AI-Enabled ECG for Liver Disease

(ADVANCE Trial)

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Mayo Clinic

Disqualifiers: Cirrhosis

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

Compensation: Varies

Number of Visits: Unknown

400 Participants Needed

Trial Summary

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

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

Is the AI-enabled ECG generally safe for humans?

The AI-enabled ECG is a non-invasive and low-cost test that has been used safely in large studies to screen for heart conditions, suggesting it is generally safe for humans. ^{1 2 3 4 5}

How is the treatment ACE 2.0 different from other treatments for liver disease?

ACE 2.0 is unique because it uses artificial intelligence (AI) to analyze electrocardiograms (ECGs) to detect liver disease-related heart changes, offering a non-invasive and potentially more accurate way to assess disease severity compared to traditional methods. ^{1 6 7 8 9}

What is the purpose of this trial?

The overall objectives of this study are to determine the effectiveness of ACE 2.0 model in early detection of advanced liver fibrosis, and to determine the acceptance and barriers for use of an AI-enabled algorithm for prediction of liver disease in primary care.

Research Team

Douglas Simonetto, MD

Principal Investigator

Mayo Clinic

Eligibility Criteria

This trial is for adults who are getting an ECG test and whose doctors can order such tests. It's aimed at those in primary care settings, including physicians, nurse practitioners, and physician assistants who agree to participate. People with known advanced liver disease or a history of cirrhosis are not eligible.

Inclusion Criteria

You are attended to by a primary healthcare provider such as a doctor, nurse practitioner or physician assistant.

Information will be gathered from patients' EMRs.

You possess the capacity to order an electrocardiogram (ECG).

Exclusion Criteria

Patients with known cirrhosis based on noninvasive fibrosis assessment tests, liver biopsy or complications of decompensated disease, or with a documented history of cirrhosis identified by clinical notes

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

The ACE 2.0 model is used to alert providers to the likelihood of advanced liver disease with a recommendation for a FibroTest-ActiTest

6 months

Follow-up

Participants are monitored for safety and effectiveness after the intervention

6 months

Treatment Details

Interventions

• ACE 2.0

Trial Overview The study is testing the ACE (AI-Cirrhosis-ECG) 2.0 model to see if it can effectively detect early signs of severe liver fibrosis using AI analysis of ECG results. The goal is also to assess how well this AI tool is accepted in a primary care environment.

Participant Groups

Group I: Electrocardiogram AI Group Experimental Treatment 1 Intervention

The ACE (AI-Cirrhosis-ECG) 2.0 will be used to alert primary care providers to the likelihood of advanced liver disease with a recommendation for laboratory tests.

Group II: Usual Care Group Active Control 1 Intervention

Primary care providers will treat subject per standard of care

Who Is Running the Clinical Trial?

Mayo Clinic

Lead Sponsor

Trials

3,427

Recruited

3,221,000+

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]

The AI-ECG algorithm demonstrated strong performance in detecting left ventricular systolic dysfunction (LVSD) in an external population of 4277 adults, achieving an area under the receiver operating curve of 0.82, indicating good accuracy.

While the AI-ECG showed high specificity (97.4%) and accuracy (97.0%), its sensitivity was lower at 26.9%, suggesting that population-specific cut-offs may be needed for optimal clinical use, especially given the differences in patient characteristics compared to the original study.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. Attia, IZ., Tseng, AS., Benavente, ED., et al.[2021]

References

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

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

2. England pubmed.ncbi.nlm.nih.gov

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

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

Automatic classification of healthy and disease conditions from images or digital standard 12-lead electrocardiograms. [2021]

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

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

5. England pubmed.ncbi.nlm.nih.gov

Application of artificial intelligence to the electrocardiogram. [2021]

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

Artificial Intelligence and the Risk for Intuition Decline in Clinical Medicine. [2022]

7. Netherlands pubmed.ncbi.nlm.nih.gov

External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. [2021]

8. England pubmed.ncbi.nlm.nih.gov

Usefulness of multi-labelling artificial intelligence in detecting rhythm disorders and acute ST-elevation myocardial infarction on 12-lead electrocardiogram. [2023]

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

Development of the AI-Cirrhosis-ECG Score: An Electrocardiogram-Based Deep Learning Model in Cirrhosis. [2023]