AI ECG Algorithm for Detecting LV Systolic Dysfunction
Condition(s) studied
Investigational drug(s) / intervention(s)
None-placebo: There is no intervention group
Study summary
This prospective observational cohort study aims to evaluate the clinical performance of a deep learning-based electrocardiography (ECG) algorithm (DeepECG LVSD) for detecting left ventricular systolic dysfunction (LVSD), defined as left ventricular ejection fraction (LVEF) ≤40%, using transthoracic echocardiography as the reference standard. Approximately 15,000 adult patients undergoing both ECG and echocardiography within 30 days at Ajou University Hospital will be enrolled. Diagnostic performance will be assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Secondary analyses will evaluate the association between AI-predicted LVSD and 30-day clinical outcomes, including all-cause mortality, emergency department visits, and heart failure rehospitalization.
Eligibility
Primary outcome measure(s)
- AUROC for detection of LVSD (LVEF ≤40%) — During procedure
Diagnostic performance including AUROC, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.
Trial sites (1)
| Facility | City | Region | Status |
|---|---|---|---|
| Ajou University School of Medicine | Suwon | Gyeonggi-do | Recruiting |
More Ajou University School of Medicine trials in South Korea
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
View NCT07636759 on ClinicalTrials.gov ↗ ← All trials in South Korea