AI-Enabled ECG Screening for Cardiovascular Disease

(NOTABLE Trial)

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Northwestern University

Disqualifiers: No AF, No PPM/ICD, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

1000 Participants Needed

Trial Summary

What is the purpose of this trial?

The goal of this clinical trial is to determine if a machine learning/artificial intelligence (AI)-based electrocardiogram (ECG) algorithm (Tempus Next software) can identify undiagnosed cardiovascular disease in patients. It will also examine the safety and effectiveness of using this AI-based tool in a clinical setting. The main questions it aims to answer are: 1. Can the AI-based ECG algorithm improve the detection of atrial fibrillation and structural heart disease? 2. How does the use of this algorithm affect clinical decision-making and patient outcomes? Researchers will compare the outcomes of healthcare providers who receive the AI-based ECG results to those who do not. Participants (healthcare providers) will: Be randomized into two groups: one that receives AI-based ECG results and one that does not. In the intervention group, receive an assessment of their patient's risk of atrial fibrillation or structural heart disease with each ordered ECG. Decide whether to perform further clinical evaluation based on the AI-generated risk assessment as part of routine clinical care.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It focuses on using an AI-based ECG tool to assess heart disease risk.

What data supports the effectiveness of the TEMPUS AI-enabled ECG-based Screening Tool treatment for cardiovascular disease?

AI applied to ECGs can detect hidden heart conditions like atrial fibrillation and left ventricular dysfunction, often before symptoms appear, making it a powerful tool for early diagnosis and management of cardiovascular diseases. ^{1 2 3 4 5}

Is the AI-enabled ECG screening tool safe for humans?

The AI-enabled ECG screening tool is generally considered safe as it uses non-invasive methods to analyze heart activity, similar to traditional ECGs, and has been validated in large clinical datasets. ^{3 4 6 7 8}

How does AI-enabled ECG screening differ from other treatments for cardiovascular disease?

AI-enabled ECG screening is unique because it uses artificial intelligence to analyze electrocardiograms (ECGs) and detect hidden or asymptomatic heart conditions that traditional methods might miss. This approach can identify patterns in large datasets without needing to understand the biological mechanisms, making it a powerful tool for early detection and management of cardiovascular diseases. ^{1 2 3 4 5}

Research Team

Sanjiv Shah, MD

Principal Investigator

Northwestern University

Eligibility Criteria

This trial is for healthcare providers who are assessing patients with potential cardiovascular issues like atrial fibrillation or structural heart disease. Providers will be randomly assigned to either use an AI-based ECG tool in their evaluations or not.

Inclusion Criteria

I am over 40 and have had an ECG as part of my regular health checks.

I am 65 or older and have had an ECG for my heart.

Exclusion Criteria

I have never had atrial fibrillation, don't have a pacemaker or ICD, and haven't had recent heart surgery.

I have no history of heart disease and haven't had a heart ultrasound in the last year.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Care teams randomized to the intervention will have access to the AI-enabled ECG-based screening tool

6 months

Monthly reports

Control

Care teams randomized to control will continue routine practice without access to the AI-enabled ECG-based screening tool

6 months

Follow-up

Participants are monitored for safety and effectiveness after treatment

6 months

Treatment Details

Interventions

• TEMPUS AI-enabled ECG-based Screening Tool

Trial Overview The NOTABLE Trial is testing whether a machine learning AI tool can help detect undiagnosed cardiovascular diseases more effectively when used alongside standard ECG tests in clinical settings.

Participant Groups

Group I: Intervention Experimental Treatment 1 Intervention

Care teams randomized to the intervention will have access to the AI-enabled ECG-based screening tool.

Group II: Control Active Control 1 Intervention

Care teams randomized to control will continue routine practice without access to the AI-enabled ECG-based screening tool.

Who Is Running the Clinical Trial?

Northwestern University

Lead Sponsor

Trials

1,674

Recruited

989,000+

Findings from Research

A study involving 44,959 patients demonstrated that an AI model applied to ECG data can effectively identify asymptomatic left ventricular dysfunction (ALVD), achieving high accuracy (85.7%) and sensitivity (86.3%).

Patients who screened positive for ALVD using the AI model were found to be four times more likely to develop future ventricular dysfunction, highlighting the potential of AI-enhanced ECG as a proactive screening tool for early intervention.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Attia, ZI., Kapa, S., Lopez-Jimenez, F., et al.[2022]

References

1. Switzerland pubmed.ncbi.nlm.nih.gov

Current and Future Use of Artificial Intelligence in Electrocardiography. [2023]

2. Italy pubmed.ncbi.nlm.nih.gov

[Artificial intelligence applied to the electrocardiogram, or is there really a needle in a haystack?] [2023]

3. England pubmed.ncbi.nlm.nih.gov

Application of artificial intelligence to the electrocardiogram. [2021]

4. England pubmed.ncbi.nlm.nih.gov

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. [2023]

5. Germany pubmed.ncbi.nlm.nih.gov

[Artificial intelligence-based ECG analysis: current status and future perspectives-Part 1 : Basic principles]. [2022]

6. United States pubmed.ncbi.nlm.nih.gov

Validation of a multiple‑lead smartphone-based electrocardiograph with automated lead placement for layman use in patients with hypertrophic cardiomyopathy. [2023]

7. England pubmed.ncbi.nlm.nih.gov

A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease. [2023]

8. United States pubmed.ncbi.nlm.nih.gov

Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. [2022]