Using AI to identify patients with cardiac amyloidosis
Cardiac Amyloidosis Discovery Trial
NA · Columbia University · NCT06469372
This study is testing a new AI tool to see if it can help doctors find patients who might have a heart condition called cardiac amyloidosis, so they can get the right tests and treatment.
Quick facts
| Phase | NA |
|---|---|
| Study type | Interventional |
| Enrollment | 100 (estimated) |
| Ages | 50 Years and up |
| Sex | All |
| Sponsor | Columbia University (other) |
| Locations | 1 site (New York, New York) |
| Trial ID | NCT06469372 on ClinicalTrials.gov |
What this trial studies
This clinical trial aims to validate a deep learning model designed to identify patients at high risk for cardiac amyloidosis, particularly transthyretin cardiac amyloidosis (ATTR-CA). The study will analyze echocardiographic, ECG, and clinical data from patients at Columbia University Irving Medical Center to pinpoint those who may have undiagnosed cardiac amyloidosis. Once identified, these patients will be invited for further diagnostic testing to confirm the presence of the condition. The effectiveness of the model will be assessed by comparing the diagnosis rates of cardiac amyloidosis among participants.
Who should consider this trial
Good fit: Ideal candidates are individuals aged 50 and older with a high predicted probability of having cardiac amyloidosis based on the deep learning model.
Not a fit: Patients with primary or secondary amyloidosis, those who have undergone prior testing for cardiac amyloidosis, or individuals with severe comorbidities may not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could lead to earlier diagnosis and treatment of cardiac amyloidosis, improving patient outcomes.
How similar studies have performed: Other studies utilizing AI for diagnostic purposes have shown promise, suggesting that this approach may be effective, though this specific application is novel.
Eligibility criteria
Show full inclusion / exclusion criteria
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.
Where this trial is running
New York, New York
- Columbia University Irving Medical Center / NewYork-Presbyterian Hospital — New York, New York, United States (RECRUITING)
Study contacts
- Principal investigator: Timothy J. Poterucha, MD — Assistant Professor of Medicine
- Study coordinator: Timothy J. Poterucha, MD
- Email: tp2558@cumc.columbia.edu
- Phone: (212) 932-4537
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Cardiac Amyloidosis, Artificial Intelligence, Deep Learning