NOrthwestern Tempus AI-enaBLed Electrocardiography (NOTABLE) Trial
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age40+
SponsorNorthwestern University
About this trial
The goal of this clinical trial is to determine if a machine learning/artificial intelligence (AI)-based electrocardiogram (ECG) algorithm (rECHOmmend and ECG-AF) can identify undiagnosed cardiovascular disease in patients. It will also examine the safety and effectiveness of using this AI-based tool in a clinical setting. The main questions it aims to answer are:
1. Can the AI-based ECG algorithm improve the detection of atrial fibrillation and structural heart disease? 2. How does the use of this algorithm affect clinical decision-making and patient outcomes?
Researchers will compare the outcomes of healthcare providers who receive the AI-based ECG results to those who do not. Participants (healthcare providers) will:
Be randomized into two groups: one that receives AI-based ECG results and one that does not.
In the intervention group, receive an assessment of their patient's risk of atrial fibrillation or structural heart disease with each ordered ECG.
Decide whether to perform further clinical evaluation based on the AI-generated risk assessment as part of routine clinical care.
Eligibility criteria
Qualifiers
Atrial fibrillation algorithm
Age 65 or over
ECG obtained as part of routine clinical care
Structural heart disease algorithm
Disqualifiers
Atrial fibrillation algorithm
No history of AF
No permanent pacemaker (PPM) or implantable cardioverter defibrillator (ICD)
No recent cardiac surgery (within the preceding 30 days)
Trial design
Treatments tested in this trial
- Risk-Based Assessment for Cardiac Dysfunction
Treatment groups
1,000 Participants
are divided into 2 treatment groupsSponsors and collaborators
Northwestern University
Lead sponsor
Tempus AI
Collaborator
Source ClinicalTrials.gov