The DETECT-AS Diagnostic Study will assess the performance of artificial intelligence (AI) risk predictions to detect aortic stenosis using results from portable electrocardiogram (ECG) and cardiac ultrasound devices.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
TRIPLE
Enrollment
410
Portable 1-lead electrocardiogram (ECG) performed with the FDA-approved AliveCor KardiaMobile device.
Point-of-care ultrasound performed with the FDA-approved VScan Air device.
Artificial intelligence (AI) risk algorithm for aortic stenosis using a 1-lead electrocardiogram
Yale New Haven Health System
New Haven, Connecticut, United States
Icahn School of Medicine at Mount Sinai
New York, New York, United States
The Methodist Hospital Research Institute
Houston, Texas, United States
Number of participants diagnosed with advanced aortic stenosis (AS) by transthoracic echocardiogram (TTE)
The number of participants diagnosed with advanced AS by TTE at 12 months. Diagnosis of advanced AS is defined as diagnosis of moderate or severe AS as documented in the participant's electronic health record (EHR) at 12 months and adjudication of outcome via review of echocardiographic reports and videos performed by blinded members of the echocardiographic lab at the coordinating center.
Time frame: Until 12 months from the baseline visit
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Artificial intelligence (AI) risk algorithm for aortic stenosis using cardiac ultrasound plax videos.