Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective study

The National Early Warning Score (NEWS) is a validated method for predicting clinical deterioration in hospital wards, but its performance in prehospital settings remains controversial. Modern machine learning models may outperform traditional statistical analyses for predicting short-term mortality...

Full description

Saved in:
Bibliographic Details
Published in:Resuscitation plus Vol. 4; p. 100046
Main Authors: Pirneskoski, Jussi, Tamminen, Joonas, Kallonen, Antti, Nurmi, Jouni, Kuisma, Markku, Olkkola, Klaus T., Hoppu, Sanna
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.12.2020
Elsevier
Subjects:
Cardiac arrest prevention
Clinical Paper
Early warning score
Emergency
Emergency medical services
Machine learning
National Early Warning Score
NEWS
Prehospital
Random forest
Early warning score
Cardiac arrest prevention
NEWS
Machine learning
Random forest
National Early Warning Score
Emergency medical services
Prehospital
ISSN:2666-5204, 2666-5204
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The National Early Warning Score (NEWS) is a validated method for predicting clinical deterioration in hospital wards, but its performance in prehospital settings remains controversial. Modern machine learning models may outperform traditional statistical analyses for predicting short-term mortality. Thus, we aimed to compare the mortality prediction accuracy of NEWS and random forest machine learning using prehospital vital signs. In this retrospective study, all electronic ambulance mission reports between 2008 and 2015 in a single EMS system were collected. Adult patients (≥ 18 years) were included in the analysis. Random forest models with and without blood glucose were compared to the traditional NEWS for predicting one-day mortality. A ten-fold cross-validation method was applied to train and validate the random forest models. A total of 26,458 patients were included in the study of whom 278 (1.0%) died within one day of ambulance mission. The area under the receiver operating characteristic curve for one-day mortality was 0.836 (95% CI, 0.810−0.860) for NEWS, 0.858 (95% CI, 0.832−0.883) for a random forest trained with NEWS variables only and 0.868 (0.843−0.892) for a random forest trained with NEWS variables and blood glucose. A random forest algorithm trained with NEWS variables was superior to traditional NEWS for predicting one-day mortality in adult prehospital patients, although the risk of selection bias must be acknowledged. The inclusion of blood glucose in the model further improved its predictive performance.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Equal contribution.
ISSN:2666-5204
2666-5204
DOI:10.1016/j.resplu.2020.100046