• 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, and Tong-Wen Sun.
• Department of General ICU, The First Affiliated Hospital of Zhengzhou University, Henan Key Laboratory of Critical Care Medicine, 1 Jianshe East Road, Zhengzhou, 450052, China.
• J Transl Med. 2019 Oct 1; 17 (1): 326.

BackgroundTo 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.MethodsA 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.ResultsAll 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.ConclusionsThis 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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