Prediction models do not optimally perform in the case of aorta surgery. We tried to define models that predict intensive care death for patients who underwent thoracic aorta surgery in the Netherlands. Therefore, we used data of 1290 patients who underwent interventions on the thoracic aorta from 1997 to 2002 which were prospectively collected in seven centers. One outcome was examined: intensive care death. Predicting models were made by multiple logistic regression analysis. The area under the receiver operating characteristics curve was used to study the discriminatory abilities of these models. We compared the models with the Euroscore. Eleven percent of the patients died during operation or on intensive care. Age, creatinine level ≥150 μmol/l, poor left ventricular ejection fraction and urgent indication were most related with intensive care-death. Prolonged extracorporal circulation and deep hypothermia were also of importance in the peri-operative model. The models performed better than the Euroscore. We conclude that the developed models perform relatively well in discriminating patients with respect to intensive care-death and even better than the Euroscore.