AI Echocardiographic Screening of Cardiac Amyloidosis

NCT ID: NCT06664866

Last Updated: 2025-06-27

Basic Information

• Recruitment Status: ENROLLING_BY_INVITATION
• Clinical Phase: NA
• Total Enrollment: 500 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-10-28
• Study Completion Date: 2026-11-01

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.

Conditions

Cardiac Amyloidosis

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

Suspicious by EchoNet-LVH Algorithm

Each potential participant identified by automated AI-enhanced echocardiogram review will be chart reviewed by each site's CA experts for appropriateness of enrollment and clinican suspicion for CA. Based on the judgement of CA experts, potential participants that meet eligibility criteria will be called to be consented, followed in the study, and referred to see the CA expert.

Group Type: EXPERIMENTAL

EchoNet-LVH Assessment

Intervention Type: DIAGNOSTIC_TEST

The AI algorithm is previously described (Duffy et al. JAMA Cardiology 2022) and will remain unchanged throughout the course of the study. A pre-determined threshold based on prior experiments and analysis has been decided prior to the study. From each site, approximately 100,000 echocardiogram studies will be reviewed by EchoNet-LVH for approximately 500 patients to be flagged.

Interventions

EchoNet-LVH Assessment

The AI algorithm is previously described (Duffy et al. JAMA Cardiology 2022) and will remain unchanged throughout the course of the study. A pre-determined threshold based on prior experiments and analysis has been decided prior to the study. From each site, approximately 100,000 echocardiogram studies will be reviewed by EchoNet-LVH for approximately 500 patients to be flagged.

Intervention Type: DIAGNOSTIC_TEST

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

• Minimum Eligible Age: 22 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Palo Alto Veteran Affairs Hospital

UNKNOWN

Sponsor Role: collaborator

Providence Heart & Vascular Institute

OTHER

Sponsor Role: collaborator

Northwestern Medicine

OTHER

Sponsor Role: collaborator

Cedars-Sinai Medical Center

OTHER

Sponsor Role: lead

Responsible Party

Lily Stern

Assistant Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Lily Stern, MD

Role: PRINCIPAL_INVESTIGATOR

Cedars-Sinai Medical Center

Locations

Cedars Sinai Medical Center

Los Angeles, California, United States

Palo Alto Veteran Affairs Hospital

Palo Alto, California, United States

Northwestern Medicine

Chicago, Illinois, United States

Providence Heart and Vascular Institute

Portland, Oregon, United States

Countries

United States

Other Identifiers

Study1720

Identifier Type: -

Identifier Source: org_study_id