Evidence map›Paper›PMID 40742654›Full record

Observational studyInternational urology and nephrology2026

Ultrasonic hemodynamic parameters for predicting acute kidney injury and establishment of a predictive model based on these parameters.

Zhi-Ming Wang, Sheng-Jiang Chen

Abstract readObservational Study
PubMed Publisher
In one paragraph

Observational study in International urology and 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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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Zhi-Ming WangUltrasound Department, The First Affiliated Hospital of Henan University of Science and Technology, No. 263, Kaiyuan Road, Luolong District, Luoyang, 471000, Henan, China.
Sheng-Jiang ChenUltrasound Department, The First Affiliated Hospital of Henan University of Science and Technology, No. 263, Kaiyuan Road, Luolong District, Luoyang, 471000, Henan, China. shengjiangchenmmc@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study was designed to explore the clinical utility of ultrasound hemodynamic parameters in predicting acute kidney injury (AKI) and assessing its severity.

methodsA total of 122 patients initially diagnosed with AKI were included in this prospective observational study. The ultrasound measurements were completed within 24 h of admission. Significant variables associated with AKI were identified through multivariable logistic regression. The discriminative power of the established model was evaluated using receiver operating characteristic (ROC) curve analysis.

resultsPatients were stratified into the AKI group (AKI stages 1-3) and the non-AKI group (AKI stage 0). Serum creatinine (SCr) ≥ 111 μmol/L, renal resistive index (RRI) ≥ 0.70, and renal blood flow/cardiac output (RBF/CO) < 0.06 were identified as risk factors for AKI (P < 0.05) in the multivariate logistic regression analysis. The predictive model that was established to predict AKI incorporating these parameters demonstrated high accuracy. Patients were further categorized into the mild AKI group (AKI stages 0-1) and the severe AKI group (AKI stages 2-3). In the multivariate logistic regression analysis, SCr ≥ 210 μmol/L and RRI ≥ 0.73 emerged as risk factors for severe AKI (P < 0.05). Based on these two indicators, a corresponding predictive model was established to predict severe AKI.

conclusionIn this study, it was found that early-stage AKI could be predicted using SCr, RRI, and RBF/CO, while SCr and RRI were effective in predicting the severity of AKI. These parameters may facilitate timely intervention in patients with AKI.

Indexed as

Acute Kidney InjuryHemodynamicsAdultAgedCreatinineFemaleHumansLogistic ModelsMaleMiddle AgedPredictive Value of TestsProspective StudiesRenal CirculationRisk FactorsROC CurveSeverity of Illness IndexCreatinineAcute kidney injuryRenal blood flowRenal resistive indexRenal ultrasoundSerum creatinine

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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.