To construct and compare the nomogram prediction model of perioperative acute kidney injury (AKI) in Stanford type A and type B acute aortic dissection (AAD) patients.

The data of patients diagnosed with AAD in our hospital from January 2019 to December 2021 were collected. The independent risk factors of TAAAD-AKI and TBAAD-AKI were screened by LASSO regression and multi-factor logistic regression, respectively, and the nomogram prediction model was constructed. Through the internal verification of bootstrapping, the advantages and disadvantages of the model were evaluated from three aspects: accuracy, calibration, and clinical benefit.

Data from 464 patients with AAD were collected. The incidence of TAAAD-AKI was 83.5% (147/176), whereas TBAAD-AKI was 41.0% (118/288). First serum creatinine (SCr) on admission, D-dimer value on admission, cardiopulmonary bypass time, mechanical ventilation time, and perioperative use of pressor medications were the independent risk variables for the creation of the TAAAD-AKI nomogram. The variables screened by TBAAD-AKI were first SCr on admission, poor renal perfusion on admission, days of ICU retention, and perioperative use of loop diuretics. The area under the curve (AUC) of receiver operating characteristic curves (ROC) of TAAAD-AKI model was 0.899, which implied a high level of accuracy. The AUC value of the TBAAD-AKI model was 0.825, indicating moderate accuracy. The two nomogram models had good calibration, according to the model's calibration curve and the Hosmer-Lemeshow test. The decision-making curve also found that the model had good clinical benefits.

The predictors of TAAAD-AKI and TBAAD-AKI are distinct except for the first SCr on admission. While the majority of the TBAAD-AKI are preoperative, the significant TAAAD-AKI variables are primarily focused during and after the procedure. Constructing and verifying two feasible nomograms is crucial for clinical early warning of AKI.