Based solely on the provided article body, a machine learning–based prediction of prolonged mechanical ventilation (PMV) is being explored for patients after Stanford type A aortic dissection (TAAD) surgery. TAAD is an acute condition with a mortality rate increasing by approximately 1% per hour after onset and reaching up to 50% within 48 hours. The incidence of respiratory failure after TAAD surgery is as high as 51.6%, and the incidence of PMV ranges from 28.9% to 73.3%. Extended mechanical ventilation may elevate patient mortality by 2.3% to 17.0%. The article discusses that machine learning methods, including support vector machine (SVM) and extreme gradient boosting (XGBoost), can handle large datasets and extract complex relationships to support risk assessment. The predictive performance of these methods can be assessed using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).