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.
Study Type
OBSERVATIONAL
Enrollment
2,000
Patients standard of care ECG's will be processed through the AI-ECG Dashboard
Patients will undergo a ultrasound to confirm diagnosis of atrial stenosis or diastolic dysfunction.
Mayo Clinic
Rochester, Minnesota, United States
RECRUITINGNumber of patients with positive AI-ECG
Positive AI-ECG will be determined by the sensitivity, specificity, positive predictive value, and negative predictive value.
Time frame: 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
Time frame: Baseline
Number of times the AI ECG and TTE (transthoracic echocardiogram) are statistically comparative
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
Time frame: Baseline
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