NCT06580158

AI in Outpatient Practice for Diagnosing Aortic Stenosis and Diastolic Dysfunction

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

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 2,000 (estimated)
• Sponsor: Mayo Clinic · Academic / Other
• Sex: All
• Age: 60 Years
• Healthy volunteers: Not accepted

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

Interventions

Type	Name	Description
DEVICE	AI-ECG Dashboard	Patients standard of care ECG's will be processed through the AI-ECG Dashboard
DIAGNOSTIC_TEST	Point of care ultrasound (POCUS)	Patients will undergo a ultrasound to confirm diagnosis of atrial stenosis or diastolic dysfunction.

Timeline

• Start date: 2024-11-08
• Primary completion: 2027-03-01
• Completion: 2027-03-01
• First posted: 2024-08-30
• Last updated: 2026-03-04

Locations

1 site across 1 country: United States

Regulatory

• FDA-regulated device study