Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography.

ABSTRACT:

Background

In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have unsatisfactory performances. We hypothesized that a deep-learning-based artificial intelligence algorithm (DLA) could effectively predict cardiac arrest using electrocardiography (ECG). We developed and validated a DLA for predicting cardiac arrest using ECG.

Methods

We conducted a retrospective study that included 47,505 ECGs of 25,672 adult patients admitted to two hospitals, who underwent at least one ECG from October 2016 to September 2019. The endpoint was occurrence of cardiac arrest within 24 h from ECG. Using subgroup analyses in patients who were initially classified as non-event, we confirmed the dela

SUBMITTER: Kwon JM 

PROVIDER: S-EPMC7541213 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature