Enhanced prediction of left ventricular ejection fraction using electrocardiography with the addition of clinical metadata.

ABSTRACT:

Background/aims

Left ventricular ejection fraction (LVEF) is a key echocardiographic parameter for assessing LV systolic function, guiding the management of many cardiovascular diseases, including heart failure (HF). While traditional electrocardiography (ECG) has been widely used in clinical practice, it has limitations in predicting LVEF. This study investigated the impact of integrating ECG data with metadata, such as age, N-terminal pro B-type natriuretic peptide (NT-proBNP), and sodium levels, to enhance the accuracy of LVEF prediction, especially in HF with reduced ejection fraction (HFrEF, LVEF ≤ 40%).

Methods

This retrospective study analyzed ECG and metadata from two tertiary teaching hospitals in Korea. A deep neural network (EfficientNet B3) was trained to predict

SUBMITTER: Park HW 

PROVIDER: S-EPMC12800464 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature