Sepsis-associated acute kidney injury (SA-AKI) is a common and serious complication in critically ill patients, leading to increased morbidity and mortality. This study aims to develop a model for early identification and risk assessment of SA-AKI based on contrast-enhanced ultrasound (CEUS) and biomarkers. This retrospective observational study included 152 septic patients admitted to the Surgical Intensive Care Unit (SICU) of Zhongshan Hospital from January 2021 to June 2022. Patients were divided into training and validation cohorts. Clinical data, CEUS-derived parameters, and biomarkers were collected within 24 h of admission. A risk assessment model was developed using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression. Model performance was assessed by the area under the receiver operating characteristic curve (AUROC) and calibration. The primary outcome was AKI occurring within 48 h of ICU admission. SA-AKI occurred in 86 patients (56.6%). Four independent predictors—urinary neutrophil gelatinase-associated lipocalin (NGAL), serum cystatin C, renal resistive index (RRI), and medullary wash-in slope (WIS)—were identified. The model demonstrated high discriminative performance, with AUROCs of 0.96 in the training cohort and 0.93 in the validation cohort. Sensitivity and specificity were 82.8% and 91.1% in the training cohort, and 75.0% and 88.9% in the validation cohort. Decision curve analysis (DCA) demonstrated favorable net clinical benefit across a wide range of thresholds. We developed a noninvasive model integrating CEUS-derived microcirculatory metrics and renal biomarkers for early identification and risk assessment of SA-AKI at ICU admission. This model may help clinicians prioritize surveillance and renal-protective strategies in the early phase of sepsis care. Given the single-center retrospective design and internal validation, external validation in independent multicenter cohorts is essential before any clinical implementation, and the generalizability of these findings should not be assumed. SA-AKI significantly increases mortality and morbidity in sepsis patients. This study aimed to develop an early risk assessment model for SA-AKI using conventional ultrasound, CEUS, and biomarkers. Independent risk factors identified include RRI, urinary NGAL, cystatin C, and medullary WIS. The model showed high discriminative performance with an AUROC of 0.96 in the training cohort and 0.93 in the validation cohort. Early identification of SA-AKI may help clinicians prioritize surveillance and kidney-protective care during the initial ICU phase.
Pan et al. (Sat,) studied this question.