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

Background: 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. Methods: A prospective cohort study was performed of consecutive patients undergoing thoracic surgery with OLV at a single Canadian centre. Intraoperative ventilatory and hemodynamic data were captured. Machine learning was used to predict postoperative complications based on: (I) preoperative variables only; (II) intraoperative variables only; and (III) combined data from both preoperative and intraoperative variables. Machine learning classification algorithms using random forests, support vector machines, and logistic regression models were compared. These models were analyzed with and without synthetic minority over-sampling technique (SMOTE). All models used the same split of training and test data. Results: Of 121 surgeries, there were 54 (44.6%) sublobar resections, 51 (42.2%) lobectomies, 3 (2.5%) pneumonectomies, and 13 (10.7%) non-pulmonary surgeries. 100 (82.6%) surgeries were minimally invasive. Mean operative time was 122.3±76.4 minutes. Postoperative complications occurred in 35 (28.9%) patients. Support vector machine classification algorithm using SMOTE of intraoperative data alone predicted complications with the highest F1 score of 0.8298. The most important parameters driving prediction accuracy were (I) duration of operative time; (II) mean and maximum heart rate; and (III) fraction of inspired oxygen. Using preoperative data alone and adding preoperative data to intraoperative data did not improve F1 score (max F1 score =0.6643 and 0.7647 respectively). Conclusions: Machine learning of intraoperative data may be feasible to predict postoperative complications. Therefore, intraoperative data could be used to augment existing risk prediction models. However, this study is hypothesis-generating only. Analysis of larger scale samples focused on specific complications by type are required to improve predictions.