Automated urinalysis combining physicochemical analysis, on-board centrifugation, and digital imaging in one system: A multicenter performance evaluation of the cobas 6500 urine work area

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.

Application of analytical performance specifications for urine test strip methods: Importance of reflectance signals

INTRODUCTION

Urinalysis is essential for diagnosing kidney-related medical conditions. Urine test strip analysis serves as an initial and efficient screening method for reflex testing with accurate quantitative methods.

MATERIALS AND METHODS

Freshly voided urines (n = 206) were analysed using two urine test strip brands on UC-MAX (Menarini) and cobas u 601 (Roche Diagnostics) instruments. Ordinal scale categories and reflectance signals (if available) were both used for the comparison with reference quantitative methods for glucose, proteins and albumin (cobas 503). Samples were considered positive when glucose > 15 or ≥ 54 mg/dL, proteins ≥ 200 mg/L and albumin ≥ 10 mg/L. Optimized reflectance thresholds were calculated by ROC curve analysis. Analytical performance specifications (APS) for trueness of test strip were gathered from the EFLM guideline (FPD, FNG, FNC).

RESULTS

Reflectance signals were significantly lower in urine samples considered positive by the reference method (p < 0.0001). Reflectance signals were also correlated with quantitative measurements, showing strong correlation (0.754 to 0.969). Only the use of optimized reflectance thresholds on cobas u 601 achieved at least the minimum EFLM APS (FPD < 20%, FNG < 50% and FNC < 10%).

CONCLUSION

The use of reflectance signals from urine test strips enhanced accuracy for glucose, proteins, and albumin measurement and may contribute to improve diagnosis of diverse kidney-related conditions.

Current Pyuria Cutoffs Promote Inappropriate Urinary Tract Infection Diagnosis in Older Women

BACKGROUND

Pre-existing lower urinary tract symptoms (LUTS), cognitive impairment, and the high prevalence of asymptomatic bacteriuria (ASB) complicate the diagnosis of urinary tract infection (UTI) in older women. The presence of pyuria remains the cornerstone of UTI diagnosis. However, >90% of ASB patients have pyuria, prompting unnecessary treatment. We quantified pyuria by automated microscopy and flowcytometry to determine the diagnostic accuracy for UTI and to derive pyuria thresholds for UTI in older women.

METHODS

Women ≥65 years with ≥2 new-onset LUTS and 1 uropathogen ≥104 colony-forming units (CFU)/mL were included in the UTI group. Controls were asymptomatic and classified as ASB (1 uropathogen ≥105 CFU/mL), negative culture, or mixed flora. Patients with an indwelling catheter or antimicrobial pretreatment were excluded. Leukocyte medians were compared and sensitivity-specificity pairs were derived from a receiver operating characteristic curve.

RESULTS

We included 164 participants. UTI patients had higher median urinary leukocytes compared with control patients (microscopy: 900 vs 26 leukocytes/µL; flowcytometry: 1575 vs 23 leukocytes/µL; P < .001). Area under the curve was 0.93 for both methods. At a cutoff of 264 leukocytes/µL, sensitivity and specificity of microscopy were 88% (positive and negative likelihood ratio: 7.2 and 0.1, respectively). The commonly used cutoff of 10 leukocytes/µL had a poor specificity (36%) and a sensitivity of 100%.

CONCLUSIONS

The degree of pyuria can help to distinguish UTI in older women from ASB and asymptomatic controls with pyuria. Current pyuria cutoffs are too low and promote inappropriate UTI diagnosis in older women. Clinical Trials Registration. International Clinical Trials Registry Platform: NL9477 (https://trialsearch.who.int/Trial2.aspx?TrialID=NL9477).

Predictive Value of Urinalysis and Recent Antibiotic Exposure to Distinguish Between Bacteriuria, Candiduria, and No-Growth Urine

PURPOSE

Urinary tract infections are diagnosed by clinical symptoms and detection of causative uropathogen. Antibiotics are usually not indicated in candiduria and no-growth urine. We aimed to develop a predictive score to distinguish bacteriuria, candiduria, and no-growth urine, and to describe the distribution of microorganisms in urine.

