Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography

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 pre...

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Veröffentlicht in:Scandinavian journal of trauma, resuscitation and emergency medicine Jg. 28; H. 1; S. 98 - 10
Hauptverfasser: Kwon, Joon-myoung, Kim, Kyung-Hee, Jeon, Ki-Hyun, Lee, Soo Youn, Park, Jinsik, Oh, Byung-Hee
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 06.10.2020
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Age
Aged
Algorithms
Analysis
Artificial intelligence
Cardiac arrest
Datasets
Deep Learning
Electrocardiography
Emergency Medicine
Female
Heart arrest
Heart Arrest - diagnosis
Hospital rapid response team
Hospitalization
Hospitals
Humans
Intensive Care Units
Male
Medicine
Medicine & Public Health
Middle Aged
Open source software
Original Research
Patient Transfer
Patients
Performance evaluation
Retrospective Studies
Traumatic Surgery
United States > US
Heart arrest
Deep learning
Electrocardiography
Hospital rapid response team
Artificial intelligence
ISSN:1757-7241, 1757-7241
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Zusammenfassung: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 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.
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ISSN:1757-7241
1757-7241
DOI:10.1186/s13049-020-00791-0