Evidence map›Paper›PMID 41723386›Full record

Observational studyBMC nephrology2026

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

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

Abstract readObservational Study
In one paragraph

Observational study in BMC nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Simeng Pan *Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200030, China.
Yi Han *Department of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200030, China.
Wei WuDepartment of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200030, China.
Yijun ZhengDepartment of Anaesthesia, Critical Care and Pain Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Yao YaoDepartment of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200030, China.
Shilong LinDepartment of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200030, China.
Xu WangDepartment of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200030, China.
Ming ZhongDepartment of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200030, China. zhongmingZS2024@163.com.
Jieqiong SongDepartment of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200030, China. song.jieqiong@zs-hospital.sh.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSepsis-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.

methodsThis 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.

resultsSA-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.

conclusionWe 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.

Indexed as

Acute Kidney InjurySepsisAgedBiomarkersContrast MediaCystatin CFemaleHumansLipocalin-2MaleMiddle AgedRetrospective StudiesRisk AssessmentUltrasonographyBiomarkersContrast MediaCystatin CLipocalin-2Acute kidney injuryCystatin cNeutrophil Gelatinase-Associated lipocalinRisk assessmentSepsisUltrasonography, doppler

Identifiers

PMID41723386
PMCPMC13032397

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.