AI-Assisted Heart Monitoring for Heart Disease

(ECG-AID Trial)

Recruiting at 2 trial locations

Overseen By ECG-AID Study

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Tempus AI

Disqualifiers: Pacemaker, Atrial fibrillation, Cardiac surgery, others

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

Compensation: Varies

Number of Visits: 1

1000 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests two devices that use AI to predict the likelihood of developing atrial fibrillation (an irregular heartbeat) or structural heart disease (problems with heart valves or muscles). The goal is to detect these conditions early to prevent strokes or permanent heart damage. Participants will be divided into two groups: one for those at risk of atrial fibrillation and another for those at risk of structural heart disease. Suitable candidates have had an ECG (a heart test) as part of their regular medical care and can identify a doctor to receive their test results. As an unphased trial, this study offers a unique opportunity to contribute to groundbreaking research that could enhance early detection and prevention strategies.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. Please consult with the study team or your healthcare provider for guidance.

What prior data suggests that these devices are safe for heart monitoring?

Research has shown that using artificial intelligence (AI) with electrocardiograms (ECGs) is generally safe for people. Studies have found that this method can predict heart issues, such as irregular heartbeats, without harming patients. AI with ECGs has been tested in various locations to detect heart problems early, and no serious side effects have been reported.

The AI technology analyzes ECG data by examining the heart's electrical activity to identify potential problems. Since it only reads existing data, it remains safe and non-invasive, posing no risk or discomfort to the person being monitored.

No evidence of negative effects from using AI with ECGs has emerged in these studies, suggesting that the technology is safe for monitoring heart health. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about AI-assisted heart monitoring because it could revolutionize how we detect and manage heart conditions like Structural Heart Disease (SHD) and Atrial Fibrillation (AF). Unlike traditional methods that rely heavily on manual interpretation of electrocardiograms (ECGs) and echocardiograms, this approach uses artificial intelligence to enhance accuracy and early detection. For the SHD Cohort, the use of AI in interpreting echocardiograms could lead to more precise diagnoses, while the AF Cohort benefits from an innovative ECG patch monitor that continuously tracks heart activity over extended periods. This method might catch irregular heart rhythms earlier than conventional short-term monitoring, potentially leading to timely interventions and better patient outcomes.

What evidence suggests that these devices are effective for predicting atrial fibrillation or structural heart disease?

This trial will compare AI-assisted heart monitoring in two separate cohorts. Research has shown that using AI with electrocardiography (ECG) can effectively identify heart problems such as atrial fibrillation (AF) and structural heart disease. Participants in the AF Cohort will wear a 2-week ECG patch monitor, which may be repeated up to three times over 12 months. Studies have found that AI-guided ECGs can detect AF even when the heart beats normally. Meanwhile, the SHD Cohort will undergo a single echocardiogram to assess the risk of structural heart disease. This technology uses advanced computer models to predict the risk of AF from standard 12-lead ECGs, a common and simple test. AI-driven ECG tools can provide quick and accurate information, potentially helping doctors manage heart diseases more effectively. This means AI in ECGs could help detect heart issues earlier, which is crucial for preventing serious problems like stroke or heart failure. ^{1 6 7 8 9}

Who Is on the Research Team?

John Pfeifer, MD

Principal Investigator

Tempus AI, Inc.

Are You a Good Fit for This Trial?

This trial is for adults aged 40 or older who have had an ECG during routine care. For the atrial fibrillation group, participants must be 65 or older and able to identify a healthcare provider to receive patch monitor results. For structural heart disease, they need to be at least 40 and can't have severe valve issues or poor heart pump function.

Inclusion Criteria

I am over 40 and had an ECG for care between the study dates. I can name a healthcare provider for my echocardiogram results.

I am 65 or older and can name a healthcare provider to get my heart monitor results.

I am 40 or older and have had an ECG as part of my regular health care.

Exclusion Criteria

You cannot finish the follow-up studies on time, are currently in the hospital, have a permanent pacemaker or implanted cardiac defibrillator, have a history of certain heart conditions, had recent or planned heart surgery, or are allergic to adhesive.

I can follow the study schedule and am not currently hospitalized. I don't have severe heart valve issues, weak heart muscle, thick heart walls, or an allergy to ultrasound gel.

Retrospective Phase: Patients who have previously requested that their data not be involved in any secondary use application such as a research study

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Initial Assessment

Participants undergo initial assessment including echocardiogram for SHD cohort and ECG patch monitor for AF cohort

2 weeks

1 visit (in-person)

Monitoring

Participants in the AF cohort wear an ECG patch monitor for up to 3 times over 12 months

12 months

Follow-up

Participants are monitored for safety and effectiveness after initial assessment

6 months

What Are the Treatments Tested in This Trial?

