Machine Learning in One-Lung Ventilation: From Static Risk Scores to Early Intraoperative Assessment of Severe Hypoxemia and Prediction of Prolonged Extubation

Background: One-lung ventilation (OLV) is routinely used during thoracic surgery. Static respiratory risk scores may not capture the physiological changes that occur after lung isolation. This study evaluated whether an early intraoperative risk assessment model based on routinely available perioperative variables could improve the identification of severe hypoxemia during OLV and predict prolonged postoperative mechanical ventilation. Methods: This single-center prospective observational study included 103 adults undergoing thoracic surgery with OLV. Severe hypoxemia was defined as a PaO2/FiO2 ratio < 100 during OLV. Prolonged extubation was defined as postoperative mechanical ventilation > 180 min. Models were developed using routinely available preoperative and intraoperative variables. Logistic regression, LASSO logistic regression, Random Forest, and Gradient Boosting were evaluated using internal cross-validation. Results: Severe hypoxemia occurred in 44 patients (42.7%), whereas prolonged extubation > 180 min occurred in 34 patients (33.0%). The early intraoperative model achieved an OOF AUC of 0.745, while prediction of prolonged extubation was more modest (OOF AUC 0.671). PaCO2 during OLV and ASA ≥ 4 were the leading contributors to Models 2 and 3, respectively. Conclusions: Static preoperative scores provided limited information for identifying severe hypoxemia during OLV. Incorporating routinely available intraoperative variables supported early intraoperative risk assessment, while perioperative variables showed moderate ability to predict prolonged extubation. Further validation in larger cohorts is required.