Clinical trial NCT06664866: AI Echocardiographic Screening of Cardiac Amyloidosis
Official title: Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases (EchoNet-Screening)
Study type: INTERVENTIONAL
Overall status: ENROLLING_BY_INVITATION
Phase: NA
Conditions: Cardiac Amyloidosis
Lead sponsor: Cedars-Sinai Medical Center
Sponsor class: OTHER
Collaborators: name: Palo Alto Veteran Affairs Hospital
name: Providence Heart & Vascular Institute
name: Northwestern Medicine
First posted: 2024-10-30
Enrollment: 500
Sex: ALL
Minimum age (years): 22
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

Brief summary: Recent advances in machine learning and image processing techniques have shown that machine learning models can identify features unrecognized by human experts and accurately assess common measurements made in clinical practice. Echocardiography is the most common form of cardiac imaging and is routinely and frequently used for diagnosis. However, there is often subjectivity and heterogeneity in interpretation. Artificial intelligence (AI)'s ability for precision measurement and detection is important in both disease screening as well as diagnosis of cardiovascular disease.

Cardiac amyloidosis (CA) is a rare, underdiagnosed disease with targeted therapies that reduce morbidity and increase life expectancy. However, CA is frequently overlooked and confused with heart failure with preserved ejection fraction. Some estimates suggest that CA can be as prevalence as 1% in a general population, with even higher prevalence in patients with left ventricular hypertrophy, heart failure, and other cardiac symptoms that might prompt echocardiography.

AI guided disease screening workflows have been proposed for rare diseases such as cardiac amyloidosis and other diseases with relatively low prevalence but significant human impact with targeted therapies when detected early. This is an area particularly suitable for AI as there are multiple mimics where diseases like hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, and other phenotypes might visually be similar but can be distinguished by AI algorithms. The investigators have developed an algorithm, termed EchoNet-LVH, to identify cardiac hypertrophy and identify patients who would benefit from additional screening for cardiac amyloidosis.


Eligibility criteria: Inclusion Criteria:

* Patients receiving an echocardiogram that is determined to be suspicious by EchoNet-LVH

Exclusion Criteria:

* Patients that decline consent
* Patients receiving an echocardiogram that is determined to be not suspicious by EchoNet-LVH

Primary outcomes:
measure: Positive Predictive Value
timeFrame: 1 year
Secondary outcomes:
measure: Time to Diagnosis from Echocardiogram Study to Clinical Diagnosis
timeFrame: 1 year
measure: Number of Patients that Receive Treatment for CA
timeFrame: 1 year
measure: Number of Cardiac Amyloidosis Diagnoses
timeFrame: 1 year
measure: Number of Participants with All Cause Death
timeFrame: 1 year
measure: Number of Participants with All Cause Hospitalization
timeFrame: 1 year
measure: Number of Participants with Heart Failure Hospitalization
timeFrame: 1 year
Other outcomes:
Locations:
city: Los Angeles
country: United States
facility: Cedars Sinai Medical Center
state: California
city: Palo Alto
country: United States
facility: Palo Alto Veteran Affairs Hospital
state: California
city: Chicago
country: United States
facility: Northwestern Medicine
state: Illinois
city: Portland
country: United States
facility: Providence Heart and Vascular Institute
state: Oregon
Study officials:
affiliation: Cedars-Sinai Medical Center
name: David Ouyang, MD
role: PRINCIPAL_INVESTIGATOR
References: