Recruiting

AI-based ECG

Sponsor:

Northwestern University

Code:

NCT06511505

Conditions

Atrial Fibrillation

Cardiovascular Diseases

Arrhythmia

Valvular Disease

Eligibility Criteria

Sex: All

Age: 40+

Healthy Volunteers: Accepted

Interventions

Risk-Based Assessment for Cardiac Dysfunction

Study Details

Brief 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. 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.

Conditions

Atrial Fibrillation

Cardiovascular Diseases

Arrhythmia

Valvular Disease

Study ID

NCT06511505

Start date

Sep 16, 2024

Status verified date

Aug, 2026

Completion date

Sep, 2028

Anticipated

Primary completion date

Sep, 2027

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 40+

Healthy Volunteers: Accepted

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

Study Design

Enrollment

1000 participants

Anticipated

Allocation

Randomized

Intervention Model

Parallel Assignment

Primary purpose

Screening

Interventions and Outcome Measures

Arms

experimental: Intervention

Care teams randomized to the intervention will have access to the AI-enabled ECG-based screening tool.

no intervention: Control

Care teams randomized to control will continue routine practice without access to the AI-enabled ECG-based screening tool.

Interventions

Risk-Based Assessment for Cardiac Dysfunction

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.

Primary outcome measure

  • Incidence of New Atrial Fibrillation Diagnosis [ Time Frame: 6 months from index ECG ]
  • Incidence of New Structural Heart Disease Diagnosis (Composite) [ Time Frame: 6 months from index ECG ]
  • Incidence of New Cardiovascular Diagnosis (Overall Composite: AF + SHD) [ Time Frame: 6 months from index ECG ]

Central Contacts and Locations

Locations

Northwestern University

Recruiting

Chicago, Illinois, United States, 60611

Contacts

Sanjiv Shah, MD

312-498-0894sanjiv.shah@northwestern.edu

Uma Mylavarapu

uma.mylavarapu@northwestern.edu

More Information

Sponsor

Northwestern University

Last update posted

Aug 20, 2026

Last verified

Aug, 2026

Keywords

  • early detection
  • artificial intelligence
  • structural heart disease
  • atrial fibrillation
  • cardiac diagnostics

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-09. This information was provided to ClinicalTrials.gov by Northwestern University on 2026-08-20.