Trial Outcomes & Findings for Cardiac Amyloidosis Discovery Trial (NCT NCT06469372)

Last Updated: 2025-12-04

Results Overview

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).

Recruitment status

COMPLETED

Study phase

Target enrollment

50 participants

Primary outcome timeframe

Up to 1 year after identification (1 day of participant assessment)

Results posted on

2025-12-04

Participant Flow

Participant milestones

Participant milestones
Measure	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.
Overall Study STARTED	50
Overall Study COMPLETED	50
Overall Study NOT COMPLETED	0

Reasons for withdrawal

Withdrawal data not reported

Baseline Characteristics

Cardiac Amyloidosis Discovery Trial

Baseline characteristics by cohort

Baseline characteristics by cohort
Measure	Intervention Arm n=50 Participants 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.
Age, Continuous	80 years STANDARD_DEVIATION 10 • n=3 Participants
Sex: Female, Male Female	18 Participants n=3 Participants
Sex: Female, Male Male	32 Participants n=3 Participants
Race/Ethnicity, Customized Hispanic	10 Participants n=3 Participants
Race/Ethnicity, Customized Non-Hispanic Black	22 Participants n=3 Participants
Race/Ethnicity, Customized Non-Hispanic White	16 Participants n=3 Participants
Race/Ethnicity, Customized Other or unknown	2 Participants n=3 Participants
Region of Enrollment United States	50 participants n=3 Participants
Orthopedic manifestations Carpal tunnel syndrome	7 Participants n=3 Participants
Orthopedic manifestations Degenerative joint disease	6 Participants n=3 Participants
Orthopedic manifestations Spinal stenosis	7 Participants n=3 Participants

PRIMARY outcome

Timeframe: Up to 1 year after identification (1 day of participant assessment)

Outcome measures

Outcome measures
Measure	Intervention Arm n=50 Participants 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.
Rate of Cardiac Amyloidosis Diagnosis	24 Participants

Adverse Events

Intervention Arm

Serious events: 0 serious events

Other events: 0 other events

Deaths: 0 deaths

Serious adverse events

Adverse event data not reported

Other adverse events

Adverse event data not reported

Additional Information

Timothy J. Poterucha, MD

Columbia University Irving Medical Center

Phone: (212) 932-4537

Email: [email protected]

Results disclosure agreements

Principal investigator is a sponsor employee
Publication restrictions are in place