NOrthwestern Tempus AI-enaBLed Electrocardiography (NOTABLE) Trial

NCT ID: NCT06511505

Last Updated: 2024-07-22

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 1000 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-08-03
• Study Completion Date: 2026-02-03

Brief Summary

The goal of this clinical trial is to determine if a machine learning/artificial intelligence (AI)-based electrocardiogram (ECG) algorithm (Tempus Next software) 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.

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, we aim 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 the Tempus Next software, which includes AI algorithms for analyzing 12-lead ECGs. Clinicians randomized to the intervention group will automatically receive an ECG with "Risk-Based Assessment for Cardiac Dysfunction" when ordering a 12-lead ECG within EPIC during the study period. If a high-risk result is identified, clinicians will receive an EHR inbox message recommending a follow-up diagnostic test, such as echocardiography and/or ambulatory ECG monitoring.

Outcome Tracking:

A monthly report will track and provide data on:

The proportion of patients with a high-risk result. The proportion of patients receiving the follow-up diagnostic test. The proportion of patients receiving guideline-recommended therapies. This report will be sent to the study participants and clinicians randomized to the intervention group. Clinicians in the usual care group will not receive any communication from the study investigators.

Conditions

Atrial Fibrillation; Cardiovascular Diseases; Arrhythmia; Valvular Disease

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: SCREENING
• Blinding Strategy: NONE

Study Groups

Intervention

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

Group Type: EXPERIMENTAL

TEMPUS AI-enabled ECG-based Screening Tool

Intervention Type: OTHER

The AI-enabled ECG-based screening tool, Tempus Next software, 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.

Control

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

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

TEMPUS AI-enabled ECG-based Screening Tool

The AI-enabled ECG-based screening tool, Tempus Next software, 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.

Intervention Type: OTHER

Eligibility 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

• Minimum Eligible Age: 40 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Northwestern University

OTHER

Sponsor Role: lead

Responsible Party

Sanjiv Shah

Director, Institute for Artificial Intelligence in Medicine - Center for Deep Phenotyping and Precision Therapeutics

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Sanjiv Shah, MD

Role: PRINCIPAL_INVESTIGATOR

Northwestern University

Locations

Northwestern University

Chicago, Illinois, United States

Countries

United States

Facility Contacts

Sanjiv Shah, MD

Role: primary

sanjiv.shah@northwestern.edu

Other Identifiers

STU00220862

Identifier Type: -

Identifier Source: org_study_id