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The Utility of Urine Microscopy Score for Early Detection and Prediction of Acute Kidney Injury in At-Risk Patients
Rolando Claure-Del Granado1, Diego Torrico-Moreira1, Jingyao Zhang2
1Division of Nephrology, Department of Medicine, Hospital Obrero No. 2 - CNS, Universidad Mayor de San Simon, Cochabamba, Bolivia.
Kidney360
|August 13, 2025
Abstract
No abstract available in PubMed .
Insights
The urine microscopy score (UMS) effectively identifies subclinical acute kidney injury (AKI) in hospitalized patients. This simple test aids in predicting clinical AKI development, enabling earlier interventions.
Area of Science:
- Nephrology
- Clinical Diagnostics
- Biomarker Research
Background:
- Acute Kidney Injury (AKI) presents a significant global health challenge, marked by high morbidity and mortality rates.
- Early detection and management of subclinical AKI are crucial for improving patient outcomes.
- The AKI Risk Assessment Model (ARA-F4) is used to identify patients at risk, but a simple biomarker for subclinical AKI is needed.
Purpose of the Study:
- To evaluate the Urine Microscopy Score (UMS) as a substitute biomarker within the ARA-F4 model.
- To determine if UMS can identify subclinical AKI and predict the development of clinical AKI.
- To assess the cost-effectiveness and simplicity of UMS in AKI risk stratification.
Main Methods:
- A prospective cohort study enrolled hospitalized adult patients (non-ICU) at moderate to high risk for AKI based on the ARA-F4 model.
- Urine microscopy was performed at admission; subclinical AKI (AKI-1S) was defined as UMS ≥2 without concurrent serum creatinine elevation.
- Primary outcomes included clinical AKI development within 48 hours, need for kidney replacement therapy (KRT), and mortality. Predictive performance was assessed using AUC.
Main Results:
- Of 103 patients, 37.9% were classified as AKI-1S. Among AKI-1S patients, 89.7% developed clinical AKI within 48 hours, versus 10.9% in the non-AKI group (p<0.05).
- The AKI-1S group showed significantly higher rates of KRT (10.3% vs. 1.6%, p<0.05) and mortality (43.6% vs. 14.1%, p<0.05).
- UMS demonstrated good predictive performance for AKI development (AUC=0.84), with 74.5% sensitivity and 92.9% specificity.
Conclusions:
- The UMS is a valuable tool for identifying subclinical AKI within the ARA-F4 model.
- Early recognition of subclinical AKI using UMS facilitates timely interventions, potentially reducing AKI burden.
- UMS offers a simple, cost-effective method for AKI prediction, particularly beneficial in low- and middle-income countries.
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