AI-ECG Screening for Left Ventricular Systolic Dysfunction

NCT ID: NCT06231797

Last Updated: 2024-02-05

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

1530 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-02-01

Study Completion Date

2025-07-10

Brief Summary

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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.

Detailed Description

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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.

Conditions

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Left Ventricular Systolic Dysfunction

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Interventions

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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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Seoul National University Hospital

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Seung-Pyo Lee, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Seoul National University Hospital

Central Contacts

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Hak Seung Lee, MD

Role: CONTACT

+1-771-216-0764

References

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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.

Reference Type BACKGROUND
PMID: 35328207 (View on PubMed)

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.

Reference Type RESULT
PMID: 31074221 (View on PubMed)

Other Identifiers

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H-2306-083-1439

Identifier Type: -

Identifier Source: org_study_id

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