Development and validation of a nomogram for postoperative severe acute kidney injury in acute type A aortic dissection

BACKGROUND Postoperative acute kidney injury (AKI) is a major complication associated with increased morbidity and mo-rtality after surgery for acute type A aortic dissection (AAAD). To the best of our knowledge, risk prediction models for AKI fol-lowing AAAD surgery have not been reported. The goal of the present study was to develop a prediction model to predict severe AKI after AAAD surgery.METHODS A total of 485 patients who underwent AAAD surgery were enrolled and randomly divided into the training coh-ort (70％) and the validation cohort (30％). Severe AKI was defined as AKI stage Ⅲ following the Kidney Disease: Improving Glo-bal Outcomes criteria. Preoperative variables, intraoperative variables and postoperative data were collected for analysis. Multiv-ariable logistic regression analysis was performed to select predictors and develop a nomogram in the study cohort. The final predic-tion model was validated using the bootstrapping techniques and in the validation cohort. RESULTS The incidence of severe AKI was 23.0％ (n = 78), and 14.7％ (n = 50) of patients needed renal replacement treatment. The hospital mortality rate was 8.3％ (n = 28), while for AKI patients, the mortality rate was 13.1％, which increased to 20.5％ for severe AKI patients. Univariate and multivariate analyses showed that age, cardiopulmonary bypass time, serum creatinine, and D-dimer were key predictors for severe AKI following AAAD surgery. The logistic regression model incorporated these pred-ictors to develop a nomogram for predicting severe AKI after AAAD surgery. The nomogram showed optimal discrimination abil-ity, with an area under the curve of 0.716 in the training cohort and 0.739 in the validation cohort. Calibration curve analysis dem-onstrated good correlations in both the training cohort and the validation cohort. CONCLUSIONS We developed a prognostic model including age, cardiopulmonary bypass time, serum creatinine, and D-di-mer to predict severe AKI after AAAD surgery. The prognostic model demonstrated an effective predictive capability for severe AKI, which may help improve risk stratification for poor in-hospital outcomes after AAAD surgery.