Novel machine learning models for the prediction of acute respiratory distress syndrome after liver transplantation

Early prediction of acute respiratory distress syndrome (ARDS) after liver transplantation (LT) facilitates timely intervention. We aimed to develop a predictor of post-LT ARDS using machine learning (ML) methods. Data from 755 patients in the internal validation set and 115 patients in the external validation set were retrospectively reviewed, covering demographics, etiology, medical history, laboratory results, and perioperative data. According to the area under the receiver operating characteristic curve (AUROC), accuracy, specificity, sensitivity, and F1-value, the prediction performance of seven ML models, including logistic regression (LR), decision tree, random forest (RF), gradient boosting decision tree (GBDT), na & iuml;ve bayes (NB), light gradient boosting machine (LGBM) and extreme gradient boosting (XGB) were evaluated and compared with acute lung injury prediction scores (LIPS). 234 (30.99%) ARDS patients were diagnosed. The RF model had the best performance, with an AUROC of 0.766 (accuracy: 0.722, sensitivity: 0.617) in the internal validation set and a comparable AUROC of 0.844 (accuracy: 0.809, sensitivity: 0.750) in the external validation set. The performance of all ML models was better than LIPS (AUROC 0.692, 0.776). The predictor variables included the age of the recipient, BMI, MELD score, total bilirubin, prothrombin time, operation time, standard urine volume, total intake volume, and red blood cell infusion volume. We firstly developed a risk predictor of post-LT ARDS based on RF model to ameliorate clinical practice.