PATIENTS AND METHODS

A single-center, retrospective cohort study was conducted between January 2017 and November 2017. Patients with concomitant urinalysis and urine culture were randomly sorted for a clinical prediction model. Multivariable regression analysis was performed to determine factors associated with bacteriuria, candiduria, and no-growth urine. A scoring system was constructed by rounding the regression coefficient for each predictor to integers. Accuracy of the score was measured by the concordance index (c-index).

RESULTS

There were 8091 positive urine cultures: bacteria 85.6%, Candida 13.7%. Randomly selected cases were sorted into derivation and validation cohorts (448 cases and 272 cases, respectively). Numerous yeast on urinalysis predicted candiduria with complete accuracy; therefore, it was excluded from a score construction. We developed a NABY score based on: positive nitrite, 1 point; Antibiotic exposure within 30 days, -2 points; numerous Bacteria in urine, 2 points; few Yeast in urine, -2 points; moderate Yeast in urine, -5 points. The c-index was 0.85 (derivation) and 0.82 (validation). A score ≥0 predicted 76% and 54% of bacteriuria in the derivation and validation cohorts, respectively. A score ≤-3 predicted 96% of candiduria in both cohorts.

CONCLUSION

Numerous yeast on urinalysis and the NABY score may help identify patients with a low risk of bacteriuria in whom empiric antibiotics for UTIs can be avoided.

Automated urine sediment analyzers underestimate the severity of hematuria in glomerular diseases

Hematuria, either glomerular or extraglomerular, is defined as 3 or more red blood cells (RBCs)/high power field. Currently, urinalyses are commonly performed using automated urine sediment analyzers. To assess whether RBC counting by automated urine sediment analyzers is reliable for defining hematuria in glomerular disease, random specimen urinalyses of men with nephritic glomerular disease (7674 urinalyses) and bladder cancer (12,510 urinalyses) were retrospectively reviewed. Urine RBCs were counted by an automated urine sediment analyzer based on flow cytometry (UF-1000i, Sysmex Corporation) or digital image analysis (Cobas 6500, Roche Diagnostics GmbH). In about 20% of urine specimens, the specific gravity was less than 1.010, making the RBC counts unreliable. In the urine specimens with specific gravity ≥ 1.010, RBC counts measured using either UF-1000i or Cobas 6500 were well correlated with the positive grades in the dipstick blood test. However, at a trace, 1+, or higher positive dipstick tests for blood, RBC counts were graded significantly lower in glomerular disease than in bladder cancer. The findings suggest that RBC counting by UF-1000i or Cobas 6500 underestimates the severity of hematuria in glomerular disease, possibly because dysmorphic RBCs in glomerular disease are susceptible to hemolysis and/or fail to be properly recognized.

How Reliable Is Automated Urinalysis in Acute Kidney Injury?

OBJECTIVE

Examination of urine sediment is crucial in acute kidney injury (AKI). In such renal injury, tubular epithelial cells, epithelial cell casts, and dysmorphic red cells may provide clues to etiology. The aim of this study was to compare automated urinalysis findings with manual microscopic analysis in AKI.

METHODS

Samples from patients diagnosed with AKI and control patients were included in the study. Red blood cells, white blood cells, renal tubular epithelial cells/small round cells, casts, and pathologic (path) cast counts obtained microscopically and by a UF1000i cytometer were compared by Spearman test. Logistic regression analysis was used to assess the ability to predict AKI from parameters obtained from the UF1000i.

RESULTS

There was poor correlation between manual and automated analysis in AKI. None of the parameters could predict AKI using logistic regression analysis. However, the increment in the automated path cast count increased the odds of AKI 93 times.

CONCLUSION

Automated urinalysis parameters are poor predictors of AKI, and there is no agreement with manual microscopy.