Cardiac AmyloidosisNCT06469372
Cardiac Amyloidosis Discovery Trial
A study testing Cardiac amyloidosis deep learning model in people with Cardiac Amyloidosis, run by Pierre Elias at 1 site in United States.
Trial 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). Read more about this study
- Who is it for?
- Ages 50 years and older
- Conditions the study is for
- Cardiac Amyloidosis
- What is being tested?
- Cardiac amyloidosis deep learning model
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 50 participants
- Where
- 1 siteUnited States
- Study dates
- 28 May 2024to 1 Aug 2025
- Phase
- Interventional
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
United States1 site
Columbia University Irving Medical Center / NewYork-Presbyterian Hospital
New York, New York, 10032
About this study
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.
Read the 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.
- Conditions
- Cardiac Amyloidosis
- Being tested
- Cardiac amyloidosis deep learning model
- Keywords
- Cardiac Amyloidosis, Artificial Intelligence, Deep Learning
- Medical subject headings
- Amyloid Neuropathies, Familial
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Cardiac amyloidosis deep learning modelDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Device: Cardiac amyloidosis deep learning model |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: Up to 1 year after identification (1 day of participant assessment)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Jun 2024
- Study start28 May 2024
- Primary completion1 Aug 2025
- Study completion1 Aug 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Cardiac AmyloidosisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 50 participants (actual)
Sponsor and investigators
- Lead sponsor
- Pierre Elias
- Sponsor type
- University, hospital or other
- Collaborators
- Pfizer, American Heart Association, Eidos Therapeutics, a BridgeBio company
- Responsible party
- Sponsor-investigator, Pierre Elias, Columbia University
- Investigators
- Timothy J. Poterucha, MD, Principal investigator, Assistant Professor of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06469372
- Sponsor's protocol ID
- AAAT2010
- Status last verified
- Nov 2025
- First submitted
- 17 Jun 2024
- Last updated
- 4 Dec 2025
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Yes: 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.
Publications
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.
PubMed 31468376Dorbala 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.
PubMed 31468377Poterucha 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.
PubMed 33221204Jain 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.
PubMed 41213043
Study at a glance
From the registry record, last updated by the sponsor on 4 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Cardiac Amyloidosis Discovery Trial
- ClinicalTrials.gov ID
- NCT06469372
- Acronym / sponsor ID
- AAAT2010
- Conditions
- Cardiac Amyloidosis
- Keywords
- Cardiac Amyloidosis, Artificial Intelligence, Deep Learning
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 50 participants (actual)
- Lead sponsor
- Pierre EliasUniversity, hospital or other
- Collaborators
- Pfizer, American Heart Association, Eidos Therapeutics, a BridgeBio company
- Study start
- 28 May 2024 (actual)
- Primary completion
- 1 Aug 2025 (actual)
- Study completion
- 1 Aug 2025 (actual)
- First posted
- 21 Jun 2024 (actual)
- Results
- Posted 4 Dec 2025
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Intervention Arm | Experimental | Device: Cardiac amyloidosis deep learning modelPatients who are identified by the deep learning model as being at high risk for undiagnosed cardiac amyloidosis who are enrolled in the study. |
Who may qualify
- Ages
- 50 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (7 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Cardiac amyloidosis deep learning modelThis 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. | Device | Intervention Arm |
Primary outcome
Rate of Cardiac Amyloidosis Diagnosis
Time frame: Up to 1 year after identification (1 day of participant assessment)
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).
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Dec 2025.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Background: PubMed 31468376Dorbala 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.
- Background: PubMed 31468377Dorbala 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.
- Background: PubMed 33221204Poterucha 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.
- Derived from this study: PubMed 41213043Jain 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.
- Individual participant dataSharing planned: Yes. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Timothy J. Poterucha, MD principal investigator
Assistant Professor of Medicine
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Cardiac amyloidosis deep learning model in people with Cardiac Amyloidosis, run by Pierre Elias at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 4 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 4 Dec 2025). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Rate of Cardiac Amyloidosis Diagnosis (time frame: Up to 1 year after identification (1 day of participant assessment)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06469372, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Dec 2025.