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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 pr...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7541213/ https://www.ncbi.nlm.nih.gov/pubmed/33023615 http://dx.doi.org/10.1186/s13049-020-00791-0 |
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author | Kwon, Joon-myoung Kim, Kyung-Hee Jeon, Ki-Hyun Lee, Soo Youn Park, Jinsik Oh, Byung-Hee |
author_facet | Kwon, Joon-myoung Kim, Kyung-Hee Jeon, Ki-Hyun Lee, Soo Youn Park, Jinsik Oh, Byung-Hee |
author_sort | Kwon, Joon-myoung |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7541213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75412132020-10-08 Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography Kwon, Joon-myoung Kim, Kyung-Hee Jeon, Ki-Hyun Lee, Soo Youn Park, Jinsik Oh, Byung-Hee Scand J Trauma Resusc Emerg Med Original Research 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. BioMed Central 2020-10-06 /pmc/articles/PMC7541213/ /pubmed/33023615 http://dx.doi.org/10.1186/s13049-020-00791-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Original Research Kwon, Joon-myoung Kim, Kyung-Hee Jeon, Ki-Hyun Lee, Soo Youn Park, Jinsik Oh, Byung-Hee Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography |
title | Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography |
title_full | Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography |
title_fullStr | Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography |
title_full_unstemmed | Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography |
title_short | Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography |
title_sort | artificial intelligence algorithm for predicting cardiac arrest using electrocardiography |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7541213/ https://www.ncbi.nlm.nih.gov/pubmed/33023615 http://dx.doi.org/10.1186/s13049-020-00791-0 |
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