The SMART-LV Pilot Study

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

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.

Study Overview

• Status: Completed
• Conditions: Left Ventricular Systolic Dysfunction
• Intervention / Treatment: Device: AI-ECG

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 Type: Interventional
• Enrollment (Actual): 10
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Connecticut
    • New Haven, Connecticut, United States, 06520
      • Yale New Haven Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Device Feasibility
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Yale University
• Investigators: Principal Investigator: Rohan Khera, MD, MS, Yale University

Study record dates

Study Major Dates

• Study Start (Actual): September 13, 2023
• Primary Completion (Actual): May 21, 2024
• Study Completion (Actual): May 22, 2024

Study Registration Dates

• First Submitted: November 17, 2022
• First Submitted That Met QC Criteria: November 17, 2022
• First Posted (Actual): November 29, 2022

Study Record Updates

• Last Update Posted (Actual): May 24, 2024
• Last Update Submitted That Met QC Criteria: May 23, 2024
• Last Verified: May 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Heart Diseases; Cardiovascular Diseases; Heart Murmurs; Ventricular Dysfunction; Systolic Murmurs; Ventricular Dysfunction, Left
• Other Study ID Numbers: 2000034006; No NIH funding (Other Identifier: 10.18.23)

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No