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Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients

BACKGROUND: Patient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room with chest symptoms is not fully established. Electrocardiogram is an eas...

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Autores principales: Goto, Shinichi, Kimura, Mai, Katsumata, Yoshinori, Goto, Shinya, Kamatani, Takashi, Ichihara, Genki, Ko, Seien, Sasaki, Junichi, Fukuda, Keiichi, Sano, Motoaki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326503/
https://www.ncbi.nlm.nih.gov/pubmed/30625197
http://dx.doi.org/10.1371/journal.pone.0210103
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author Goto, Shinichi
Kimura, Mai
Katsumata, Yoshinori
Goto, Shinya
Kamatani, Takashi
Ichihara, Genki
Ko, Seien
Sasaki, Junichi
Fukuda, Keiichi
Sano, Motoaki
author_facet Goto, Shinichi
Kimura, Mai
Katsumata, Yoshinori
Goto, Shinya
Kamatani, Takashi
Ichihara, Genki
Ko, Seien
Sasaki, Junichi
Fukuda, Keiichi
Sano, Motoaki
author_sort Goto, Shinichi
collection PubMed
description BACKGROUND: Patient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room with chest symptoms is not fully established. Electrocardiogram is an easy and rapid procedure, but may contain crucial information not recognized even by well-trained physicians. OBJECTIVE: To make a prediction model for the needs for urgent revascularization from 12-lead electrocardiogram recorded in the emergency room. METHOD: We developed an artificial intelligence model enabling the detection of hidden information from a 12-lead electrocardiogram recorded in the emergency room. Electrocardiograms obtained from consecutive patients visiting the emergency room at Keio University Hospital from January 2012 to April 2018 with chest discomfort was collected. These data were splitted into validation and derivation dataset with no duplication in each dataset. The artificial intelligence model was constructed to select patients who require urgent revascularization within 48 hours. The model was trained with the derivation dataset and tested using the validation dataset. RESULTS: Of the consecutive 39,619 patients visiting the emergency room with chest discomfort, 362 underwent urgent revascularization. Of them, 249 were included in the derivation dataset and the remaining 113 were included in validation dataset. For the control, 300 were randomly selected as derivation dataset and another 130 patients were randomly selected for validation dataset from the 39,317 who did not undergo urgent revascularization. On validation, our artificial intelligence model had predictive value of the c-statistics 0.88 (95% CI 0.84–0.93) for detecting patients who required urgent revascularization. CONCLUSIONS: Our artificial intelligence model provides information to select patients who need urgent revascularization from only 12-leads electrocardiogram in those visiting the emergency room with chest discomfort.
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spelling pubmed-63265032019-01-18 Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients Goto, Shinichi Kimura, Mai Katsumata, Yoshinori Goto, Shinya Kamatani, Takashi Ichihara, Genki Ko, Seien Sasaki, Junichi Fukuda, Keiichi Sano, Motoaki PLoS One Research Article BACKGROUND: Patient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room with chest symptoms is not fully established. Electrocardiogram is an easy and rapid procedure, but may contain crucial information not recognized even by well-trained physicians. OBJECTIVE: To make a prediction model for the needs for urgent revascularization from 12-lead electrocardiogram recorded in the emergency room. METHOD: We developed an artificial intelligence model enabling the detection of hidden information from a 12-lead electrocardiogram recorded in the emergency room. Electrocardiograms obtained from consecutive patients visiting the emergency room at Keio University Hospital from January 2012 to April 2018 with chest discomfort was collected. These data were splitted into validation and derivation dataset with no duplication in each dataset. The artificial intelligence model was constructed to select patients who require urgent revascularization within 48 hours. The model was trained with the derivation dataset and tested using the validation dataset. RESULTS: Of the consecutive 39,619 patients visiting the emergency room with chest discomfort, 362 underwent urgent revascularization. Of them, 249 were included in the derivation dataset and the remaining 113 were included in validation dataset. For the control, 300 were randomly selected as derivation dataset and another 130 patients were randomly selected for validation dataset from the 39,317 who did not undergo urgent revascularization. On validation, our artificial intelligence model had predictive value of the c-statistics 0.88 (95% CI 0.84–0.93) for detecting patients who required urgent revascularization. CONCLUSIONS: Our artificial intelligence model provides information to select patients who need urgent revascularization from only 12-leads electrocardiogram in those visiting the emergency room with chest discomfort. Public Library of Science 2019-01-09 /pmc/articles/PMC6326503/ /pubmed/30625197 http://dx.doi.org/10.1371/journal.pone.0210103 Text en © 2019 Goto et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Goto, Shinichi
Kimura, Mai
Katsumata, Yoshinori
Goto, Shinya
Kamatani, Takashi
Ichihara, Genki
Ko, Seien
Sasaki, Junichi
Fukuda, Keiichi
Sano, Motoaki
Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients
title Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients
title_full Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients
title_fullStr Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients
title_full_unstemmed Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients
title_short Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients
title_sort artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326503/
https://www.ncbi.nlm.nih.gov/pubmed/30625197
http://dx.doi.org/10.1371/journal.pone.0210103
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