Cardiac Amyloidosis Discovery Trial

Study Results

Results available

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Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

50 participants

Study Classification

INTERVENTIONAL

Study Start Date

2024-05-28

Study Completion Date

2025-08-01

Brief Summary

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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.

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Detailed Description

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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

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Cardiac Amyloidosis

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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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.

Group Type EXPERIMENTAL

Cardiac amyloidosis deep learning model

Intervention Type 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.

Interventions

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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.

Intervention Type DEVICE

Eligibility Criteria

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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.

Minimum Eligible Age

50 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No