The SMART-LV Pilot Study

Sponsor

Yale University (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT05630170

Collaborator

(none)

Enrollment

Location

Arm

Anticipated Duration (Months)

Patients Per Site Per Month

Study Details

Study Description

Brief 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.

Condition or Disease	Intervention/Treatment	Phase
Left Ventricular Systolic Dysfunction	Device: AI-ECG	N/A

Detailed 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.

Study Design

Study Type:

Interventional

Anticipated Enrollment :

20 participants

Allocation:

N/A

Intervention Model:

Single Group Assignment

Intervention Model Description:

In the ECG repository of Yale New Haven Hospital, all patients undergoing a 12-lead screen in an outpatient setting, from whom 20 individuals, 10 each with high and low predicted probability of LVSD, will be invited for a limited echocardiogram to definitively evaluate for LVSD. The investigators will assess whether the AI-ECG model continues to have the reported discrimination and sensitivity of >90% for LVSD diagnosis in a screening setting in outpatient routine clinical care.In the ECG repository of Yale New Haven Hospital, all patients undergoing a 12-lead screen in an outpatient setting, from whom 20 individuals, 10 each with high and low predicted probability of LVSD, will be invited for a limited echocardiogram to definitively evaluate for LVSD. The investigators will assess whether the AI-ECG model continues to have the reported discrimination and sensitivity of >90% for LVSD diagnosis in a screening setting in outpatient routine clinical care.

Masking:

None (Open Label)

Primary Purpose:

Device Feasibility

Official Title:

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

Anticipated Study Start Date :

Jan 25, 2023

Anticipated Primary Completion Date :

Apr 25, 2023

Anticipated Study Completion Date :

Jun 25, 2023

Arms and Interventions

Arm	Intervention/Treatment
Experimental: 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.The AI-ECG model will be used on all participants undergoing a 12-lead ECG.	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.

Arm

Intervention/Treatment

Experimental: 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.The AI-ECG model will be used on all participants undergoing a 12-lead ECG.

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.

Outcome Measures

Primary Outcome Measures

Successful detection of asymptomatic LVSD by AI-ECG [During study visit approximately 50 minutes]
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.

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

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

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Yale New Haven Hospital	New Haven	Connecticut	United States	06520

Sponsors and Collaborators

Yale University

Investigators

Principal Investigator: Rohan Khera, MD, MS, Yale University

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

Responsible Party:

Yale University

ClinicalTrials.gov Identifier:

NCT05630170

Other Study ID Numbers:

2000034006

First Posted:

Nov 29, 2022

Last Update Posted:

Jan 25, 2023

Last Verified:

Jan 1, 2023

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Additional relevant MeSH terms:

Ventricular Dysfunction, Left

Systolic Murmurs

Study Results

No Results Posted as of Jan 25, 2023