AI-Enhanced Detection for Aortic Stenosis

Recruiting at 2 trial locations

Overseen By Rohan Khera, MD, MS

Age: 65+

Sex: Any

Trial Phase: Academic

Sponsor: Yale University

Disqualifiers: Echocardiogram, Aortic valve replacement, Dementia, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 1

410 Participants Needed

Trial Summary

Do I need to stop my current medications for the trial?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the treatment AI-ECG risk algorithm, AI-POCUS for detecting aortic stenosis?

Research shows that artificial intelligence-enabled electrocardiograms (AI-ECG) and deep learning algorithms can accurately detect significant aortic stenosis (AS), with high accuracy rates in both internal and external validations. These tools use advanced techniques to identify AS early, which is crucial for better patient outcomes. ^{1 2 3 4 5}

How is the AI-ECG risk algorithm and AI-POCUS treatment for aortic stenosis different from other treatments?

The AI-ECG risk algorithm and AI-POCUS treatment for aortic stenosis is unique because it uses artificial intelligence to enhance early detection of the condition through electrocardiograms (ECGs) and echocardiography, potentially identifying patients before symptoms appear, which is not typically possible with standard screening tools. ^{1 2 3 4 6}

What is the purpose of this trial?

The DETECT-AS Diagnostic Study will assess the performance of artificial intelligence (AI) risk predictions to detect aortic stenosis using results from portable electrocardiogram (ECG) and cardiac ultrasound devices.

Research Team

Rohan Khera, MD, MS

Principal Investigator

Yale University

Eligibility Criteria

This trial is for individuals aged 70 or older attending routine outpatient primary care clinics at specific sites. It's not for those with prior aortic valve replacement/repair, implantable cardiac devices, moderate/severe AS history, recent echocardiograms, heart transplants, non-English speakers, dementia or less than a year to live.

Inclusion Criteria

Attending a routine outpatient primary care clinic at one of the three enrollment sites

I am 70 years old or older.

Exclusion Criteria

I have had surgery or a procedure to fix or replace my aortic valve.

Presence of implantable cardiac devices, including permanent cardiac pacer, implantable cardioverter-defibrillator, or left ventricular assist device

Prior history of moderate or severe AS

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Diagnostic Assessment

Participants undergo sequential screening for aortic stenosis using portable 1-lead ECGs, followed by point-of-care ultrasound (POCUS) if indicated by AI-based risk algorithms

4-6 weeks

1-2 visits (in-person)

Follow-up

Participants are monitored for diagnosis of advanced aortic stenosis via transthoracic echocardiogram (TTE) and electronic health record (EHR) review

12 months

Treatment Details

Interventions

• AI-ECG risk algorithm
• AI-POCUS

Trial Overview The DETECT-AS Diagnostic Study is testing AI tools that analyze data from portable ECGs and cardiac ultrasounds to predict the risk of aortic stenosis in patients. The study aims to enhance early detection using these advanced technologies.

Participant Groups

Group I: Intervention Experimental Treatment 4 Interventions

The intervention arm will undergo sequential screening for aortic stenosis using portable 1-lead electrocardiograms (ECGs), followed by point-of-care ultrasound (POCUS), if indicated, by artificial intelligence (AI)-based risk algorithms.

Group II: Control Placebo Group 2 Interventions

The control arm will undergo a portable 1-lead electrocardiogram (ECG), with 10% randomly assigned to undergo point-of-care ultrasound (POCUS).

Who Is Running the Clinical Trial?

Yale University

Lead Sponsor

Trials

1,963

Recruited

3,046,000+

The Methodist Hospital Research Institute

Collaborator

Trials

299

Recruited

82,500+

Icahn School of Medicine at Mount Sinai

Collaborator

Trials

933

Recruited

579,000+

Kaiser Permanente School of Medicine

Collaborator

Trials

4

Recruited

1,300+

National Institute on Aging (NIA)

Collaborator

Trials

1,841

Recruited

28,150,000+

References

1. England pubmed.ncbi.nlm.nih.gov

Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography. [2021]

2. England pubmed.ncbi.nlm.nih.gov

Correlation between artificial intelligence-enabled electrocardiogram and echocardiographic features in aortic stenosis. [2023]

3. England pubmed.ncbi.nlm.nih.gov

Enhanced detection of severe aortic stenosis via artificial intelligence: a clinical cohort study. [2023]

4. England pubmed.ncbi.nlm.nih.gov

Electrocardiogram screening for aortic valve stenosis using artificial intelligence. [2021]

5. England pubmed.ncbi.nlm.nih.gov

Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score. [2022]

6. United States pubmed.ncbi.nlm.nih.gov

Fully Automated Artificial Intelligence Assessment of Aortic Stenosis by Echocardiography. [2023]