The SMART-LV Pilot Study

Left Ventricular Systolic Dysfunction Clinical Trial

Official title:

Pilot Evaluation for SMartphone-adaptable Artificial Intelligence for PRediction and DeTection of Left Ventricular Systolic Dysfunction

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05630170
• Other study ID #: 2000034006
• Secondary ID: No NIH funding
• Status: Completed
• Phase: N/A
• Start date: September 13, 2023
• Est. completion date: May 22, 2024

Study information

• Verified date: May 2024
• Source: Yale University
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The goal of this pilot study is to evaluate the prospective performance of an image-based, smartphone-adaptable artificial intelligence electrocardiogram (AI-ECG) strategy to predict and detect left ventricular systolic dysfunction (LVSD) in a real-world setting.

Description:

The SMART-LV pilot study will be a prospective cohort study in outpatient clinics at the Yale New Haven Hospital. Participants who have undergone a 12-lead electrocardiogram (ECGs) with either a high (≥80%) or low (<10%) probability of LVSD on AI-ECG algorithm, but without an echocardiogram done in the clinical setting for at least 90 days after the ECG, will be identified by electronic health record (EHR) and invited for a limited echocardiogram/cardiac ultrasonogram for assessing LV ejection fraction. The goal of the study is to evaluate the feasibility of recruiting patients and performing the study after pursuing a screening on 12-lead ECGs. The procedure currently used for detection of LVSD, echocardiograms, are inaccessible and expensive. Therefore, while AI-ECG-based algorithms using a smartphone- or web-based application can broaden access to screening, a thorough evaluation for this indication is needed before clinical adoption. The investigators intend to use the results as pilot data for sample size and drop-off rate estimation for a subsequent larger prospective cohort study aimed at validating the performance characteristics of the model in a screening setting.

The validation of this accessible ECG-based screening strategy, that can be directly used by clinicians using a smartphone or web-based application, can transform the early identification of LVSD before the development of symptoms, thereby allowing broader utilization of evidence-based therapies to prevent symptomatic heart failure and premature death.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 10
• Est. completion date: May 22, 2024
• Est. primary completion date: May 21, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Provision of signed and dated informed consent form.

• Stated willingness to comply with all study procedures and availability for the duration of the study

Exclusion Criteria:

• Patients who have undergone a prior echocardiogram.

• Patients with a prior diagnosis of left ventricular dysfunction, based on a documented low ejection fraction (EF) in the medical record.

• Patients with an intermediate predicted probability of low EF (10 to 80%)

• Patients with a prior diagnosis of heart failure as determined by International Classification of Diseases-10 diagnosis code for heart failure.

• Research opt-out patients

Related Conditions & MeSH terms

• Left Ventricular Systolic Dysfunction
• Systolic Murmurs
• Ventricular Dysfunction, Left

Intervention

Device:
• AI-ECG
A novel AI-ECG model developed at the Cardiovascular Data Science (CarDS) lab will be used as Software as Medical Device (SaMD) on ECG images for detection of LVSD.

Locations

Country	Name	City	State
United States	Yale New Haven Hospital	New Haven	Connecticut

Sponsors (1)

Lead Sponsor	Collaborator
Yale University

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Successful detection of asymptomatic LVSD by AI-ECG	Device feasibility of AI-ECG will be evaluated by comparing the proportion of patients with LVSD on echocardiography among those with a high predicted probability of LVSD on an AI-ECG screen compared with the proportion of patients with LVSD on echocardiography in those with a negative AI-ECG screen. Higher proportions indicate successful detection of asymptomatic LVSD compared with routine clinical care.	During study visit approximately 50 minutes