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NCT06231797 Clinical Trial

AI-ECG Screening for Left Ventricular Systolic Dysfunction

Left Ventricular Systolic Dysfunction Clinical Trial

Official title:
AI-ECG Screening for Left Ventricular Systolic Dysfunction: A Prospective, Observational, Multicenter Study

Clinical Trial Details — Status: Not yet recruiting

Administrative data
NCT number NCT06231797
Other study ID # H-2306-083-1439
Secondary ID
Status Not yet recruiting
Phase
First received
Last updated
Start date February 1, 2024
Est. completion date July 10, 2025

Study information
Verified date October 2023
Source Seoul National University Hospital
Contact Hak Seung Lee, MD
Phone +1-771-216-0764
Email cardiolee@gmail.com
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.

Description:

The current investigators have developed an artificial intelligence (AI) algorithm based on 12-lead electrocardiogram (ECG) detecting left ventricular systolic dysfunction, through 364,845 ECGs from 148,547 patients. Then, when the model was tested retrospectively on 59,805 ECGs of 24,376 patients, the model performance expressed as an area under the receiver operating characteristic curve was 0.889 (95% CI 0.887-0.891). The investigators are planning to prospectively validate the model's effectiveness as a potential screening tool for left ventricular systolic dysfunction.

Recruitment information / eligibility
Status Not yet recruiting
Enrollment 1530
Est. completion date July 10, 2025
Est. primary completion date July 10, 2024
Accepts healthy volunteers
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - 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 Exclusion Criteria: - 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

Study Design

Related Conditions & MeSH terms

Intervention

Diagnostic Test:
• AI algorithm conducted on 12-lead ECG and transthoracic echocardiography
12-lead ECG is performed for each patient. For 12-lead ECG, AITIALVSD (AI algorithm) analysis will be performed through a separate server.

Locations
Country Name City State
n/a

Sponsors (1)
Lead Sponsor Collaborator
Seoul National University Hospital

References & 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. — View Citation

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.20 — View Citation

Outcome
Type Measure Description Time frame Safety issue
Primary 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 Sensitivity AI model sensitivity detecting LVSD Through study completion, an average of 1 year
Secondary Specificity AI model sensitivity detecting patients with normal left ventricular systolic function Through study completion, an average of 1 year
Secondary Positive predictive value Positive predictive value in the recruited patient population Through study completion, an average of 1 year
Secondary Negative predictive value Negative predictive value in the recruited patient population Through study completion, an average of 1 year
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