Six-minute walk test (6MWT) is one of the cardiopulmonary exercise testing (CPET). It is not only used to assess the cardiac and pulmonary function of patients with chronic obstructive pulmonary disease (COPD), but also used to assess COPD patients’ health-related quality of life (HRQoL) or self-management in daily life. With the concept of enhanced recovery after surgery (ERAS) put forward, assessing patients’ preoperative cardiac and pulmonary function, establishing preoperative and early postoperative exercises standards, as well as assessing cardiac and pulmonary rehabilitation after surgery become much more important. CPET gets more attention from clinical surgeons. This study focuses on the clinical value and status of 6MWT in thoracic surgery.
ObjectiveTo develop a predictive model for postoperative pulmonary complications (PPC) following video-assisted thoracic surgery (VATS) in lung cancer patients by integrating cardiopulmonary exercise testing (CPET) parameters and machine learning techniques. MethodsA retrospective analysis was conducted patients with early-stage non-small cell lung cancer who underwent CPET and VATS at Guangdong Provincial People’s Hospital between October 2021 and July 2023. Patients were divided into a PPC group and a non-PPC group. The least absolute shrinkage and selection operator (LASSO) regression was used to select important features associated with PPC. Six machine learning algorithms were utilized to construct prediction models, including logistic regression, support vector machine, k-nearest neighbors, random forest, gradient boosting machine, and extreme gradient boosting. The optimal model was interpreted using SHapley Additive exPlanations (SHAP). ResultsA total of 325 patients were included, with an average age of 60.36 years, and 55.1% were male. Significant differences were observed between the PPC and non-PPC groups in age, diabetes, coronary heart disease, surgical approach, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FVC% predicted, peak oxygen uptake (peak VO2), anaerobic threshold (AT), and ventilatory equivalent for carbon dioxide slope (VE/VCO2 slope) (P<0.05). In the predictive model constructed by selecting 7 key features using LASSO regression, the random forest model demonstrated the best overall performance across various metrics, with an AUC of 0.930, an F1 score of 0.836, and a Brier score of 0.133 in the training set. It also exhibited good predictive ability and calibration in the test set. SHAP analysis ranked feature importance as follows: peak VO2, VE/VCO2 slope, age, FEV1, smoking history, diabetes, and surgical approach. ConclusionIntegrating CPET parameters, the random forest model can effectively identify high-risk patients for PPC and has the potential for clinical application.