Deep Learning-enhanced Personalized Monitoring of Aortic Stenosis - The DETECT-AS Prognostic Study
- Conditions
- Aortic Stenosis
- Registration Number
- NCT06749132
- Lead Sponsor
- Yale University
- Brief Summary
This study will evaluate the validity of a digital biomarker score for precision risk stratification among older adults with aortic sclerosis or mild aortic stenosis (AS) at three US health systems.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 210
- Age 65 years or older
- Prior transthoracic echocardiogram within the past 2-3 years showing aortic sclerosis without stenosis or mild AS
- Opted out of research studies
- Non-English speaking
- Any echocardiogram within 24 months of medical record review.
- Prior history of aortic valve replacement or repair, including transcatheter and surgical AVR with either a bioprosthetic or mechanical valve
- Presence of implantable cardiac devices, including permanent cardiac pacer, implantable cardioverter-defibrillator, or left ventricular assist device
- Prior heart transplant
- History of dementia
- Unable to attend study visit at echocardiogram lab within four years of most recent echocardiogram.
- Documented life expectancy of <1 year or current participation in hospice services
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Annualized change in AV Vmax in m/sec/years From baseline TTE to TTE performed at enrollment The primary outcome measure is the annualized change in AV Vmax in m/sec/years, defined as the absolute difference between the value on the enrollment and baseline echocardiogram measurement, divided by the time difference between the studies.
- Secondary Outcome Measures
Name Time Method
Related Research Topics
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Trial Locations
- Locations (3)
Santa Clara Medical Center - Kaiser Permanente
🇺🇸Santa Clara, California, United States
Yale New Haven Health System
🇺🇸New Haven, Connecticut, United States
Icahn School of Medicine at Mount Sinai
🇺🇸New York, New York, United States