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Journal of Translational Medicine
Published in: Journal of Translational Medicine 1/2019

Open Access 01-12-2019 | Acute Respiratory Distress-Syndrome | Research

Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study

Authors: Xian-Fei Ding, Jin-Bo Li, Huo-Yan Liang, Zong-Yu Wang, Ting-Ting Jiao, Zhuang Liu, Liang Yi, Wei-Shuai Bian, Shu-Peng Wang, Xi Zhu, Tong-Wen Sun

Published in: Journal of Translational Medicine | Issue 1/2019

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Abstract

Background

To develop a machine learning model for predicting acute respiratory distress syndrome (ARDS) events through commonly available parameters, including baseline characteristics and clinical and laboratory parameters.

Methods

A secondary analysis of a multi-centre prospective observational cohort study from five hospitals in Beijing, China, was conducted from January 1, 2011, to August 31, 2014. A total of 296 patients at risk for developing ARDS admitted to medical intensive care units (ICUs) were included. We applied a random forest approach to identify the best set of predictors out of 42 variables measured on day 1 of admission.

Results

All patients were randomly divided into training (80%) and testing (20%) sets. Additionally, these patients were followed daily and assessed according to the Berlin definition. The model obtained an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.82 and yielded a predictive accuracy of 83%. For the first time, four new biomarkers were included in the model: decreased minimum haematocrit, glucose, and sodium and increased minimum white blood cell (WBC) count.

Conclusions

This newly established machine learning-based model shows good predictive ability in Chinese patients with ARDS. External validation studies are necessary to confirm the generalisability of our approach across populations and treatment practices.
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Metadata
Title
Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study
Authors
Xian-Fei Ding
Jin-Bo Li
Huo-Yan Liang
Zong-Yu Wang
Ting-Ting Jiao
Zhuang Liu
Liang Yi
Wei-Shuai Bian
Shu-Peng Wang
Xi Zhu
Tong-Wen Sun
Publication date
01-12-2019
Publisher
BioMed Central
Published in
Journal of Translational Medicine / Issue 1/2019
Electronic ISSN: 1479-5876
DOI
https://doi.org/10.1186/s12967-019-2075-0

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