Cardiac Amyloidosis Discovery Trial

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.

Study Overview

• Status: Completed
• Conditions: Cardiac Amyloidosis
• Intervention / Treatment: Device: Cardiac amyloidosis deep learning model

Detailed Description

Heart failure is a leading cause of death in the United States and throughout the world. One cause of heart failure is transthyretin cardiac amyloidosis (ATTR-CA), in which misfolded proteins deposit into the heart. This condition is often diagnosed very late when patients have severe symptoms. In this study, the investigators are trying to use a computer algorithm to find patients with cardiac amyloidosis that has not been diagnosed or suspected by their doctors. The investigators will look at patients seen at Columbia University Irving Medical Center and use our algorithm to identify 100 patients with a high probability of having cardiac amyloidosis and bring them in to be tested.

• ATTR-CA diagnosis: A diagnosis of ATTR-CA will be made according to consensus guidelines by an amyloidosis expert. These criteria include either (1) imaging criteria with requires that a patient's cardiac amyloid scintigraphy single-photon emission computed tomography (SPECT) scan shows myocardial uptake, increase left ventricular (LV) wall thickness by cardiac imaging that is unexplained by loading conditions, and follow-up monoclonal protein testing shows no evidence of clinical amyloid light-chain (AL) amyloidosis or (2) pathologic criteria with a biopsy showing systemic transthyretin deposition.
• Cardiac amyloidosis (AL-CA) diagnosis: A clinical diagnosis of AL-CA will be by an amyloidosis expert according to society guidelines. These includes a diagnosis made in one of the following settings: (1) cardiac biopsy showing AL deposition and (2) extra-cardiac biopsy showing AL deposition with typical cardiac features on imaging such as echocardiography or cardiac magnetic resonance imaging.

• Study Type: Interventional
• Enrollment (Actual): 50
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • New York
    • New York, New York, United States, 10032
      • Columbia University Irving Medical Center / NewYork-Presbyterian Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• 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.

Exclusion Criteria:

• 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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Intervention Arm; 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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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)

Collaborators and Investigators

• Sponsor: Pierre Elias
• Collaborators: Pfizer; American Heart Association; Eidos Therapeutics, a BridgeBio company
• Investigators: Principal Investigator: Timothy J. Poterucha, MD, Assistant Professor of Medicine

Publications and helpful links

General Publications

• 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.
• 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.
• 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.
• 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. 2025 Nov 10:e254591. doi: 10.1001/jamacardio.2025.4591. Online ahead of print.

Study record dates

Study Major Dates

• Study Start (Actual): May 28, 2024
• Primary Completion (Actual): August 1, 2025
• Study Completion (Actual): August 1, 2025

Study Registration Dates

• First Submitted: June 17, 2024
• First Submitted That Met QC Criteria: June 17, 2024
• First Posted (Actual): June 21, 2024

Study Record Updates

• Last Update Posted (Estimated): December 4, 2025
• Last Update Submitted That Met QC Criteria: November 21, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Deep Learning; Cardiac Amyloidosis
• Additional Relevant MeSH Terms: Nervous System Diseases; Neuromuscular Diseases; Metabolism, Inborn Errors; Genetic Diseases, Inborn; Metabolic Diseases; Peripheral Nervous System Diseases; Neurodegenerative Diseases; Heredodegenerative Disorders, Nervous System; Proteostasis Deficiencies; Amyloid Neuropathies; Amyloidosis, Familial; Amyloidosis; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Nutritional and Metabolic Diseases; Amyloid Neuropathies, Familial
• Other Study ID Numbers: AAAT2010

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: De-identified individual participant data will be made available to other researchers. Data available will include age range (50-59, 60-69, etc), sex, ECG and echocardiogram findings, pre-study clinical diagnosis, and post-study diagnosis.
• IPD Sharing 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
• IPD Sharing Access Criteria: Access will be made available request to academic investigators
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No