Abstract
This study aimed to establish a predictive model for the risk of post-thoracic endovascular aortic repair (TEVAR) post-implantation syndrome (PIS) in type B aortic dissection (TBAD) patients, assisting clinical physicians in early risk stratification and decision management for high-risk PIS patients. This study retrospectively analyzed the clinical data of 547 consecutive TBAD patients who underwent TEVAR treatment at our hospital. Feature variables were selected through LASSO regression and logistic regression analysis to construct a nomogram predictive model, and the model's performance was evaluated. The optimal cutoff value for the PIS risk nomogram score was calculated through receiver operating characteristic (ROC) curve analysis, further dividing patients into high-risk group (HRG) and low-risk group (LRG), and comparing the short to midterm postoperative outcomes between the two groups. In the end, a total of 158 cases (28.9%) experienced PIS. Through LASSO regression analysis and multivariable logistic regression analysis, variables including age, emergency surgery, operative time, contrast medium volume, and number of main prosthesis stents were selected to construct the nomogram predictive model. The model achieved an area under the curve (AUC) of 0.86 in the training set and 0.82 in the test set. Results from calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated that the predictive model exhibited good performance and clinical utility. Furthermore, after comparing the postoperative outcomes of HRG and LRG patients, we found that the incidence of postoperative PIS significantly increased in HRG patients. The duration of ICU stay and mechanical assistance time was prolonged, and the incidence of postoperative type II entry flow and acute kidney injury (AKI) was higher. The risk of aortic-related adverse events (ARAEs) and major adverse events (MAEs) at the first and twelfth months of follow-up also significantly increased. However, there was no significant difference in the mortality rate during hospitalization. This study established a nomogram model for predicting the risk of PIS in patients with TBAD undergoing TEVAR. It serves as a practical tool to assist clinicians in early risk stratification and decision-making management for patients.