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

Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE

Original Publication: San Francisco, CA : Public Library of Science

Myocardial Revascularization* ; Neural Networks, Computer*; Acute Coronary Syndrome/*diagnosis ; Chest Pain/*diagnosis ; Diagnosis, Computer-Assisted/*methods ; Electrocardiography/*methods; Acute Coronary Syndrome/complications ; Acute Coronary Syndrome/surgery ; Chest Pain/etiology ; Datasets as Topic ; Decision Making, Computer-Assisted ; Diagnosis, Differential ; Emergency Service, Hospital ; Humans ; Patient Selection ; Predictive Value of Tests ; Prognosis ; Sensitivity and Specificity ; Time Factors

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.
Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: SG (first author) received a grant from Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research Grant Number 18K15859. Shinya Goto acknowledge the financial support from MEXT/JSPS KAKENHI 17K19669 and partly by 18H01726. Shinya Goto acknowledge the financial support from Bristol-Myers Squibb from their independent research support project (33999603). Shinya Goto received research funding from Sanofi, Pfizer, and Ono. Shinya Goto received a modest personal fee from Bayer. Shinya Goto is an associated Editor for Circulation, an associate Editor for Journal of Biorheology, an associate Editor for Archives of Medical Science, section Editor for Thrombosis and Haemostasis.

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Date Created: 20190110 Date Completed: 20190927 Latest Revision: 20200309

20240829

PMC6326503

10.1371/journal.pone.0210103

30625197