Machine learning-based prediction of prolonged air leak after uniportal video-assisted thoracic surgery segmentectomy

Objective Prolonged air leak (PAL), defined as air leakage lasting more than 5 days, remains a prevalent complication following uniportal video-assisted thoracic surgery (uVATS) segmentectomy, contributing to extended hospitalization, elevated medical expenditures, and higher postoperative morbidity. This study was designed to develop and internally validate machine learning models for predicting PAL after uVATS segmentectomy using a comprehensive set of clinical, surgical, and physiological var