Left Ventricular Systolic Dysfunction
Conditions
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.
Interventions
DIAGNOSTIC_TESTAI 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.
Sponsors
Seoul National University Hospital
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity | Through study completion, an average of 1 year | AI model sensitivity detecting LVSD |
| Specificity | Through study completion, an average of 1 year | AI model sensitivity detecting patients with normal left ventricular systolic function |
| 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 |
Contacts
Primary ContactHak Seung Lee, MD
Outcome results
None listed