Distinction between urine crystals by automated urine analyzer SediMAX conTRUST is specific but lacks sensitivity

Evaluation of AUTION EYE AI-4510 flow cell morphology analyzer for counting particles in urine

OBJECTIVES

We evaluated the performance of a novel flow cell morphology analyzer AUTION EYE AI-4510 for counting particles in urine.

METHODS

Analytical performance was assessed according to the EFLM European Urinalysis Guideline 2023. Trueness was compared by analyzing 1.012 fresh urine samples with the AUTION EYE AI-4510 (ARKRAY, Inc., Kyoto, Japan) against phase-contrast visual microscopy. Poisson statistics were utilized in assessment of imprecision of particle counts both with quality control material and patient specimens.

RESULTS

Relative imprecision against theoretical Poisson imprecision, R(CV), was estimated to be 1.1 for red blood cells (RBC), 1.0 for white blood cells (WBC), 0.9 for squamous epithelial cells (SEC) and 1.1 for bacteria. The agreement with visual microscopy (Cohen's weighted kappa) was 0.93 for RBC, 0.95 for WBC, 0.90 for SEC, 0.79 for non-squamous epithelial cells (NSEC), 0.67 for combined casts, 0.90 for crystals and 0.88 for bacteria. No clinically significant bias was observed. Limits of quantitation at CV=30 % reached 4 × 106/L for RBC and 5 × 106/L for WBC. Differentiation of urinary crystals was improved as compared to previous data on digital cuvette imaging.

CONCLUSIONS

The ARKRAY AUTION EYE AI-4510 provided a desirable imprecision, met the criteria for linearity, LoQ and carry-over, and showed an optimum comparison to visual microscopy for RBC, WBC, SEC and crystals as defined in the EFLM European Urinalysis Guideline 2023. The identification of kidney damage is recommended to be improved by using user-defined review rules. Performance of bacteria counting needs to be confirmed against urine bacterial cultures.

Advances and Progress in Automated Urine Analyzers

The clinical analysis of urine has classically focused on conventional chemical-based urinalysis and urine microscopy. Contemporary advances in both analysis subsets have started to employ new technologies such as automated image analysis, flow cytometry, and mass spectrometry. In addition to new detection technologies, current analyzers have incorporated more advanced imaging, automated sample handing, and machine learning analyses into their workflow. The most advanced semiautomated analyzers can be interfaced with hospital medical record systems, and in the point-of-care setting, smartphones can be used for image analysis. This review will discuss current technological advancements in the field of urinalysis and urine microscopy.

Progress in Automated Urinalysis

New technological advances have paved the way for significant progress in automated urinalysis. Quantitative reading of urinary test strips using reflectometry has become possible, while complementary metal oxide semiconductor (CMOS) technology has enhanced analytical sensitivity and shown promise in microalbuminuria testing. Microscopy-based urine particle analysis has greatly progressed over the past decades, enabling high throughput in clinical laboratories. Urinary flow cytometry is an alternative for automated microscopy, and more thorough analysis of flow cytometric data has enabled rapid differentiation of urinary microorganisms. Integration of dilution parameters (e.g., creatinine, specific gravity, and conductivity) in urine test strip readers and urine particle flow cytometers enables correction for urinary dilution, which improves result interpretation. Automated urinalysis can be used for urinary tract screening and for diagnosing and monitoring a broad variety of nephrological and urological conditions; newer applications show promising results for early detection of urothelial cancer. Concomitantly, the introduction of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) has enabled fast identification of urinary pathogens. Automation and workflow simplification have led to mechanical integration of test strip readers and particle analysis in urinalysis. As the information obtained by urinalysis is complex, the introduction of expert systems may further reduce analytical errors and improve the quality of sediment and test strip analysis. With the introduction of laboratory-on-a-chip approaches and the use of microfluidics, new affordable applications for quantitative urinalysis and readout on cell phones may become available. In this review, we present the main recent developments in automated urinalysis and future perspectives.