A Predictive Model for Postoperative Acute Kidney Injury in Patients with Acute Type A Aortic Dissection

Background Acute kidney injury (AKI) is a prevalent and severe complication following acute type A aortic dissection (ATAAD) surgery. Although several predictive models exist, most lack rigorous validation in independent populations, limiting their clinical generalizability and readiness for implementation. Methods This single-center, retrospective cohort study enrolled 317 patients with ATAAD. The cohort was chronologically divided into training and external temporal validation sets. A predictive nomogram was developed from preoperative variables identified by multivariate logistic analysis. Model performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA). Sensitivity analysis confirmed the robustness of the findings. Results Higher serum creatinine (SCr) levels, elevated D-dimer (DDi) concentrations, and lower platelet counts (PLT) were identified as independent preoperative risk factors for AKI following ATAAD surgery. The nomogram demonstrated a strong and generalizable discriminatory ability, with an area under the receiver operating characteristic curve of 0.842 in the training cohort and 0.804 in the temporal validation cohort. Calibration plots and DCA confirmed the favorable calibration and clinical utility. Restricted cubic spline analysis revealed nonlinear associations of AKI with preoperative SCr and PLT levels, but a linear positive association with DDi. Conclusion We developed and temporally validated a concise nomogram using three preoperative biomarkers (PLT, SCr, DDi) for predicting AKI after ATAAD surgery, providing a practical tool for preoperative risk stratification.