AI-ECG Screening for Left Ventricular Systolic Dysfunction
- Conditions
- Left Ventricular Systolic Dysfunction
- Registration Number
- NCT06231797
- Lead Sponsor
- Seoul National University Hospital
- 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.
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 1530
- 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
- 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
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Area under the receiver operating characteristic curve (AUROC) Through study completion, an average of 1 year 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.
- Secondary Outcome Measures
Name Time Method Sensitivity Through study completion, an average of 1 year AI model sensitivity detecting LVSD
Positive predictive value Through study completion, an average of 1 year Positive predictive value in the recruited patient population
Negative predictive value Through study completion, an average of 1 year Negative predictive value in the recruited patient population
Specificity Through study completion, an average of 1 year AI model sensitivity detecting patients with normal left ventricular systolic function