Development and Validation of a Nomogram-Based Risk Prediction Model for Postoperative Delirium in Patients with Stanford Type A Aortic Dissection

Background Postoperative delirium (POD) is a common and serious neurological complication following surgical repair of Stanford type A aortic dissection (TAAD), and is associated with poor clinical outcomes. This study aimed to develop and validate a nomogram-based risk prediction model for POD in patients with TAAD. Methods This prospective observational study included patients who underwent surgical treatment for TAAD at a cardiac surgery center in Nanjing between August 2021 and June 2023. Clinical data were collected prospectively, and mental status was assessed continuously until discharge. Patients were divided into delirium and non-delirium groups based on the occurrence of POD. Risk factors for POD were identified using multivariate logistic regression, best subset selection, and least absolute shrinkage and selection operator (LASSO) regression. The area under the receiver operating characteristic curve (AUC) was used to compare model performance and determine the optimal predictive model. Results A total of 510 patients were included, among whom 253 (49.61%) developed POD. The logistic regression–based model demonstrated the best predictive performance. Independent risk factors for POD included smoking, body mass index (BMI) ≥ 25 kg/m², elevated preoperative white blood cell (WBC) count, lower baseline left regional cerebral oxygen saturation (rSO₂), postoperative hypernatremia, and higher acute physiology and chronic health evaluation II (APACHE II) scores (all P  < 0.05). A nomogram was constructed based on these six variables. The model demonstrated excellent discrimination with an AUC of 0.929 and a concordance index (C-index) of 0.720. The sensitivity and specificity were 0.815 and 0.914, respectively. Conclusions The proposed nomogram-based model effectively predicts the risk of POD in patients undergoing surgery for Stanford type A aortic dissection. It may assist clinicians in early identification of high-risk patients and facilitate timely preventive and therapeutic interventions.