Atrial FibrillationNOTABLENCT06511505
NOrthwestern Tempus AI-enaBLed Electrocardiography (NOTABLE) Trial
A study testing Risk-Based Assessment for Cardiac Dysfunction in people with Atrial Fibrillation and Cardiovascular Diseases, run by Northwestern University at 1 site in United States.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 40 years and olderHealthy volunteers can take part
- Conditions the study is for
- Atrial Fibrillation, Cardiovascular Diseases, Arrhythmia
- What is being tested?
- Risk-Based Assessment for Cardiac Dysfunction
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,000 participants
- Where
- 1 siteUnited States
- Study dates
- 16 Sep 2024to Sep 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Atrial fibrillation algorithm
- Age 65 or over
- ECG obtained as part of routine clinical care
- Structural heart disease algorithm
- Age 40 or over
- ECG obtained as part of routine clinical care
Who cannot join
- 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)
- Structural heart disease algorithm
- No history of SHD
- No echocardiogram within the past 1 year
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Northwestern University
Chicago, Illinois, 60611
Sanjiv Shah, MD, 312-498-0894, sanjiv.shah@northwestern.edu
Uma Mylavarapu, uma.mylavarapu@northwestern.edu
About this study
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
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:
- Can the AI-based ECG algorithm improve the detection of atrial fibrillation and structural heart disease?
- How does the use of this algorithm affect clinical decision-making and patient outcomes?
Read more
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.
Read the detailed description
There is a large burden of undiagnosed, treatable cardiovascular disease (CVD), encompassing various heart conditions such as arrhythmias (e.g., atrial fibrillation) and structural heart diseases (e.g., valvular disease). Early detection and accurate diagnosis can significantly improve patient outcomes by enabling timely, guideline-based interventions or therapies.
The goal of this study is to leverage machine learning approaches to enhance the detection and diagnosis of CVD. By identifying patients at risk of undiagnosed CVD and referring them for further clinical evaluation, the study aims to improve health outcomes.
Study Overview:
The NOTABLE study will compare the rates of new disease diagnoses, therapeutic interventions, and cardiovascular outcomes between two groups of patients managed by clinicians at Northwestern Medicine:
Patients whose clinicians use ECG predictive models. Patients whose clinicians do not use ECG predictive models.
Intervention Details:
This study utilizes Tempus AI algorithms (rECHOmmend and ECG-AF) to analyze 12-lead ECGs. Clinicians randomized to the intervention group will receive a "Risk-Based Assessment for Cardiac Dysfunction" when ordering a 12-lead ECG within EPIC. If a high-risk result is identified, clinicians receive an EHR inbox message suggesting a follow-up diagnostic test, such as echocardiography and/or ambulatory ECG monitoring.
Outcome Tracking:
Weekly summaries will inform clinicians in the intervention group of high-risk results identified by the AI algorithm. Clinicians in the usual care group will not receive any communication from the study investigators regarding AI predictions.
- Being tested
- Risk-Based Assessment for Cardiac Dysfunction
- Keywords
- early detection, artificial intelligence, structural heart disease, atrial fibrillation, cardiac diagnostics
- Medical subject headings
- Atrial Fibrillation, Cardiovascular Diseases, Arrhythmias, Cardiac
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Risk-Based Assessment for Cardiac DysfunctionDevice
Also called rECHOmmend, ECG-AF
The AI-enabled ECG-based screening tool analyzes 12-lead ECG recordings to identify patients at increased risk for undiagnosed cardiovascular diseases, specifically atrial fibrillation (AF) and structural heart disease (SHD). Clinicians in the intervention group will receive a risk assessment for AF and SHD each time they order an ECG for their patients.
| Study group | Role | What they receive |
|---|---|---|
| Intervention | Experimental | Care teams randomized to the intervention will have access to the AI-enabled ECG-based screening tool. Receives: Device: Risk-Based Assessment for Cardiac Dysfunction |
| Control | No intervention | Care teams randomized to control will continue routine practice without access to the AI-enabled ECG-based screening tool. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Incidence of New Atrial Fibrillation Diagnosis
Number of participants with a new diagnosis of atrial fibrillation, identified by ICD-10-CM diagnosis code entry in the electronic health record (EHR), among patients ≥65 years old without a prior AF diagnosis who received a 12-lead ECG as part of routine clinical care.
