TY - JOUR
T1 - Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients
AU - Goto, Shinichi
AU - Kimura, Mai
AU - Katsumata, Yoshinori
AU - Goto, Shinya
AU - Kamatani, Takashi
AU - Ichihara, Genki
AU - Ko, Seien
AU - Sasaki, Junichi
AU - Fukuda, Keiichi
AU - Sano, Motoaki
N1 - Publisher Copyright:
Copyright: © 2019 Goto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2019/1
Y1 - 2019/1
N2 - 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.
AB - 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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U2 - 10.1371/journal.pone.0210103
DO - 10.1371/journal.pone.0210103
M3 - Article
C2 - 30625197
AN - SCOPUS:85059797493
SN - 1932-6203
VL - 14
JO - PLoS One
JF - PLoS One
IS - 1
M1 - 0210103
ER -