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Background <p>This study was designed to explore the clinical utility of ultrasound hemodynamic parameters in predicting acute kidney injury (AKI) and assessing its severity.</p> Methods <p>A total of 122 patients initially diagnosed with AKI were included in this prospective observational study. The ultrasound measurements were completed within 24&#xa0;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.</p> Results <p>Patients were stratified into the AKI group (AKI stages 1–3) and the non-AKI group (AKI stage 0). Serum creatinine (SCr) ≥ 111&#xa0;μmol/L, renal resistive index (RRI) ≥ 0.70, and renal blood flow/cardiac output (RBF/CO) &lt; 0.06 were identified as risk factors for AKI (<i>P</i> &lt; 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&#xa0;μmol/L and RRI ≥ 0.73 emerged as risk factors for severe AKI (<i>P</i> &lt; 0.05). Based on these two indicators, a corresponding predictive model was established to predict severe AKI.</p> Conclusion <p>In 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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年7月30日

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  • Zhi-Ming Wang,
  • Sheng-Jiang Chen

摘要

Background

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

Methods

A 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.

Results

Patients 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.

Conclusion

In 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.