Interventions

• Electrocardiogram-based Artificial Intelligence

Trial Overview The study tests two devices: an echocardiogram and Zio Patch Monitor, which may help predict atrial fibrillation or structural heart disease using electrocardiogram results. It includes looking back at past patient data as well as monitoring new patients.

How Is the Trial Designed?

Group I: SHD Cohort Experimental Treatment 1 Intervention

Will be comprised 500 participants at increased risk for Structural Heart Disease (SHD) will be referred for a single echocardiogram.

Group II: AF Cohort Experimental Treatment 1 Intervention

Will be comprised of 500 participants predicted to be increased risk for Atrial Fibrillation (AF) will receive a 2-week ECG patch monitor to wear (up to 3 times over 12 months),

Who Is Running the Clinical Trial?

Tempus AI

Lead Sponsor

Trials

18

Recruited

20,700+

Tempus Labs

Lead Sponsor

Trials

17

Recruited

20,200+

Published Research Related to This Trial

The study emphasizes the need to expand cardiovascular safety assessments beyond just electrocardiogram events to include a wider range of cardiovascular parameters, using real-life case studies to demonstrate progress.

To enhance the prediction of cardiovascular events in patients, the development of more relevant humanized models is essential, allowing for better translation of findings to novel therapeutic approaches.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Current and future approaches to nonclinical cardiovascular safety assessment. Collins, TA., Rolf, MG., Pointon, A.[2021]

A study involving 221,846 ECGs from four institutions aimed to develop AI models for detecting left ventricular systolic dysfunction (LVSD) with an ejection fraction (EF) <40%, showing promising internal accuracy but variable external validation results.

The performance of AI models varied significantly between institutions, emphasizing the need for external validation and careful consideration of patient characteristics and ECG abnormalities when using AI for LVSD detection.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Importance of external validation and subgroup analysis of artificial intelligence in the detection of low ejection fraction from electrocardiograms. Yagi, R., Goto, S., Katsumata, Y., et al.[2023]

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]

A comprehensive database of 396,985 drug-related cardiac adverse effects (AEs) linked to 1,632 approved drugs was created, revealing significant clusters of AEs affecting heart function, such as arrhythmias and heart failure.

The study identified that cardiac AEs are highly correlated with drugs targeting cardiovascular functions and specific mechanisms of action, including interactions with various receptors like alpha- and beta-adrenergic receptors, which may help predict and understand these adverse effects.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Prediction of drug-related cardiac adverse effects in humans--A: creation of a database of effects and identification of factors affecting their occurrence. Matthews, EJ., Frid, AA.[2013]

Citations

1. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC12227326/

AI‐ECG for early detection of atrial fibrillation: First‐year ... An artificial intelligence algorithm‐guided electrocardiogram (AI‐ECG) has been developed to detect atrial fibrillation (AF) in patients with sinus rhythm (SR).

2. sciencedirect.com sciencedirect.com/science/article/pii/S0022073624000128

Artificial intelligence-enhanced electrocardiography for ... AI-enhanced electrocardiography has enormous potential to improve the management of cardiovascular illness by delivering precise and timely diagnostic insights.

3. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC12292989/

Artificial Intelligence and ECG: A New Frontier in Cardiac ... Conclusions: Modern AI algorithms—especially deep neural networks—show promise in detecting arrhythmias, heart failure, prolonged QT syndrome, ...

4. ahajournals.org ahajournals.org/doi/10.1161/CIRCEP.124.012959

Electrocardiogram-Based Artificial Intelligence to ... ECG-AI is a validated deep learning model to estimate AF risk using the 12-lead ECG, an inexpensive test routinely performed after stroke.

5. jmir.org jmir.org/2024/1/e52139/

Artificial Intelligence–Based Electrocardiographic ... We examined the utility of an artificial intelligence (AI) algorithm that analyzes printed electrocardiograms (ECGs) for outcome prediction in patients with ...

6. jacc.org jacc.org/doi/10.1016/j.jacadv.2025.102041

Electrocardiogram-Based Artificial Intelligence to Identify ... Advances in artificial intelligence (AI) have enabled rapid prediction of disease states using data from the whole ECG, as recently shown for ...

7. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC8748400/

Electrocardiogram-based Deep Learning and Clinical Risk ... Artificial intelligence (AI)-enabled analysis of 12-lead electrocardiograms (ECGs) may facilitate efficient estimation of incident atrial fibrillation (AF) ...

8. sciencedirect.com sciencedirect.com/science/article/pii/S2589750024001729

Articles Artificial intelligence-enabled electrocardiogram for ... Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet ...

9. academic.oup.com academic.oup.com/eurheartj/article/45/46/4920/7740534

Prediction of incident atrial fibrillation using deep learning ... This study introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing its performance with clinical models and AF polygenic score ( ...