Journal Article

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

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

2019

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Acute respiratory distress syndrome / Artificial intelligence / Biological markers / Biomarkers / Biomedical and Life Sciences / Biomedicine / Bone marrow / Cohort analysis / Critical care / Critical Care and Anesthesia / Health aspects / Hematocrit / Hospitals / Identification and classification / Intensive care / Intensive care units / Learning algorithms / Machine learning / Medicine/Public Health / Pain / Patients / Prediction models / Predictive model / Respiratory distress syndrome / Risk factors / Sodium / Transplants & implants / Tumor necrosis factor-TNF