Development of a Risk Scoring Model to Predict Unexpected Conversion to Thoracotomy during Video-Assisted...

Area of Science:

• Thoracic Surgery
• Surgical Oncology
• Pulmonary Medicine

Background:

• Unplanned conversion from video-assisted thoracoscopic surgery (VATS) to thoracotomy is associated with adverse outcomes.
• Identifying patients at high risk for conversion is crucial for surgical planning and patient safety.

Purpose of the Study:

• To identify preoperative risk factors for unexpected conversion to thoracotomy during VATS for non-small cell lung cancer.
• To develop and validate a predictive risk scoring model for preoperative use.

Main Methods:

• Retrospective analysis of 1,506 patients undergoing VATS for non-small cell lung cancer.
• Univariate analysis and logistic regression to identify independent risk factors.
• Development and validation of a 6-point risk scoring model using preoperative data.

Main Results:

• Male sex, prior ipsilateral lung surgery, calcified lymph nodes, and clinical T stage were independent predictors of conversion.
• The developed risk score demonstrated good predictive accuracy (AUC 0.747) in both derivation and validation cohorts.
• The model achieved 80.5% sensitivity and 91.0% negative predictive value for predicting conversion.

Conclusions:

• A validated risk scoring model can effectively predict the likelihood of unplanned conversion to thoracotomy during VATS.
• Preoperative identification of high-risk patients allows for better surgical planning and potentially improved outcomes.