Measured: 6 months from index ECG
Incidence of New Structural Heart Disease Diagnosis (Composite)
Number of participants with a new diagnosis of one or more of the following, identified by ICD-10-CM diagnosis code and/or echocardiographic report in the EHR: moderate or severe aortic stenosis, moderate or severe aortic regurgitation, moderate or severe mitral stenosis, severe mitral regurgitation, severe tricuspid regurgitation, left ventricular ejection fraction ≤40%, or interventricular septal thickness (IVSd) >15 mm - among patients ≥40 years old without prior SHD diagnosis who received a 12-lead ECG as part of routine clinical care.
Measured: 6 months from index ECG
Incidence of New Cardiovascular Diagnosis (Overall Composite: AF + SHD)
Number of participants with a new diagnosis of atrial fibrillation and/or any structural heart disease component listed in Primary Outcome Measure 2, identified by ICD-10-CM diagnosis code and/or echocardiographic report in the EHR.
Measured: 6 months from index ECG
Secondary outcomes (5)
Incidence of New Atrial Fibrillation-Related Therapy (Composite)
Number of participants initiated on one or more of the following after index ECG, identified by EHR medication order/administration record and procedural (CPT) codes: antiarrhythmic medication, atrioventricular (AV) nodal blocking agent, anticoagulant medication, or AF ablation procedure.
Measured: 6 months from index ECG
Incidence of New Structural Heart Disease-Related Therapy (Composite)
Number of participants initiated on one or more of the following after index ECG, identified by EHR medication order/administration record and procedural (CPT) codes: medication for left ventricular systolic dysfunction (beta blocker, ACE-I/ARB/ARNI, MRA, or SGLT2 inhibitor); valvular heart disease therapy (valve repair or replacement); or new therapy for hypertrophic cardiomyopathy, cardiac amyloidosis, or hypertensive heart disease.
Measured: 6 months from index ECG
Incidence of Cardiovascular Death
Number of participants who died from a cardiovascular cause, identified by EHR mortality data and/or documented cause of death.
Measured: 6 months from index ECG
Incidence of Myocardial Infarction
Number of participants with a new myocardial infarction, identified by ICD-10-CM diagnosis code in the EHR.
Measured: 6 months from index ECG
Incidence of Hospitalization for a Cardiovascular Cause
Number of participants hospitalized for a cardiovascular cause, including heart failure and stroke, identified by inpatient encounter records and ICD-10-CM diagnosis codes in the EHR.
Measured: 6 months from index ECG
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jul 2024
- Study start16 Sep 2024
- Primary completionSep 2027 (estimated)
- Study completionSep 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Atrial FibrillationStudies of the same condition, open and under way.
- More trials run by Northwestern UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Northwestern University
- Sponsor type
- University, hospital or other
- Collaborators
- Tempus AI
- Responsible party
- Principal investigator, Sanjiv Shah, Northwestern University
- Investigators
- Sanjiv Shah, MD, Principal investigator, Northwestern University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06511505
- Sponsor's protocol ID
- STU00220862
- Acronym
- NOTABLE
- Status last verified
- Aug 2026
- First submitted
- 16 Jul 2024
- Last updated
- 20 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
Study at a glance
From the registry record, last updated by the sponsor on 20 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT06511505
- Acronym / sponsor ID
- NOTABLE / STU00220862
- Keywords
- early detection, artificial intelligence, structural heart disease, atrial fibrillation, cardiac diagnostics
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Northwestern UniversityUniversity, hospital or other
- Collaborators
- Tempus AI
- Study start
- 16 Sep 2024 (actual)
- Primary completion
- Sep 2027 (estimated)
- Study completion
- Sep 2028 (estimated)
- First posted
- 22 Jul 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- Can the AI-based ECG algorithm improve the detection of atrial fibrillation and structural heart disease?
- 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.
Detailed description
There is a large burden of undiagnosed, treatable cardiovascular disease (CVD), encompassing various heart conditions such as arrhythmias (e.g., atrial fibrillation) and structural heart diseases (e.g., valvular disease). Early detection and accurate diagnosis can significantly improve patient outcomes by enabling timely, guideline-based interventions or therapies.
