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.

Imbalance between HNE and PI3 levels in ARDS patients was associated with ARDS mortality. By combining these biomarkers with Berlin categories and APACHE II, prognostic power of ARDS was greatly improved. Circulation levels of HNE and PI3 may have the potential to predict ARDS mortality and better inform clinicians about ARDS mortality risk.

Perioperative administration of dexmedetomidine to paediatric patients undergoing cardiac surgery may shorten the duration of mechanical ventilation, LOS in the intensive care unit and in the hospital and reduce the incidence of junctional ectopic tachycardia. More high-quality randomized controlled trials are encouraged to verify the beneficial effect of dexmedetomidine before its clinical application in paediatric patients undergoing surgery for congenital heart disease.