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NCT Number: NCT06511505

NOrthwestern Tempus AI-enaBLed Electrocardiography (NOTABLE) 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.

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Key information

Conditions

Atrial Fibrillation Arrhythmia Arrhythmias, Cardiac Cardiovascular Diseases Heart Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Valvular Disease

Age range

40 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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

Exclusion criteria

  • 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

Treatment and study plan

Risk-Based Assessment for Cardiac Dysfunction

Device

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.

Other names: rECHOmmend, ECG-AF

Primary outcomes

  1. 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.

  2. 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.

  3. 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.

Secondary outcomes

  1. Incidence of New Atrial Fibrillation-Related Therapy (Composite)

    Time frame: 6 months from index ECG

    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.

  2. Incidence of New Structural Heart Disease-Related Therapy (Composite)

    Time frame: 6 months from index ECG

    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.

  3. Incidence of Cardiovascular Death

    Time frame: 6 months from index ECG

    Number of participants who died from a cardiovascular cause, identified by EHR mortality data and/or documented cause of death.

  4. Incidence of Myocardial Infarction

    Time frame: 6 months from index ECG

    Number of participants with a new myocardial infarction, identified by ICD-10-CM diagnosis code in the EHR.

  5. Incidence of Hospitalization for a Cardiovascular Cause

    Time frame: 6 months from index ECG

    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.

Other outcomes

  1. Total Cost of Care

    Time frame: Up to 5 years from index ECG

    Total cost of care per participant managed by Northwestern Medicine clinicians using ECG-based predictive models compared to that of patients managed by Northwestern Medicine clinicians that are not using ECG-based predictive models, in US dollars, calculated from billing and procedural (CPT) codes recorded in the EHR and converted to cost using standard procedural code cost estimates.

  2. Incidence of Cardiovascular Death, Myocardial Infarction, or Hospitalization for a Cardiovascular Cause (Long-Term)

    Time frame: Up to 5 years from index ECG

    Number of participants experiencing cardiovascular death, myocardial infarction, or hospitalization for a cardiovascular cause (including heart failure and stroke), identified by EHR mortality data, ICD-10-CM diagnosis codes, and inpatient encounter records.

Sponsors and collaborators

Lead sponsor

Northwestern University

Other

Collaborators

  • Tempus AI

Registry information

Official study 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

Acronym: NOTABLE

Important dates

Study start
2024
Primary completion
2027
Study completion
2028
First posted
Jul 22, 2024
Registry last updated
Aug 20, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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