1000 Participants Needed

AI-Assisted Heart Monitoring for Heart Disease

(ECG-AID Trial)

Recruiting at 2 trial locations
ES
Overseen ByECG-AID Study
Age: 18+
Sex: Any
Trial Phase: Academic
Sponsor: Tempus AI
No Placebo GroupAll trial participants will receive the active study treatment (no placebo)

What You Need to Know Before You Apply

What is the purpose of this trial?

Atrial fibrillation is an abnormal beating of the heart that can lead to stroke or heart failure. Structural heart diseases are conditions that affect the heart valves or heart muscle and can cause permanent heart damage if left untreated. Sometimes people have atrial fibrillation or structural heart disease and do not know it. The purpose of this study is to evaluate two devices that can predict who has or may develop atrial fibrillation or structural heart disease based on the results of an electrocardiogram.

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.

Is AI-assisted heart monitoring safe for humans?

The research on AI-assisted heart monitoring, particularly using electrocardiograms (ECGs), suggests it is a safe, non-invasive tool for detecting heart issues like left ventricular dysfunction. It has been tested on large groups of patients and adapted for use with wearable devices, showing promise as a reliable screening method without reported safety concerns.12345

How does the AI-Assisted Heart Monitoring treatment differ from other heart disease treatments?

This treatment is unique because it uses artificial intelligence to analyze electrocardiograms (ECGs) for real-time heart monitoring, which can detect heart issues like arrhythmias and ventricular dysfunction more efficiently and accurately than traditional methods. It allows for continuous monitoring in a home environment, providing immediate and actionable insights to both patients and clinicians.12678

What data supports the effectiveness of the treatment Electrocardiogram-based Artificial Intelligence for heart disease?

Research shows that using artificial intelligence with electrocardiograms (ECGs) can effectively identify heart problems like left ventricular dysfunction, which can lead to heart failure. In a study with over 52,000 patients, the AI model accurately detected heart issues with high sensitivity and specificity, making it a powerful tool for early screening.1291011

Who Is on the Research Team?

JP

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?
2Treatment groups
Experimental Treatment
Group I: SHD CohortExperimental Treatment1 Intervention
Group II: AF CohortExperimental Treatment1 Intervention

Find a Clinic Near You

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

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.
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]
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.
External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction.Attia, IZ., Tseng, AS., Benavente, ED., et al.[2021]
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.
Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram.Attia, ZI., Kapa, S., Lopez-Jimenez, F., et al.[2022]

Citations

Why recording of an electrocardiogram should be required in every inpatient and outpatient encounter of patients with heart failure. [2011]
Artificial Intelligence-Augmented Electrocardiogram Detection of Left Ventricular Systolic Dysfunction in the General Population. [2023]
Importance of external validation and subgroup analysis of artificial intelligence in the detection of low ejection fraction from electrocardiograms. [2023]
External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. [2021]
Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. [2022]
Current and future approaches to nonclinical cardiovascular safety assessment. [2021]
Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices. [2023]
Prediction of drug-related cardiac adverse effects in humans--A: creation of a database of effects and identification of factors affecting their occurrence. [2013]
Patient-Level Artificial Intelligence-Enhanced Electrocardiography in Hypertrophic Cardiomyopathy: Longitudinal Treatment and Clinical Biomarker Correlations. [2023]
10.United Statespubmed.ncbi.nlm.nih.gov
An artificial intelligence-enabled ECG algorithm for comprehensive ECG interpretation: Can it pass the 'Turing test'? [2022]
11.United Statespubmed.ncbi.nlm.nih.gov
The Role of Artificial Intelligence in Arrhythmia Monitoring. [2021]
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