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| Name | Class |
|---|---|
| Pfizer | INDUSTRY |
| American Heart Association | OTHER |
| Eidos Therapeutics, a BridgeBio company | INDUSTRY |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| 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) |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Timothy J. Poterucha, MD | Assistant Professor of Medicine | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Columbia University Irving Medical Center / NewYork-Presbyterian Hospital | New York | New York | 10032 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 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 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. | |
| 31468377 |
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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.
Data will be made available within 6 months of study publication and will be kept available for up to 3 years following publication
Access will be made available request to academic investigators
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| ID | Title | Description |
|---|---|---|
| FG000 | 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. 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. |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
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| ID | Title | Description |
|---|---|---|
| BG000 | 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. 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. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | 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). | Posted | Count of Participants | Participants | Up to 1 year after identification (1 day of participant assessment) |
|
Throughout study duration, approx 1 year (1 day per participant assessment)
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | 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. 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. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Timothy J. Poterucha, MD | Columbia University Irving Medical Center | (212) 932-4537 | tp2558@cumc.columbia.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Aug 22, 2024 | Nov 19, 2025 | Prot_SAP_000.pdf |
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| ID | Term |
|---|---|
| D028227 | Amyloid Neuropathies, Familial |
| ID | Term |
|---|---|
| D020271 | Heredodegenerative Disorders, Nervous System |
| D019636 | Neurodegenerative Diseases |
| D009422 | Nervous System Diseases |
| D017772 | Amyloid Neuropathies |
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|
| 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. |
| 33221204 | 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. |
| 41213043 | Derived | 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. |
| years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Race/Ethnicity, Customized | Count of Participants | Participants |
|
| Region of Enrollment | Number | participants |
|
| Orthopedic manifestations | Count of Participants | Participants |
|
|
|
| 0 |
| 50 |
| 0 |
| 50 |
| 0 |
| 50 |
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| D010523 | Peripheral Nervous System Diseases |
| D009468 | Neuromuscular Diseases |
| D030342 | Genetic Diseases, Inborn |
| D009358 | Congenital, Hereditary, and Neonatal Diseases and Abnormalities |
| D028226 | Amyloidosis, Familial |
| D008661 | Metabolism, Inborn Errors |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D000686 | Amyloidosis |
| D057165 | Proteostasis Deficiencies |