Cardiac Amyloidosis Discovery Trial

NCT06469372 | Completed Results FDA Device

• 1 other identifier: AAAT2010
• Study Type: interventional
• Target: 50
• Locations: 1 country
• Sites: 1

Brief Summary

This is a single center, diagnostic clinical trial in which the investigators aim to prospectively validate a deep learning model that identifies patients with features suggestive of cardiac amyloidosis, including transthyretin cardiac amyloidosis (ATTR-CA). Cardiac Amyloidosis is an age-related infiltrative cardiomyopathy that causes heart failure and death that is frequently unrecognized and underdiagnosed. The investigators have developed a deep learning model that identifies patients with features of ATTR-CA and other types of cardiac amyloidosis using echocardiographic, ECG, and clinical factors. By applying this model to the population served by NewYork-Presbyterian Hospital, the investigators will identify a list of patients at highest predicted risk for having undiagnosed cardiac amyloidosis. The investigators will then invite these patients for further testing to diagnose cardiac amyloidosis. The rate of cardiac amyloidosis diagnosis of patients in this study will be compared to rate of cardiac amyloidosis diagnosis in historic controls from the following two groups: (1) patients referred for clinical cardiac amyloidosis testing at NewYork-Prebysterian Hospital and (2) patients enrolled in the Screening for Cardiac Amyloidosis With Nuclear Imaging in Minority Populations (SCAN-MP) study.

Conditions

Cardiac Amyloidosis

Keywords

Cardiac Amyloidosis; Artificial Intelligence; Deep Learning

Outcome Measures

Primary Outcomes (1)

• Rate of Cardiac Amyloidosis Diagnosis

  The primary outcome is the rate of cardiac amyloidosis diagnosis (inclusive of transthyretin and light chain cardiac amyloidosis) which is performed in response to patient identification using the deep learning model, reported as the number of participants who had a positive diagnosis for ATTR-CM (transthyretin amyloid cardiomyopathy).

  Up to 1 year after identification (1 day of participant assessment)

Study Arms (1)

Intervention Arm

EXPERIMENTAL

Patients who are identified by the deep learning model as being at high risk for undiagnosed cardiac amyloidosis who are enrolled in the study.

Device: Cardiac amyloidosis deep learning model

Interventions

Cardiac amyloidosis deep learning model DEVICE

This is a deep learning algorithm which intakes a patient's age, sex, clinical factors known to be related to amyloidosis and their ECG and echocardiogram results and determines their estimated risk for having cardiac amyloidosis.

Intervention Arm

Eligibility Criteria

• Age: 50 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• High predicted probability of having cardiac amyloidosis as determined by deep learning model.
• Age ≥ 50 years.
• Electronically stored ECG and echocardiogram within 5 years of study start date.
• Ability for the patient or health care proxy to understand and sign the informed consent after the study has been explained.

You may not qualify if:

• Primary amyloidosis (AL) or secondary amyloidosis (AA).
• Prior liver or heart transplantation.
• Active malignancy or non-amyloid disease with expected survival of less than 1 year.
• Previous testing for cardiac amyloidosis such as amyloid nuclear scintigraphy, cardiac, or fat pad biopsy.
• Impairment from stroke, injury or other medical disorder that precludes participation in the study.
• Disabling dementia or other mental or behavioral disease
• Nursing home resident.

Sponsors & Collaborators

• Pierre Elias: lead
• Pfizer: collaborator
• Eidos Therapeutics, a BridgeBio company: collaborator
• American Heart Association: collaborator

Study Sites (1)

Columbia University Irving Medical Center / NewYork-Presbyterian Hospital

New York, New York, 10032, United States

Location

Related Publications (4)

• Dorbala S, Ando Y, Bokhari S, Dispenzieri A, Falk RH, Ferrari VA, Fontana M, Gheysens O, Gillmore JD, Glaudemans AWJM, Hanna MA, Hazenberg BPC, Kristen AV, Kwong RY, Maurer MS, Merlini G, Miller EJ, Moon JC, Murthy VL, Quarta CC, Rapezzi C, Ruberg FL, Shah SJ, Slart RHJA, Verberne HJ, Bourque JM. ASNC/AHA/ASE/EANM/HFSA/ISA/SCMR/SNMMI expert consensus recommendations for multimodality imaging in cardiac amyloidosis: Part 1 of 2-evidence base and standardized methods of imaging. J Nucl Cardiol. 2019 Dec;26(6):2065-2123. doi: 10.1007/s12350-019-01760-6. No abstract available.

