Work

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

Year: 2025

Type: article

Abstract: Objectives: 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). Methods: = 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. Results: = 0.001), with optimal decision thresholds at 40-60% probability. Conclusion: The nomogram demonstrates superior preoperative discriminative accuracy in AKI following ATAAD repair surgery. External validation via the VASCUNET registry is planned to confirm generalizability.

Source: Frontiers in Medicine

DOI: 10.3389/fmed.2025.1562956

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

Institutions: Hebei Medical University, First Affiliated Hospital of Hebei Medical University

Language: English

Topic: Aortic Disease and Treatment Approaches

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801