Abstract
BackgroundIn-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.MethodsWe 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 delayed occurrence of cardiac arrest and unexpected intensive care unit transfer over 14 days.ResultsWe used 32,294 ECGs of 10,461 patients and 4483 ECGs of 4483 patients from a hospital were used as development and internal validation data, respectively. Additionally, 10,728 ECGs of 10,728 patients from another hospital were used as external validation data, which confirmed the robustness of the developed DLA. During internal and external validation, the areas under the receiver operating characteristic curves of the DLA in predicting cardiac arrest within 24 h were 0.913 and 0.948, respectively. The high risk group of the DLA showed a significantly higher hazard for delayed cardiac arrest (5.74% vs. 0.33%, P < 0.001) and unexpected intensive care unit transfer (4.23% vs. 0.82%, P < 0.001). A sensitivity map of the DLA displayed the ECG regions used to predict cardiac arrest, with the DLA focused most on the QRS complex.ConclusionsOur DLA successfully predicted cardiac arrest using diverse formats of ECG. The results indicate that cardiac arrest could be screened and predicted not only with a conventional 12-lead ECG, but also with a single-lead ECG using a wearable device that employs our DLA.
Highlights
Cardiac arrest is a major public health burden and a recent study of in-hospital cardiac arrests in the United States estimates that 292,000 adults suffer cardiac arrestSeveral track and trigger systems (TTSs) using discrete numeric values such as vital signs and laboratory results are used in rapid response systems (RRSs) [9, 10]
Several track and trigger systems (TTSs) using discrete numeric values such as vital signs and laboratory results are used in RRSs [9, 10]
Previous studies found QT prolongation, QRS prolongation, fragmented QRS complexes, and early repolarization to be associated with cardiac arrest [16,17,18,19]
Summary
Cardiac arrest is a major public health burden and a recent study of in-hospital cardiac arrests in the United States estimates that 292,000 adults suffer cardiac arrestSeveral track and trigger systems (TTSs) using discrete numeric values such as vital signs and laboratory results are used in RRSs [9, 10]. As conventional TTSs have limitations in detecting deterioration in patients, several researchers have adopted deep learning based algorithms to deal with these numeric values, which performed better than conventional tools [11,12,13,14,15]. A paradigm shift is needed to use a new type of variable to improve the performance of predicting cardiac arrest. We hypothesized that DLAs could effectively predict cardiac arrests. To test this hypothesis, we developed and validated a DLA for predicting cardiac arrest using ECGs. In-hospital cardiac arrest is a major burden in health care. We hypothesized that a deeplearning-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
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