Random Forest-Based Prediction of Acute Respiratory Distress Syndrome in Patients Undergoing Cardiac Surgery

Abstract

Background: To develop a machine learning-based model for predicting the risk of acute respiratory distress syndrome (ARDS) after cardiac surgery. Methods: Data were collected from 1011 patients, who underwent cardiac surgery between February 2018 and September 2019. We developed a predictive model on ARDS by using the random forest algorithm of machine learning. The discrimination of the model was then shown by the area under the curve (AUC) of the receiver operating characteristic curve. Internal validation was performed by using a 5-fold cross-validation technique, so as to evaluate and optimize the predictive model. Model visualization was performed to reveal the most influential features during the model output. Results: Of the 1011 patients included in the study, 53 (5.24%) suffered ARDS episodes during the first postoperative week. This random forest distinguished ARDS patients from non-ARDS patients with an AUC of 0.932 (95% CI=0.896-0.968) in the training set and 0.864 (95% CI=0.718-0.997) in the final test set. The top 10 variables in the random forest were cardiopulmonary bypass time, transfusion red blood cell, age, EuroSCORE II score, albumin, hemoglobin, operation time, serum creatinine, diabetes, and type of surgery. Conclusion: Our findings suggest that machine learning algorithm is highly effective in predicting ARDS in patients undergoing cardiac surgery. The successful application of the generated random forest may guide clinical decision-making and aid in improving the long-term prognosis of patients.