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

Journal: Frontiers In Medicine
Published:
Abstract

This 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). A 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. Multivariable 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. The nomogram demonstrates superior preoperative discriminative accuracy in AKI following ATAAD repair surgery. External validation via the VASCUNET registry is planned to confirm generalizability.

Authors
Rui Du, Lai Wang, Yan Wang, Zhitao Zhao, Dahong Zhang, Shanshan Zuo
Relevant Conditions

Aortic Dissection