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  4. Machine learning to predict in-hospital cardiac arrest from patients presenting to the emergency department
 
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Machine learning to predict in-hospital cardiac arrest from patients presenting to the emergency department

Journal
Internal and emergency medicine
Date Issued
2022-11-06
Author(s)
TSUNG-CHIEN LU  
CHIH-HUNG WANG  
Chou, Fan-Ya
Sun, Jen-Tang
Chou, Eric H
EDWARD PEI-CHUAN HUANG  
CHU-LIN TSAI  
MATTHEW HUEI-MING MA  
CHENG-CHUNG FANG  
CHIEN-HUA HUANG  
DOI
10.1007/s11739-022-03143-1
URI
https://scholars.lib.ntu.edu.tw/handle/123456789/626887
URL
https://api.elsevier.com/content/abstract/scopus_id/85141375005
Abstract
In-hospital cardiac arrest (IHCA) in the emergency department (ED) is not uncommon but often fatal. Using the machine learning (ML) approach, we sought to predict ED-based IHCA (EDCA) in patients presenting to the ED based on triage data. We retrieved 733,398 ED records from a tertiary teaching hospital over a 7 year period (Jan. 1, 2009-Dec. 31, 2015). We included only adult patients (≥ 18 y) and excluded cases presenting as out-of-hospital cardiac arrest. Primary outcome (EDCA) was identified via a resuscitation code. Patient demographics, triage data, and structured chief complaints (CCs), were extracted. Stratified split was used to divide the dataset into the training and testing cohort at a 3-to-1 ratio. Three supervised ML models were trained and performances were evaluated and compared to the National Early Warning Score 2 (NEWS2) and logistic regression (LR) model by the area under the receiver operating characteristic curve (AUC). We included 316,465 adult ED records for analysis. Of them, 636 (0.2%) developed EDCA. Of the constructed ML models, Random Forest outperformed the others with the best AUC result (0.931, 95% CI 0.911-0.949), followed by Gradient Boosting (0.930, 95% CI 0.909-0.948) and Extra Trees classifier (0.915, 95% CI 0.892-0.936). Although the differences between each of ML models and LR (AUC: 0.905, 95% CI 0.882-0.926) were not significant, all constructed ML models performed significantly better than using the NEWS2 scoring system (AUC 0.678, 95% CI 0.635-0.722). Our ML models showed excellent discriminatory performance to identify EDCA based only on the triage information. This ML approach has the potential to reduce unexpected resuscitation events if successfully implemented in the ED information system.
Subjects
Cardiopulmonary resuscitation; Emergency department; In-hospital cardiac arrest; Machine learning
Publisher
SPRINGER-VERLAG ITALIA SRL
Type
journal article

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