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J Transl Med ; 17(1): 326, 2019 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-31570096

RESUMO

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.


Assuntos
Algoritmos , Unidades de Terapia Intensiva , Aprendizado de Máquina , Modelos Teóricos , Síndrome do Desconforto Respiratório/diagnóstico , Idoso , China , Estudos de Coortes , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Curva ROC
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