This is a multi-center, observational study with the overall objective to examine the scale of under-diagnosis for transthyretin amyloid cardiomyopathy (ATTR-CM) across a broad range of diverse health systems in the US using a fully federated deployment of an artificial intelligence (AI) toolkit of algorithms that detect ATTR-CM on electrocardiography (ECG), point-of-care ultrasound (POCUS), and transthoracic echocardiography (TTE).
Study Type
OBSERVATIONAL
Enrollment
1,500,000
An artificial intelligence (AI) toolkit of algorithms that detect ATTR-CM on electrocardiography (ECG), point-of-care ultrasound (POCUS), and transthoracic echocardiography (TTE)
University of California - San Francisco (UCSF) Health
San Francisco, California, United States
Yale New Haven Health System
New Haven, Connecticut, United States
Henry Ford Health
Detroit, Michigan, United States
Mount Sinai
New York, New York, United States
Duke Health
Durham, North Carolina, United States
Providence Health
Tigard, Oregon, United States
Medical University of South Carolina (MUSC) Health
Charleston, South Carolina, United States
UT Southwestern Medical Center
Dallas, Texas, United States
Houstin Methodist
Houston, Texas, United States
University of Virginia School of Medicine
Charlottesville, Virginia, United States
...and 1 more locations
To describe the prevalence of probable AI-defined ATTR-CM in defined cohorts of individuals who have undergone standard cardiovascular investigations across a diverse network of US-based health care delivery systems
Time frame: At enrollment
Validate the diagnostic performance of AI-enabled ECG, POCUS, and TTE algorithms for ATTR-CM
Time frame: At enrollment
To examine the association between the AI-defined probability of ATTR-CM and the incidence of adverse cardiovascular events
Time frame: At enrollment
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