  PMID: 31468376 BACKGROUND

• Dorbala S, Ando Y, Bokhari S, Dispenzieri A, Falk RH, Ferrari VA, Fontana M, Gheysens O, Gillmore JD, Glaudemans AWJM, Hanna MA, Hazenberg BPC, Kristen AV, Kwong RY, Maurer MS, Merlini G, Miller EJ, Moon JC, Murthy VL, Quarta CC, Rapezzi C, Ruberg FL, Shah SJ, Slart RHJA, Verberne HJ, Bourque JM. ASNC/AHA/ASE/EANM/HFSA/ISA/SCMR/SNMMI expert consensus recommendations for multimodality imaging in cardiac amyloidosis: Part 2 of 2-Diagnostic criteria and appropriate utilization. J Nucl Cardiol. 2020 Apr;27(2):659-673. doi: 10.1007/s12350-019-01761-5.

  PMID: 31468377 BACKGROUND

• Poterucha TJ, Elias P, Bokhari S, Einstein AJ, DeLuca A, Kinkhabwala M, Johnson LL, Flaherty KR, Saith SE, Griffin JM, Perotte A, Maurer MS. Diagnosing Transthyretin Cardiac Amyloidosis by Technetium Tc 99m Pyrophosphate: A Test in Evolution. JACC Cardiovasc Imaging. 2021 Jun;14(6):1221-1231. doi: 10.1016/j.jcmg.2020.08.027. Epub 2020 Nov 18.

  PMID: 33221204 BACKGROUND

• Jain SS, Sun T, Pierson E, Roedan Oliver F, Malta P, Castillo M, Wan N, Alishetti S, Hartman H, Finer J, Brown KL, Ramlall V, Tatonetti N, Elhadad N, Rodriguez F, Witteles R, Goyal P, Homma S, Einstein AJ, Maurer MS, Elias P, Poterucha TJ. Detecting Transthyretin Cardiac Amyloidosis With Artificial Intelligence: A Nonrandomized Clinical Trial. JAMA Cardiol. 2026 Feb 1;11(2):117-124. doi: 10.1001/jamacardio.2025.4591.

  PMID: 41213043 DERIVED

MeSH Terms

Conditions

Amyloid Neuropathies, Familial

Condition Hierarchy (Ancestors)

Heredodegenerative Disorders, Nervous System; Neurodegenerative Diseases; Nervous System Diseases; Amyloid Neuropathies; Peripheral Nervous System Diseases; Neuromuscular Diseases; Genetic Diseases, Inborn; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Amyloidosis, Familial; Metabolism, Inborn Errors; Metabolic Diseases; Nutritional and Metabolic Diseases; Amyloidosis; Proteostasis Deficiencies

Results Point of Contact

• Title: Timothy J. Poterucha, MD
• Organization: Columbia University Irving Medical Center

tp2558@cumc.columbia.edu

(212) 932-4537

Study Officials

• Timothy J. Poterucha, MD

  Assistant Professor of Medicine

  PRINCIPAL INVESTIGATOR

Publication Agreements

• PI is Sponsor Employee: Yes

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Assistant Professor of Medicine

Study Record Dates

• First Submitted: June 17, 2024
• First Posted: June 21, 2024
• Study Start: May 28, 2024
• Primary Completion: August 1, 2025
• Study Completion: August 1, 2025
• Last Updated: December 4, 2025
• Results First Posted: December 4, 2025

Record last verified: 2025-11

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: Data will be made available within 6 months of study publication and will be kept available for up to 3 years following publication
• Access Criteria: Access will be made available request to academic investigators

Locations

US (1)