To analyze the risk factors for postoperative hypoxemia in Stanford type A aortic dissection (TAAD), construct a risk nomogram model based on risk factors, and validate its efficacy.

A retrospective analysis was conducted on 347 TAAD patients treated in Ningbo Medical Center Lihuili Hospital from February 2019 to May 2025. The patients were divided into a training set of 260 cases and a validation set of 87 cases in a 3∶1 ratio. In the training set, there were 128 males and 132 females, with ages ranging from 22 to 81 years [(51.73±15.93) years]. The hypoxemia group and non hypoxemia group were analyzed based on whether hypoxemia occurred 24 hours after surgery. The clinical data of the two groups were compared, and binary logistic regression was used to analyze the risk factors for postoperative hypoxemia in TAAD patients. A nomogram prediction model was constructed based on risk factors. The predictive value of the model was analyzed using receiver operating characteristic (ROC) curves, and the model calibration was evaluated using Hosmer-Lemeshow goodness of fit test.

The proportions of patients aged ≥50 years, body mass index (BMI) ≥25 kg/m², pericardial effusion, white blood cell (WBC) count, high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and blood ransfusion volume were higher in the hypoxemia group than in the non-hypoxemia group; the cardiopulmonary bypass time was longer in the hypoxemia group (P<0.05). According to binary logistic regression analysis, age≥50 years old, BMI≥25 kg/m², pericardial effusion, high WBC count, high hs-CRP, and prolonged cardiopulmonary bypass are risk factors for postoperative hypoxemia in TAAD patients (P<0.05). The constructed nomogram model has a C-index of 0.863 (95%CI: 0.818-0.909) on the training set and 0.856 after Bootstrap correction, with a difference of only 0.007, indicating a low risk of overfitting; The accuracy of the model is 78%. The C-index of the validation set is 0.842 (95%CI: 0.759-0.942), which is close to the corrected value, indicating good generalization ability of the model.

The predictive model for postoperative hypoxemia in TAAD patients constructed in this article shows good performance and can provide auxiliary predictive value for the occurrence of postoperative hypoxemia in TAAD patients.