{"title":"The Utility of Urine Microscopy Score for Early Detection and Prediction of Acute Kidney Injury in At-Risk Patients.","authors":"Rolando Claure-Del Granado, Diego Torrico-Moreira, Jingyao Zhang, Jacqueline Breunig, Basmh Shamel, Vineet Gupta, Tushar Chopra, Subhasis Dasgupta, Rakesh Malhotra","doi":"10.34067/KID.0000000911","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>AKI is a global health concern associated with high morbidity and mortality. Early diagnosis and treatment of subclinical AKI are critical for mitigating adverse outcomes. Here, we evaluated whether the urine microscopy score (UMS), a simple and cost-effective method for detecting structural kidney injury, could serve as a substitute biomarker within the AKI Risk Assessment Model (ARA-F4) to identify subclinical AKI and predict clinical AKI development.</p><p><strong>Methods: </strong>A prospective cohort study was conducted, enrolling hospitalized adult patients (non-ICU) at moderate to high risk of AKI according to ARA-F4 model. At admission, urine microscopy was performed, and patients with UMS ≥2 without concurrent serum creatinine elevation were classified as subclinical AKI (AKI-1S); those with UMS = 1 were classified as non-AKI. The primary outcomes was development of clinical AKI within 48 hours, the need for kidney replacement therapy (KRT), and mortality. The discriminative ability of the UMS for predicting AKI was assessed using the area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>A total of 103 patients were included in the study, with 39 (37.9%) classified as AKI-1S and 64 (62.1%) as non-AKI at admission. Among the AKI-1S group, 89.7% developed clinical AKI within 48 hours compared to 10.9% of non-AKI patients (p<0.05). The AKI-1S group had a significant higher requirement for KRT (10.3% vs. 1.6%, p<0.05) and increased mortality rate (43.6% vs. 14.1%, p<0.05). The UMS demonstrated good predictive performance for AKI development, with an AUC of 0.84 (95% CI: 0.75-0.92). The sensitivity and specificity of the UMS were 74.5% and 92.9%, respectively.</p><p><strong>Conclusions: </strong>The UMS can be used in the ARA-F4 model to identify patients with subclinical AKI and predict the subsequent development of clinical AKI. Early recognition of subclinical AKI using the UMS can facilitate timely interventions and may reduce the burden of AKI in low- and middle-income countries.</p>","PeriodicalId":17882,"journal":{"name":"Kidney360","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Kidney360","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34067/KID.0000000911","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: AKI is a global health concern associated with high morbidity and mortality. Early diagnosis and treatment of subclinical AKI are critical for mitigating adverse outcomes. Here, we evaluated whether the urine microscopy score (UMS), a simple and cost-effective method for detecting structural kidney injury, could serve as a substitute biomarker within the AKI Risk Assessment Model (ARA-F4) to identify subclinical AKI and predict clinical AKI development.
Methods: A prospective cohort study was conducted, enrolling hospitalized adult patients (non-ICU) at moderate to high risk of AKI according to ARA-F4 model. At admission, urine microscopy was performed, and patients with UMS ≥2 without concurrent serum creatinine elevation were classified as subclinical AKI (AKI-1S); those with UMS = 1 were classified as non-AKI. The primary outcomes was development of clinical AKI within 48 hours, the need for kidney replacement therapy (KRT), and mortality. The discriminative ability of the UMS for predicting AKI was assessed using the area under the receiver operating characteristic curve (AUC).
Results: A total of 103 patients were included in the study, with 39 (37.9%) classified as AKI-1S and 64 (62.1%) as non-AKI at admission. Among the AKI-1S group, 89.7% developed clinical AKI within 48 hours compared to 10.9% of non-AKI patients (p<0.05). The AKI-1S group had a significant higher requirement for KRT (10.3% vs. 1.6%, p<0.05) and increased mortality rate (43.6% vs. 14.1%, p<0.05). The UMS demonstrated good predictive performance for AKI development, with an AUC of 0.84 (95% CI: 0.75-0.92). The sensitivity and specificity of the UMS were 74.5% and 92.9%, respectively.
Conclusions: The UMS can be used in the ARA-F4 model to identify patients with subclinical AKI and predict the subsequent development of clinical AKI. Early recognition of subclinical AKI using the UMS can facilitate timely interventions and may reduce the burden of AKI in low- and middle-income countries.