20143 Participants Needed

AI-Enhanced ECG for Cardiac Amyloidosis

Recruiting at 8 trial locations
Age: 18+
Sex: Any
Trial Phase: Academic
Sponsor: Mayo Clinic
No Placebo GroupAll trial participants will receive the active study treatment (no placebo)

What You Need to Know Before You Apply

What is the purpose of this trial?

The purpose of this study is to assess a novel artificial intelligence (AI)-enabled electrocardiogram (ECG)-based screening tool for improving the diagnosis of cardiac amyloidosis (CA).

Do I need to stop my current medications for this trial?

The trial information does not specify whether you need to stop taking your current medications.

How does the AI-enhanced ECG treatment for cardiac amyloidosis differ from other treatments?

The AI-enhanced ECG treatment for cardiac amyloidosis is unique because it uses artificial intelligence to detect the condition early from a standard heart test (ECG), which can help identify the disease sooner than traditional methods. This early detection is crucial because cardiac amyloidosis is often underdiagnosed due to its similarity to other heart conditions.12345

What data supports the effectiveness of the AI ECG Amyloid algorithm treatment for cardiac amyloidosis?

Research shows that AI models using electrocardiograms (ECG) can effectively detect cardiac amyloidosis, with accuracy rates between 85% and 91%. This suggests that the AI ECG Amyloid algorithm could help identify this condition earlier, potentially improving treatment outcomes.14567

Who Is on the Research Team?

AD

Angela Dispenzieri, MD

Principal Investigator

Mayo Clinic

Are You a Good Fit for This Trial?

This trial is for Mayo Clinic providers who agree to participate and are involved in caring for adult patients within cardiology or hematology departments. There are no specific exclusion criteria, making it broadly accessible to these healthcare professionals.

Inclusion Criteria

Mayo Clinic providers who consent to participate on this study
I am under the care of a Mayo Clinic cardiologist or hematologist.

Exclusion Criteria

N/A

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Providers receive AI ECG algorithm and educational guidance to improve diagnosis of cardiac amyloidosis

1 year

Follow-up

Participants are monitored for safety and effectiveness after intervention

1 year

What Are the Treatments Tested in This Trial?

Interventions

  • AI ECG Amyloid algorithm
Trial Overview The study is evaluating an innovative AI-powered tool that analyzes ECG readings to help detect cardiac amyloidosis more effectively. This could potentially improve diagnosis rates of this heart condition.
How Is the Trial Designed?
2Treatment groups
Experimental Treatment
Active Control
Group I: Notification of the AI ECG algorithm and the A3E scoresExperimental Treatment1 Intervention
Provider-facing recommendation report that alerts to provider for positive AI ECG Amyloid Score, standardized amyloid order set, diagnostic algorithm and reminders
Group II: Usual CareActive Control1 Intervention
No alerts to provider for positive AI ECG Amyloid Score, standardized amyloid order set, diagnostic algorithm and reminders

Find a Clinic Near You

Who Is Running the Clinical Trial?

Mayo Clinic

Lead Sponsor

Trials
3,427
Recruited
3,221,000+

Published Research Related to This Trial

The study utilized machine learning algorithms to analyze cardiac strain and function data from 43 patients with cardiac amyloidosis, achieving a high diagnostic accuracy of 90.9% using the SVM RBF kernel, indicating its potential for improving CA diagnostics.
Key predictors for cardiac amyloidosis were identified, including bi-atrial longitudinal strain and left and right atrial ejection fraction, suggesting that these parameters could enhance non-invasive diagnostic approaches in clinical settings.
A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function.Eckstein, J., Moghadasi, N., Körperich, H., et al.[2022]

Citations

Outcome and incidence of appropriate implantable cardioverter-defibrillator therapy in patients with cardiac amyloidosis. [2018]
Artificial Intelligence-Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis. [2022]
Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms. [2021]
Evaluation of patients with cardiac amyloidosis using echocardiography, ECG and right heart catheterization. [2016]
Associations of Electrocardiographic Parameters with Left Ventricular Longitudinal Strain and Prognosis in Cardiac Light Chain Amyloidosis. [2021]
A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function. [2022]
Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis. [2023]
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