AUTHOR=Du Rui , Wang Lai , Wang Yan , Zhao Zhitao , Zhang Dahong , Zuo Shanshan 

TITLE=AKI prediction model in acute aortic dissection surgery: nomogram development and validation

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1562956

DOI=10.3389/fmed.2025.1562956

ISSN=2296-858X

ABSTRACT=ObjectivesThis multicenter study developed and internally validated a biomarker-enhanced risk prediction nomogram integrating hemodynamic parameters and novel urinary biomarkers to stratify postoperative acute kidney injury (AKI) risks in patients undergoing emergency surgical repair for acute Stanford Type A aortic dissection (ATAAD).MethodsA cohort of 1,277 patients from the China Aortic Dissection Alliance (CADA) registry was chronologically split into derivation (70%, n = 894) and validation (30%, n = 383) sets. LASSO regression with 10-fold cross-validation (λ1SE criterion) was applied to identify non-redundant predictors from 34 candidate variables (e.g., cardiac dysfunction [LVEF <50% or INTERMACS 1–3]) and elevated urinary biomarkers. Multivariable logistic regression refined these predictors to establish independent risk factors for the final nomogram. Model performance was evaluated using the concordance index (C-index), area under the receiver operating characteristic curve (AUC-ROC), calibration plots (Brier score and Hosmer-Lemeshow test), and decision curve analysis (DCA) to quantify clinical utility.ResultsMultivariable analysis identified seven independent predictors of postoperative AKI: preexisting cardiac dysfunction (adjusted odds ratio [aOR] = 2.17; 95% CI: 1.68–3.56), microvascular complications of diabetes (aOR = 3.26; 2.71–4.34), baseline renal impairment (aOR = 1.72; 1.36–3.29), blood urea nitrogen (BUN) ≥ 20 mg/dL (aOR = 2.19; 1.57–3.64), glomerular filtration rate (GFR) < 90 mL/min/1.73 m2 (aOR = 1.47; 1.02–2.13), serum creatinine >1.3 mg/dL (aOR = 3.28; 2.58–3.75), and peripheral vasculopathy (aOR = 1.78; 1.12–2.32). The model demonstrated strong discrimination (training AUC-ROC: 0.830 [0.802–0.858]; internal validation AUC-ROC: 0.786 [0.737–0.834]), calibration (Brier scores: 0.138 training, 0.141 validation), and clinical utility (net reclassification improvement [NRI] = 0.21, p = 0.001), with optimal decision thresholds at 40–60% probability.ConclusionThe nomogram demonstrates superior preoperative discriminative accuracy in AKI following ATAAD repair surgery. External validation via the VASCUNET registry is planned to confirm generalizability.