NDLI: Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography

Content Provider	Springer Nature : BioMed Central
Author	Kwon, Joon-myoung Kim, Kyung-Hee Jeon, Ki-Hyun Lee, Soo Youn Park, Jinsik Oh, Byung-Hee
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 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.
Related Links	https://sjtrem.biomedcentral.com/counter/pdf/10.1186/s13049-020-00791-0.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	17577241
DOI	10.1186/s13049-020-00791-0
Journal	Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine
Issue Number	1
Volume Number	28
Language	English
Publisher	BioMed Central
Publisher Date	2020-10-06
Access Restriction	Open
Subject Keyword	Scandinavian Journal of Trauma Resuscitation and Emergency Medicine Emergency Medicine Traumatic Surgery Heart arrest Deep learning Electrocardiography Artificial intelligence Hospital rapid response team
Content Type	Text
Resource Type	Article
Subject	Emergency Medicine Critical Care and Intensive Care Medicine
Journal Impact Factor	3/2023
5-Year Journal Impact Factor	3.5/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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