Development of a Predictive Model of Intraoperative Conversion to Thoracotomy for Lung Cancer Patients Undergoing Video-Assisted Thoracoscopic Surgery Lobectomy

Abstract

Background: The predictors of intraoperative conversion to thoracotomy (ICT) remained inadequately understood. This multicenter trial aimed to develop a predictive model based on logistic regression model and other machine learning algorithms, thus to evaluate the risk of ICT for lung cancer patients undergoing video-assisted thoracoscopic surgery (VATS) lobectomy. Methods: We retrospectively collected data of lung cancer patients undergoing VATS lobectomy from three centers from September 2005 to August 2019. Predictors were selected based on stepwise selection, least absolute shrinkage and selection operator regression. Modelling approach included logistic regression, artificial neural network and random forest. Models was compared with area under the curve (AUC) of receiver operating characteristic (ROC), brier score and Logarithmic-loss in internal validation (10-fold cross validation) and external validation (200 bootstraps). Findings: Finally, 6546 patients were included, and 251 (3.8%) patients experienced ICT. Logistic regression-based model with nine predictors consisting of age, sex, peak expiratory flow, tumor size, clinical TNM stages, VATS ports, resection lobes, with/without pleural adhesion and with/without well-developed interlobar lung fissure, were selected based on the best discrimination and calibration in internal (AUC of ROC: 0.709, 95% CI: 0.560-0.806; Brier score: 0.036, 95%CI: 0.017-0.078; Logarithmic-loss: 0.154, 95%CI: 0.087-0.307) and external validation (AUC of ROC: 0.759, 95% CI: 0.519-0.968; Brier score: 0.037, 95% CI: 0.033-0.041; Logarithmic-loss: 0.152, 95% CI: 0.124-0.179). Interpretation: Logistic-regression based predictive model with nine selected predictors had the capability to evaluate the risk of ICT for lung cancer patients undergoing VATS lobectomy. Larger prospective study was needed to validate model performance. Funding Information: This work was supported by 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (ZYGD18021, ZYJC18009), and Post-Doctor Research Project, West China Hospital, Sichuan University (2020HXBH109). Declaration of Interests: The authors declare no potential conflicts of interest. Ethics Approval Statement: The Ethics Committee of West China Hospital of Sichuan University (No. 2020-1034) and Affiliated Hospital of Zunyi Medical University (No. KLL-2020-278) approved this study and waived the requirement of informed consent.