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Deep Learning-enhanced Personalized Monitoring of Aortic Stenosis - The DETECT-AS Prognostic Study

Not yet recruiting
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
Inclusion Criteria
  1. Age 65 years or older
  2. Prior transthoracic echocardiogram within the past 2-3 years showing aortic sclerosis without stenosis or mild AS
Exclusion Criteria
  1. Opted out of research studies
  2. Non-English speaking
  3. Any echocardiogram within 24 months of medical record review.
  4. Prior history of aortic valve replacement or repair, including transcatheter and surgical AVR with either a bioprosthetic or mechanical valve
  5. Presence of implantable cardiac devices, including permanent cardiac pacer, implantable cardioverter-defibrillator, or left ventricular assist device
  6. Prior heart transplant
  7. History of dementia
  8. Unable to attend study visit at echocardiogram lab within four years of most recent echocardiogram.
  9. Documented life expectancy of <1 year or current participation in hospice services

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Primary Outcome Measures
NameTimeMethod
Annualized change in AV Vmax in m/sec/yearsFrom 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
NameTimeMethod

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

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