AI-powered ECG to detect heart disease and atrial fibrillation in routine care
Official title: NOrthwestern Tempus AI-enaBLed Electrocardiography (NOTABLE) Trial: A Pragmatic, Real-world Study of an Artificial-intelligence Enabled Electrocardiogram Algorithms to Improve the Diagnosis of Cardiovascular Disease
Run by Northwestern University · NCT06511505 · Data updated 2026-11-12
This trial tests whether an AI-based ECG tool can help doctors find undiagnosed heart disease and atrial fibrillation in patients and whether using the tool changes clinical decisions and outcomes.
Quick facts
| Study type | Interventional |
|---|---|
| Ages | 40 Years and up |
| Sex | All |
| Enrollment | 1000 (estimated) |
| Sponsor | Northwestern University Academic / other |
| Locations | 1 site (Chicago, Illinois) |
| Status | Recruiting |
| Started | September 16, 2024 |
| Main results expected | September 2027 |
| Study ends | September 2028 |
| Registry record updated | August 20, 2026 |
| Trial ID | NCT06511505 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are adults aged 40 and older with routine ECGs, with separate AF and structural disease algorithms.
✕Probably not a fit
Patients without undiagnosed cardiovascular disease or those not receiving follow-up testing despite high risk may see no benefit.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
In this randomized study, clinicians will be assigned to receive or not receive AI-derived ECG risk results when ordering 12-lead ECGs. The AI tools analyze ECGs to predict risk of atrial fibrillation and structural heart disease and may prompt follow-up tests if high risk is detected. Outcomes include new diagnoses, treatments started, and cardiovascular events. The trial is conducted at Northwestern Medicine and compares care with AI-assisted risk information versus standard care. The study aims to determine safety, practicality, and impact on patient management in real-world clinical settings.
Why it matters
Potential benefit: If successful, clinicians may detect more undiagnosed heart conditions earlier, allowing timely treatment decisions.
How similar studies have gone: Previous work has shown promise for AI-guided ECG interpretation, but this study adds randomized clinician-facing implementation in routine care.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Atrial fibrillation algorithm 1. Age 65 or over 2. ECG obtained as part of routine clinical care 2. Structural heart disease algorithm 1. Age 40 or over 2. ECG obtained as part of routine clinical care Exclusion Criteria: 1. Atrial fibrillation algorithm 1. No history of AF 2. No permanent pacemaker (PPM) or implantable cardioverter defibrillator (ICD) 3. No recent cardiac surgery (within the preceding 30 days) 2. Structural heart disease algorithm 1. No history of SHD 2. No echocardiogram within the past 1 year
Where it's running
-
Northwestern University
Chicago, Illinois, United States Recruiting
Study contacts
- Principal investigator: Sanjiv Shah, MD — Northwestern University
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.