AI-ECG Algorithm for Low Heart Function

(AIM ECG-AI Trial)

No longer recruiting at 5 trial locations

Overseen By Sarah Hackett

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Anumana, Inc.

Disqualifiers: LVEF ≤ 40%, Systolic heart failure, others

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

Compensation: Varies

Number of Visits: Unknown

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a new AI tool, the Anumana Low EF AI-ECG Algorithm, which uses ECG (a heart test) to detect low heart function, specifically identifying when the heart pumps less effectively. The research aims to determine if using this AI tool during regular check-ups can help doctors diagnose this issue more frequently. Participants will either use the AI tool or continue with regular care. This trial suits individuals who haven't been diagnosed with low heart function but have recent ECG tests available at their doctor's office. As an unphased trial, it offers a unique opportunity to contribute to innovative research that could enhance early diagnosis of heart conditions.

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.

What prior data suggests that this ECG-AI algorithm is safe for detecting low heart function?

Research has shown that the Anumana Low EF AI-ECG Algorithm helps doctors identify low heart function by analyzing ECG (electrocardiogram) data. The FDA approved this AI tool for detecting low ejection fraction, confirming its safety for this purpose. Since it involves no drugs or surgery, reports of negative reactions have not emerged. By using data from a standard heart test, it provides doctors with additional information, indicating that the AI tool is safe and well-tolerated. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the Anumana Low EF AI-ECG Algorithm because it offers a novel approach to detecting low heart function using artificial intelligence. Unlike standard methods that rely on traditional ECG interpretation and imaging tests, this AI-driven algorithm analyzes ECG data to identify heart issues more quickly and accurately. This innovative technique could potentially provide earlier detection and intervention, improving patient outcomes for those with heart conditions.

What evidence suggests that this AI-ECG algorithm is effective for detecting low heart function?

Research shows that the Anumana Low EF AI-ECG Algorithm, which participants in this trial may receive, effectively identifies low left ventricular ejection fraction (LVEF ≤ 40%), a measure of how well the heart pumps blood. Studies have found that this AI tool can detect low ejection fraction early, which is crucial for timely treatment. One study confirmed that using AI-ECG for screening leads to early detection, potentially improving patient health. Healthcare providers view the algorithm as a reliable tool for spotting heart issues during regular check-ups, making it a valuable option. ^{1 2 6 7 8}

Who Is on the Research Team?

Francisco Lopez-Jimenez, MD, MSc, MBA

Principal Investigator

Mayo Clinic

Are You a Good Fit for This Trial?

This trial is for adults aged 18 or older who can have a digital 10-second, 12-lead ECG captured or available in their electronic health records (EHR) for AI analysis at the point-of-care.

Inclusion Criteria

Digital 10 second, 12 Lead ECG captured or available in EHR or ECG data store for AI-ECG analysis at point-of-care

Exclusion Criteria

Known history of LVEF ≤ 40%

Opted out of electronic health record-based research

I have a history of heart failure.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Feasibility Assessment

Feasibility pilot to evaluate integration and usability of the ECG-AI algorithm

6 weeks

Clinical Impact Observation

Observational period to evaluate clinical outcomes using the ECG-AI algorithm

3 months

Follow-up

Participants are monitored for safety and effectiveness after the observational period

90 days

What Are the Treatments Tested in This Trial?

Interventions

• Anumana Low EF AI-ECG Algorithm

Trial Overview The study tests an AI-powered ECG algorithm designed to detect low left ventricular ejection fraction (LVEF), which indicates heart problems. It compares usual care with and without the use of this AI tool in outpatient settings.

How Is the Trial Designed?

Group I: Care-as-Usual Experimental Treatment 1 Intervention

Care-as-Usual

Group II: Anumana Low EF AI-ECG Algorithm Experimental Treatment 1 Intervention

Anumana Low EF AI-ECG Algorithm

Who Is Running the Clinical Trial?

Anumana, Inc.

Lead Sponsor

Trials

2

Recruited

66,200+

Mayo Clinic

Collaborator

Trials

3,427

Recruited

3,221,000+

Published Research Related to This Trial

AI-enhanced electrocardiograms (AI-ECG) can effectively track changes in cardiac structure and function in patients with obstructive hypertrophic cardiomyopathy (HCM) undergoing treatment with mavacamten, as shown in a study involving 13 patients and 216 ECGs.

Both AI-ECG algorithms demonstrated significant reductions in HCM scores during treatment, correlating well with echocardiographic measures and laboratory markers, suggesting that AI-ECG could be a valuable tool for monitoring therapeutic responses in HCM.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Patient-Level Artificial Intelligence-Enhanced Electrocardiography in Hypertrophic Cardiomyopathy: Longitudinal Treatment and Clinical Biomarker Correlations. Siontis, KC., Abreau, S., Attia, ZI., et al.[2023]

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]

Citations

1. anumana.ai anumana.ai/ecg-ai-lef

Anumana ECG-AI LEF ECG-AI™ LEF is Clinically Proven to Detect Low Ejection Fraction (LEF) Early. ECG-AI LEF is a highly effective screening tool for identifying low EF and has the ...

2. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC12017965/

A multicenter pragmatic implementation study of AI-ECG ... A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods

3. anumana.ai anumana.ai/publications/Zt_9EhkAAEYAWNHn

Low Ejection Fraction The purpose of this study was to evaluate echocardiographic characteristics and all-cause mortality risk in FP patients. Methods Patients with ...

4. mcpdigitalhealth.org mcpdigitalhealth.org/article/S2949-7612(24)00104-4/fulltext

Cost-Effectiveness of Artificial Intelligence-Enabled ... To investigate the cost-effectiveness of using artificial intelligence (AI) to screen for low ejection fraction (EF) in routine clinical practice.

5. sciencedirect.com sciencedirect.com/science/article/pii/S2949761224001044

Cost-Effectiveness of Artificial Intelligence-Enabled ... The ECG ai-guided screening for low ejection fraction (EAGLE) clinical trial (NCT04000087) found that AI-ECG enabled the early detection of low EF among ...

6. accessdata.fda.gov accessdata.fda.gov/cdrh_docs/pdf23/K232699.pdf

September 28, 2023 Anumana, Inc. Alexia Haralambous ... The Low Ejection Fraction AI-ECG Algorithm interprets 12-lead ECG voltage times series data using an artificial intelligence-based algorithm.

7. anumana.ai anumana.ai/

Anumana - Unlocking the language of the heart Anumana Receives U.S. FDA 510(k) Clearance for ECG-AI Algorithm to Detect Low Ejection Fraction. Based on pioneering research from Mayo Clinic, ECG-AI LEF ...

8. clinicaltrials.gov clinicaltrials.gov/study/NCT05867407?locStr=United+States&country=United+States&aggFilters=status%3A

A Multicenter Pragmatic Implementation Study of ECG-AI-Based ... A prospective, cluster-randomized, care-as-usual controlled trial to evaluate the impact of an ECG-based artificial intelligence (ECG-AI) algorithm to detect ...