Clinical trial NCT06231797: AI-ECG Screening for Left Ventricular Systolic Dysfunction
Official title: AI-ECG Screening for Left Ventricular Systolic Dysfunction: A Prospective, Observational, Multicenter Study
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Left Ventricular Systolic Dysfunction
Lead sponsor: Seoul National University Hospital
Sponsor class: OTHER
First posted: 2024-01-30
Enrollment: 1530
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients undergoing 12-lead ECG and transthoracic echocardiography in routine clinical practice

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.

Detailed 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.

Eligibility criteria: 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

Primary outcomes:
measure: Area under the receiver operating characteristic curve (AUROC)
timeFrame: Through study completion, an average of 1 year
Secondary outcomes:
measure: Sensitivity
timeFrame: Through study completion, an average of 1 year
measure: Specificity
timeFrame: Through study completion, an average of 1 year
measure: Positive predictive value
timeFrame: Through study completion, an average of 1 year
measure: Negative predictive value
timeFrame: Through study completion, an average of 1 year
Other outcomes:
Locations:
Study officials:
affiliation: Seoul National University Hospital
name: Seung-Pyo Lee, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
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.2018.0446. Epub 2019 Mar 21.