Machine learning of intraoperative variables to test feasibility of multivariable prediction modelling for postoperative complications in thoracic surgery: a prospective cohort study

Among patients undergoing thoracic surgery, the impact of intraoperative variables on postoperative complications is unclear. Because patients receiving one-lung ventilation (OLV) experience further unique intraoperative stressors, a knowledge gap exists around the impact of intraoperative predictor variables that may not be well-accounted for in existing risk prediction models. The objectives of this study were therefore to (I) assess the feasibility of measuring intraoperative variables using modern machine learning techniques; (II) determine if machine learning of intraoperative parameters predicts postoperative complications; and (III) compare model performance of machine learning against a set of known preoperative predictors.