Machine learning–enhanced preoperative assessment of spontaneous ventilation risk in diverse video-assisted thoracoscopic surgeries

Objectives Despite its widespread use, spontaneous ventilation can lead to intraoperative hypoxemia or hypercapnia. In this retrospective study, we developed and validated a preoperative clinical data-based model to predict intraoperative hypoxemia or hypercapnia risk during spontaneous ventilation video-assisted thoracoscopic surgery. Methods We enrolled patients who underwent spontaneous ventilation video-assisted thoracoscopic surgery at the National Respiratory Medicine Center of First Affiliated Hospital of Guangzhou Medical University between 2011 and 2023. Four machine learning algorithms—light gradient boosting machine, category boosting, extreme gradient boosting, and random forest—were trained and combined into a weighted ensemble to predict intraoperative hypoxemia (peripheral oxygen saturation 80 mm Hg). Results Among 6590 included patients, 12.6% and 3.8% developed intraoperative hypoxemia and severe hypercapnia, respectively. The final risk assessment model achieved an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.68-0.76) and an area under the precision-recall curve of 0.35 (95% CI, 0.29-0.42). The statistical analysis of the data and the risk interpretation from the ensemble machine learning algorithm consistently identified body mass index (23.6 [21.6-25.5] vs 22.2 [20.4-24.0] kg/m 2 ) and age (55 [46-62] vs 50 [41-58] years) as the strongest patient-related predictors positively correlated with intraoperative hypoxemia and severe hypercapnia. Segmentectomy (22.4% vs 14.7%), lobectomy (21.9% vs 17.2%), and surgical location in the right lung (86.4% vs 73.8%) increased risk of intraoperative hypoxia and hypercapnia. Analysis of postoperative hospital stay (3.67 vs 3.49 days), chest tube drainage (1.59 vs 1.47 days), complications (88.1 vs 91%), and incidence of intensive care unit (8.2 vs 7.6%) demonstrated that intraoperative hypoxia and hypercapnia can adversely affect patient postoperative recovery ( P < .05). Conclusions The predictive model based on preoperative clinical characteristics effectively assessed intraoperative hypoxemia and severe hypercapnia risk in diverse spontaneous ventilation video-assisted thoracoscopic surgery procedures. This model can systematically guide preoperative evaluation to select appropriate patients and improve the intraoperative safety and surgical outcomes of spontaneous ventilation video-assisted thoracoscopic surgery.