The SMART-LV Pilot Study

Yale University

Status: Completed

Conditions: Left Ventricular Systolic Dysfunction

Treatments: Device: AI-ECG

Study type: Interventional

Funder types: Other

Identifiers

NCT05630170

No NIH funding (Other Identifier)

2000034006

Details and patient eligibility

About

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.

Full 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.

Enrollment: 10 patients

Sex: All

Ages: 18+ years old

Volunteers: No 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

Trial design

Primary purpose: Device Feasibility

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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10 participants in 1 patient group

AI-ECG

Experimental group

Description:

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.

Treatment:

Device: AI-ECG