Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography
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ORIGINAL RESEARCH
Open Access
Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography Joon-myoung Kwon1,2,3,4* , Kyung-Hee Kim5, Ki-Hyun Jeon2,5, Soo Youn Lee2,5, Jinsik Park3,5 and Byung-Hee Oh5
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 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. 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 delayed occurrence of cardiac arrest and unexpected intensive care unit transfer over 14 days. Results: We 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. Conclusions: Our 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. Keywords: Heart arrest, Deep learning, Electrocardiography, Artificial intelligence, Hospital rapid response team
Introduction 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 arrest * Correspondence: [email protected] 1 Department of Critical Care and Emergency Medicine, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, Republic of Korea 2 Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, South Korea Full list of author information is available at the end of the article
each year [1–3]. The study shows a concerning trend of cardiac arrest in approximately 38% greater than previously data [2, 4]. Although the survival rate of cardi
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