AI-ECG Screening for Left Ventricular Systolic Dysfunction

NCT06231797 | Unknown

• 1 other identifier: H-2306-083-1439
• Study Type: observational
• Target: 1,530
• Locations: 0 countries
• Sites: N/A

Brief Summary

The purpose of the current study is to verify the effectiveness of the artificial intelligence algorithm applied to the electrocardiogram as a potential screening tool for left ventricular systolic dysfunction.

Conditions

Left Ventricular Systolic Dysfunction

Outcome Measures

Primary Outcomes (1)

• Area under the receiver operating characteristic curve (AUROC)

  AI model performance detecting LVSD, expressed as an AUROC. As a diagnostic assistance for LVSD, an ROC curve expressed as sensitivity to (1-specificity) will be presented, and the accuracy of prediction will be confirmed by calculating the AUROC, which is the area below.

  Through study completion, an average of 1 year

Secondary Outcomes (4)

• Sensitivity

  Through study completion, an average of 1 year

• Specificity

  Through study completion, an average of 1 year

• Positive predictive value

  Through study completion, an average of 1 year

• Negative predictive value

  Through study completion, an average of 1 year

Interventions

AI algorithm conducted on 12-lead ECG and transthoracic echocardiography DIAGNOSTIC_TEST

12-lead ECG is performed for each patient. For 12-lead ECG, AITIALVSD (AI algorithm) analysis will be performed through a separate server.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients undergoing 12-lead ECG and transthoracic echocardiography in routine clinical practice

You may qualify if:

• Individuals or those whose legal representative agree to participate in the study, and sign the consent form
• Can complete both 12-lead electrocardiogram and transthoracic echocardiography

You may not qualify if:

• Individuals whose age is less than 18 year-old.
• Individuals who do not agree to participate in the study
• Patients who are unable to participate in clinical trials at the discretion of the investigator

Sponsors & Collaborators

• Seoul National University Hospital: lead

Related Publications (2)

• Kwon JM, Jo YY, Lee SY, Kang S, Lim SY, Lee MS, Kim KH. Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG. Diagnostics (Basel). 2022 Mar 8;12(3):654. doi: 10.3390/diagnostics12030654.

  PMID: 35328207 BACKGROUND

• Kwon JM, Kim KH, Jeon KH, Kim HM, Kim MJ, Lim SM, Song PS, Park J, Choi RK, Oh BH. Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification. Korean Circ J. 2019 Jul;49(7):629-639. doi: 10.4070/kcj.2018.0446. Epub 2019 Mar 21.

  PMID: 31074221 RESULT

MeSH Terms

Conditions

Ventricular Dysfunction, Left

Interventions

Echocardiography

Condition Hierarchy (Ancestors)

Ventricular Dysfunction; Heart Diseases; Cardiovascular Diseases

Intervention Hierarchy (Ancestors)

Cardiac Imaging Techniques; Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis; Ultrasonography; Heart Function Tests; Diagnostic Techniques, Cardiovascular

Study Officials

• Seung-Pyo Lee, MD, PhD

  Seoul National University Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Hak Seung Lee, MD

CONTACT

+1-771-216-0764; cardiolee@gmail.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 2, 2023
• First Posted: January 30, 2024
• Study Start: February 1, 2024
• Primary Completion: July 10, 2024
• Study Completion: July 10, 2025
• Last Updated: February 5, 2024

Record last verified: 2023-10

Data Sharing

• IPD Sharing: Will not share