Development of a Risk Scoring Model to Predict Unexpected Conversion to Thoracotomy during Video-Assisted Thoracoscopic Surgery for Lung Cancer

Objective: This study aimed to create a risk scoring model to foresee unexpected conversions to thoracotomy during video-assisted thoracoscopic surgery (VATS) for lung cancer. By identifying the factors contributing to these conversions, surgical planning and patient outcomes can be enhanced. Methods: A retrospective analysis was performed on 240 patients who underwent VATS for lung cancer from January 2019 to December 2024. Among these, 26 patients required conversion to thoracotomy. Various clinical and perioperative factors were examined to identify predictors of conversion through univariate and multivariate logistic regression analyses. A risk scoring model was subsequently developed based on these factors, and its predictive performance was assessed. Results: Of the 240 patients, 26 (10.8%) needed conversion to thoracotomy. Key predictors of conversion identified through multivariate analysis included larger tumor size (OR 2.5, 95% CI 1.2-5.3), central tumor location (OR 3.1, 95% CI 1.5-6.4), and reduced forced expiratory volume (FEV1) (OR 2.8, 95% CI 1.3-6.0). The risk scoring model exhibited strong predictive accuracy with an area under the receiver operating characteristic (ROC) curve of 0.82. Conclusion: The developed risk scoring model effectively predicts the likelihood of conversion to thoracotomy during VATS for lung cancer. This model serves as a valuable tool for preoperative planning and patient counseling, thereby potentially improving surgical outcomes and resource allocation.