Aortic Stenosis, Diastolic Dysfunction
Conditions
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.
Interventions
DEVICEAI-ECG Dashboard
Patients standard of care ECG's will be processed through the AI-ECG Dashboard
DIAGNOSTIC_TESTPoint of care ultrasound (POCUS)
Patients will undergo a ultrasound to confirm diagnosis of atrial stenosis or diastolic dysfunction.
Sponsors
Mayo Clinic
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
60 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
| 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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 |
Countries
United States
Contacts
CONTACTBrian Rudquist
CONTACTJae Oh, M.D.
PRINCIPAL_INVESTIGATORJae Oh, M.D.
Mayo Clinic
Outcome results
None listed