The SMART-LV Pilot Study

Yale University10 enrolled

Overview

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.

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 Type

INTERVENTIONAL

Allocation

Purpose

DEVICE_FEASIBILITY

Masking

NONE

Enrollment

Conditions

Left Ventricular Systolic Dysfunction

Interventions

AI-ECGDEVICE

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.

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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

Locations (1)

Yale New Haven Hospital

New Haven, Connecticut, United States

Outcomes

Primary Outcomes

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.

Time frame: During study visit approximately 50 minutes

Data from ClinicalTrials.gov

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