AI-Enabled ECG for Liver Disease
(ADVANCE Trial)
No Placebo GroupAll trial participants will receive the active study treatment (no placebo)
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?
How is the treatment ACE 2.0 different from other treatments for liver disease?
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).
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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
2Treatment groups
Experimental Treatment
Active Control
Group I: Electrocardiogram AI GroupExperimental Treatment1 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 GroupActive Control1 Intervention
Primary care providers will treat subject per standard of care
Find a Clinic Near You
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
Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. [2022]
Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. [2023]
Automatic classification of healthy and disease conditions from images or digital standard 12-lead electrocardiograms. [2021]
A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease. [2023]
Application of artificial intelligence to the electrocardiogram. [2021]
Artificial Intelligence and the Risk for Intuition Decline in Clinical Medicine. [2022]
External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. [2021]
Usefulness of multi-labelling artificial intelligence in detecting rhythm disorders and acute ST-elevation myocardial infarction on 12-lead electrocardiogram. [2023]
Development of the AI-Cirrhosis-ECG Score: An Electrocardiogram-Based Deep Learning Model in Cirrhosis. [2023]
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