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Background <p>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.</p> Methods <p>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&#xa0;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&#xa0;h of ICU admission.</p> Results <p>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.</p> Conclusion <p>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.</p>

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An early risk assessment model for sepsis-associated acute kidney injury based on CEUS and biomarkers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月21日

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  • Simeng Pan,
  • Yi Han,
  • Wei Wu,
  • Yijun Zheng,
  • Yao Yao,
  • Shilong Lin,
  • Xu Wang,
  • Ming Zhong,
  • Jieqiong Song

摘要

Background

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.

Methods

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.

Results

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.

Conclusion

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.