How Reliable Is Automated Urinalysis in Acute Kidney Injury?

Area of Science:

• Nephrology
• Clinical Chemistry
• Urology

Background:

• Urine sediment examination is vital for diagnosing acute kidney injury (AKI).
• Specific urinary findings like tubular cells, casts, and dysmorphic red blood cells can indicate AKI etiology.
• Current diagnostic approaches often rely on manual microscopic analysis.

Purpose of the Study:

• To compare the diagnostic accuracy of automated urinalysis findings with traditional manual microscopic analysis in patients with AKI.
• To evaluate the predictive capability of automated urinalysis parameters for AKI.
• To assess the agreement between automated and manual methods in quantifying urinary elements relevant to AKI.

Main Methods:

• A comparative study design involving patients diagnosed with AKI and a control group.
• Quantification of red blood cells, white blood cells, renal tubular epithelial cells/small round cells, casts, and pathologic casts using both manual microscopy and an automated UF1000i cytometer.
• Statistical analysis including Spearman correlation and logistic regression to compare methods and assess predictive value.

Main Results:

• A poor correlation was observed between manual microscopic findings and automated urinalysis results in the context of AKI.
• Automated urinalysis parameters, individually, did not demonstrate significant predictive ability for AKI via logistic regression.
• A notable finding was that an increase in the automated pathologic cast count was associated with a 93-fold increase in the odds of AKI.

Conclusions:

• Automated urinalysis parameters are generally poor predictors of acute kidney injury when compared to manual microscopy.
• There is a lack of agreement between automated and manual methods for key urine sediment components in AKI.
• Despite poor overall correlation, automated pathologic cast counts may hold some predictive value for AKI.