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Summary of the Study:
Background: ValveNet is an AI-ECG model designed to detect moderate or greater left-sided valvular heart disease (VHD) — specifically aortic stenosis, aortic regurgitation, and mitral regurgitation — which are a subset of structural heart disease (SHD).
Trial Design: The DISCOVERY trial recruited 100 adult patients based on their ValveNet risk score to test ValveNet’s ability to identify clinically significant cardiac disease. Eligibility criteria included having a recent 12-lead digital ECG without echocardiogram in the past 3 years and no known left-sided VHD or significant comorbidities limiting survival.
Stratification: Patients were recruited from the moderate- and high-risk groups (defined by ValveNet risk tertiles: 0–0.3, 0.3–0.6, >0.6). The lowest risk group was excluded.
Endpoints:
Primary: Detection of moderate or severe aortic stenosis, aortic regurgitation, or mitral regurgitation by echocardiogram.
Secondary: Detection of all clinically significant SHD as defined by EchoNext.
Results:
Majority of patients were elderly (median age 80) and 43% male.
In the high-risk ValveNet group (53 patients), 17% had moderate or greater left-sided VHD and 53% had SHD.
In the moderate-risk ValveNet group (47 patients), 0% had moderate or greater left-sided VHD and 19% had SHD.
Significant differences existed between high- vs. moderate-risk groups for detection of left-sided VHD (P=0.005) and SHD (P=0.003).
EchoNext AI model retrospectively analyzed the ECGs and stratified patients into risk groups (high, moderate, low). There were strong correlations between risk groups and disease prevalence, all statistically significant.
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