AI in Outpatient Practice for Diagnosing Aortic Stenosis and Diastolic Dysfunction

Recruiting

• Conditions: Aortic Stenosis; Diastolic Dysfunction

• Registration Number: NCT06580158
• Lead Sponsor: Mayo Clinic

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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2024-08-29
• Study First Submitted QC: 2024-08-29
• Study First Posted: 2024-08-30
• Study Start: 2024-11-08
• Status Verified: 2024-12
• Last Update Submitted: 2024-12-30
• Last Update Posted: 2024-12-31
• Primary Completion: 2026-03
• Study Completion: 2026-03

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 2000

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Number of studies with reasonable image quality in patients with positive AI-ECG	Baseline	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
Number of patients with positive AI-ECG	Baseline	Positive AI-ECG will be determined by the sensitivity, specificity, positive predictive value, and negative predictive value.

Secondary Outcome Measures

Name	Time	Method
Number of times the AI ECG and TTE (transthoracic echocardiogram) are statistically comparative	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

Trial Locations

Locations (1)

Mayo Clinic; 🇺🇸 Rochester, Minnesota, United States