An Observational Study Using Artificial Intelligence (AI) Algorithms on Electrocardiography (ECG), Point-of-care Ultrasound (POCUS), and Transthoracic Echocardiophy (TTE) to Estimate the Under-diagnosis of Transthyretin Amyloid Cardiomyopathy (ATTR-CM) Across a Diverse Range of US Health Systems.

The Transthyretin Amyloid Cardiomyopathy Early Detection With Artificial Intelligence (TRACE-AI) Network Study

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07062848

Acronym

TRACE Network

Enrollment

1500000

Registered

2025-07-14

Start date

2025-01-24

Completion date

2027-01-01

Last updated

2026-07-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Transthyretin (TTR) Amyloid Cardiomyopathy

Keywords

artificial intelligence, AI-ECG, AI-Echo, cardiac amyloidosis, transthyretin amyloid cardiomyopathy

Brief summary

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).

Interventions

DIAGNOSTIC_TESTAI Toolkit for ATTR-CM Diagnosis

An artificial intelligence (AI) toolkit of algorithms that detect ATTR-CM on electrocardiography (ECG), point-of-care ultrasound (POCUS), and transthoracic echocardiography (TTE)

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

50 Years to 95 Years

Healthy volunteers

Inclusion criteria

Broad inclusion and

Exclusion criteria

across all 3 objectives: Inclusion Criteria: * Age 50-95 * At least one retrievable ECG and/or 2D echo file (DICOM or equivalent video file) from EHR.

Design outcomes

Primary

Measure	Time frame
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	At enrollment

Secondary

Measure	Time frame
Validate the diagnostic performance of AI-enabled ECG, POCUS, and TTE algorithms for ATTR-CM	At enrollment
To examine the association between the AI-defined probability of ATTR-CM and the incidence of adverse cardiovascular events	At enrollment

Countries

United States

Contacts

PRINCIPAL_INVESTIGATORRohan Khera, MD, MS

Yale University

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Jul 30, 2026