibility Criteria

• Age: 60 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Mayo Clinic: lead

Study Sites (1)

Mayo Clinic

Rochester, Minnesota, 55905, United States

RECRUITING

MeSH Terms

Conditions

Aortic Valve Stenosis

Condition Hierarchy (Ancestors)

Aortic Valve Disease; Heart Valve Diseases; Heart Diseases; Cardiovascular Diseases; Ventricular Outflow Obstruction

Study Officials

• Jae Oh, M.D.

  Mayo Clinic

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Brian Rudquist

CONTACT

(507) 538-5146; Rudquist.Brian@mayo.edu

Jae Oh, M.D.

CONTACT

oh.jae@mayo.edu

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: August 29, 2024
• First Posted: August 30, 2024
• Study Start: November 8, 2024
• Primary Completion (Estimated): March 1, 2027
• Study Completion (Estimated): March 1, 2027
• Last Updated: March 4, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)