The goal of this study is to leverage machine learning approaches to enhance the detection and diagnosis of CVD. By identifying patients at risk of undiagnosed CVD and referring them for further clinical evaluation, the study aims to improve health outcomes.
Study Overview:
The NOTABLE study will compare the rates of new disease diagnoses, therapeutic interventions, and cardiovascular outcomes between two groups of patients managed by clinicians at Northwestern Medicine:
Patients whose clinicians use ECG predictive models. Patients whose clinicians do not use ECG predictive models.
Intervention Details:
This study utilizes Tempus AI algorithms (rECHOmmend and ECG-AF) to analyze 12-lead ECGs. Clinicians randomized to the intervention group will receive a "Risk-Based Assessment for Cardiac Dysfunction" when ordering a 12-lead ECG within EPIC. If a high-risk result is identified, clinicians receive an EHR inbox message suggesting a follow-up diagnostic test, such as echocardiography and/or ambulatory ECG monitoring.
Outcome Tracking:
Weekly summaries will inform clinicians in the intervention group of high-risk results identified by the AI algorithm. Clinicians in the usual care group will not receive any communication from the study investigators regarding AI predictions.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Intervention | Experimental | Device: Risk-Based Assessment for Cardiac DysfunctionCare teams randomized to the intervention will have access to the AI-enabled ECG-based screening tool. |
| Control | No intervention | –Care teams randomized to control will continue routine practice without access to the AI-enabled ECG-based screening tool. |
Who may qualify
- Ages
- 40 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- Atrial fibrillation algorithm
- Structural heart disease algorithm
Key exclusion criteria (7 listed)
- Atrial fibrillation algorithm
- Structural heart disease algorithm
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Risk-Based Assessment for Cardiac DysfunctionAlso: rECHOmmend, ECG-AFThe AI-enabled ECG-based screening tool analyzes 12-lead ECG recordings to identify patients at increased risk for undiagnosed cardiovascular diseases, specifically atrial fibrillation (AF) and structural heart disease (SHD). Clinicians in the intervention group will receive a risk assessment for AF and SHD each time they order an ECG for their patients. | Device | Intervention |
Primary outcome
Incidence of New Atrial Fibrillation Diagnosis
Time frame: 6 months from index ECG
Number of participants with a new diagnosis of atrial fibrillation, identified by ICD-10-CM diagnosis code entry in the electronic health record (EHR), among patients ≥65 years old without a prior AF diagnosis who received a 12-lead ECG as part of routine clinical care.
Incidence of New Structural Heart Disease Diagnosis (Composite)
Time frame: 6 months from index ECG
Number of participants with a new diagnosis of one or more of the following, identified by ICD-10-CM diagnosis code and/or echocardiographic report in the EHR: moderate or severe aortic stenosis, moderate or severe aortic regurgitation, moderate or severe mitral stenosis, severe mitral regurgitation, severe tricuspid regurgitation, left ventricular ejection fraction ≤40%, or interventricular septal thickness (IVSd) >15 mm - among patients ≥40 years old without prior SHD diagnosis who received a 12-lead ECG as part of routine clinical care.
Incidence of New Cardiovascular Diagnosis (Overall Composite: AF + SHD)
Time frame: 6 months from index ECG
Number of participants with a new diagnosis of atrial fibrillation and/or any structural heart disease component listed in Primary Outcome Measure 2, identified by ICD-10-CM diagnosis code and/or echocardiographic report in the EHR.
5 secondary outcomes
Incidence of New Atrial Fibrillation-Related Therapy (Composite)
Time frame: 6 months from index ECG
Incidence of New Structural Heart Disease-Related Therapy (Composite)
Time frame: 6 months from index ECG
Incidence of Cardiovascular Death
Time frame: 6 months from index ECG
Incidence of Myocardial Infarction
Time frame: 6 months from index ECG
Incidence of Hospitalization for a Cardiovascular Cause
Time frame: 6 months from index ECG
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Aug 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Sanjiv Shah, MD principal investigator
Northwestern University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Risk-Based Assessment for Cardiac Dysfunction in people with Atrial Fibrillation and Cardiovascular Diseases, run by Northwestern University at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 20 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Incidence of New Atrial Fibrillation Diagnosis (time frame: 6 months from index ECG). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Aug 2026; AzTrials last refreshed its copy on 26 Oct 2026.