Mortality risk prediction of the electrocardiogram as an informative indicator of cardiovascular diseases.

ABSTRACT:

Background

The electrocardiogram (ECG) may be the most popular test in the management of cardiovascular disease (CVD). Although wide applications of artificial intelligence (AI)-enabled ECG have been developed, an integrating indicator for CVD risk stratification was not investigated. Since mortality may be the most important global outcome, this study aimed to develop a survival deep learning model (DLM) to establish a critical ECG value and explore the associations with various CVD events.

Methods

We trained a DLM with 451,950 12-lead resting ECGs obtained from 210,552 patients, for whom 23,592 events occurred. The internal validation set included 27,808 patients with one ECG for each patient. The external validations were performed in a community hospital with 33,047 pati

SUBMITTER: Tsai DJ 

PROVIDER: S-EPMC10336769 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature