Performance evaluation of the new fully automated urine particle analyser UF-5000 compared to the reference method of the Fuchs-Rosenthal chamber

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.

Acoustofluidic-based microscopic examination for automated and point-of-care urinalysis

Urinalysis is a heavily used diagnostic test in clinical laboratories; however, it is chronically held back by urine sediment microscopic examination. Current instruments are bulky and expensive to be widely adopted, making microscopic examination a procedure that still relies on manual operations and requires large time and labor costs. To improve the efficacy and automation of urinalysis, this study develops an acoustofluidic-based microscopic examination system. The system utilizes the combination of acoustofluidic manipulation and a passive hydrodynamic mechanism, and thus achieves a high throughput (1000 μL min-1) and a high concentration factor (95.2 ± 2.1 fold) simultaneously, fulfilling the demands for urine examination. The concentrated urine sample is automatically dispensed into a hemocytometer chamber and the images are then analyzed using a machine learning algorithm. The whole process is completed within 3 minutes with detection accuracies of erythrocytes and leukocytes of 94.6 ± 3.5% and 95.1 ± 1.8%, respectively. The examination outcome of urine samples from 50 volunteers by this device shows a correlation coefficient of 0.96 compared to manual microscopic examination. Our system offers a promising tool for automated urine microscopic examination, thus it has potential to save a large amount of time and labor in clinical laboratories, as well as to promote point-of-care urine testing applications in and beyond hospitals.

Enhancing clinical decision-making: Sysmex UF-5000 as a screening tool for bacterial urinary tract infection in children

BACKGROUND

A rapid screening test for urinary tract infections (UTIs) in children is needed to avoid unnecessary cultures and provide prompt reports to make appropriate clinical decisions. We have evaluated for the first time the performance of the Sysmex UF-5000 flow cytometer as a screening tool for UTIs in children.

METHODS

This study included 4445 pediatric patients, with urinary sediment and urine culture data collected from January 2020 to September 2023. The Sysmex UF-5000 analyzer was utilized to measure urine white blood cell (WBC) and bacteria (BACT), with the findings being compared to the culture results.

RESULTS

At ≥ 104 colony-forming unit (CFU)/mL, 513 samples were culture-positive (400 samples presented 104-105 CFU/mL, and 113 demonstrated ≥ 105 CFU/mL bacterial growth). Optimal indicators for positive cultures were BACT counts of 92.2/μL (AUC: 0.944) and WBC counts of 40.8/μL (AUC:0.863). False negative rate were 0.9% when using a 7.8 bacteria/μL cut-off and avoiding unnecessary cultures in 28.1%. The UF-5000 has a higher consistency rate for Gram-negative (GN) bacteria (90.3%) than Gram-positive (GP) bacteria (86.8%). For samples with 105 CFU/mL, UF-5000's Bacteria -Information flags showed superior concordance for samples with 104-105 CFU/mL bacteria.

CONCLUSIONS

Screening pediatric urine cultures with the UF-5000 showed potential application value in identifying negative cultures and significant bacterial growth, although performance may vary depending on the study population. Furthermore, detecting Gram typing aids in guiding early clinical empirical medication, particularly for UTIs caused by GN bacteria.

AI Driven Lab-on-Chip Cartridge for Automated Urinalysis

After haematology, urinalysis is the most common biological test performed in clinical settings. Hence, simplified workflow and automated analysis of urine elements are of absolute necessities. In the present work, a novel lab-on-chip cartridge (Gravity Sedimentation Cartridge) for the auto analysis of urine elements is developed. The GSC consists of a capillary chamber that uptakes a raw urine sample by capillary force and performs particles and cells enrichment within 5 min through a gravity sedimentation process for the microscopic examination. Centrifugation, which is necessary for enrichment in the conventional method, was circumvented in this approach. The AI100 device (Image based autoanalyzer) captures microscopic images from the cartridge at 40x magnification and uploads them into the cloud. Further, these images were auto-analyzed using an AI-based object detection model, which delivers the reports. These reports were available for expert review on a web-based platform that enables evidence-based tele reporting. A comparative analysis was carried out for various analytical parameters of the data generated through GSC (manual microscopy, tele reporting, and AI model) with the gold standard method. The presented approach makes it a viable product for automated urinalysis in point-of-care and large-scale settings.

Laboratory Tests, Bacterial Resistance, and Treatment Options in Adult Patients Hospitalized with a Suspected Urinary Tract Infection

Patients treated for systemic urinary tract infections commonly have nonspecific presentations, and the specificity of the results of the urinalysis and urine cultures is low. In the following narrative review, we will describe the widespread misuse of urine testing, and consider how to limit testing, the disutility of urine cultures, and the use of antibiotics in hospitalized adult patients. Automated dipstick testing is more precise and sensitive than the microscopic urinalysis which will result in false negative test results if ordered to confirm a positive dipstick test result. There is evidence that canceling urine cultures if the dipstick is negative (negative leukocyte esterase, and nitrite) is safe and helps prevent the overuse of urine cultures. Because of the side effects of introducing a urine catheter, for patients who cannot provide a urine sample, empiric antibiotic treatment should be considered as an alternative to culturing the urine if a trial of withholding antibiotic therapy is not an option. Treatment options that will decrease both narrower and wider spectrum antibiotic use include a period of watching and waiting before antibiotic therapy and empiric treatment with antibiotics that have resistance rates > 10%. Further studies are warranted to show the option that maximizes patient comfort and safety.

Assessing Concordance of Results: A Comparative Study of the Manual and Automated Urinalysis Methods

Introduction

An accurate urine analysis is a good indicator of the status of the renal and genitourinary system. However, limited studies have been done on comparing the diagnostic performance of the fully automated analyser and manual urinalysis especially in Ghana. This study evaluated the concordance of results of the fully automated urine analyser (Sysmex UN series) and the manual method urinalysis at the Komfo Anokye Teaching Hospital in Kumasi, Ghana. Methodology. Sixty-seven (67) freshly voided urine samples were analysed by the automated urine analyser Sysmex UN series and by manual examination at Komfo Anokye Teaching Hospital, Ghana. Kappa and Bland-Altman plot analyses were used to evaluate the degree of concordance and correlation of both methods, respectively.

Results

Substantial (κ = 0.711, p < 0.01), slight (κ = 0.193, p = 0.004), and slight (κ = 0.109, p < 0.001) agreements were found for urine colour, appearance, and pH, respectively, between the manual and automated methods. A strong and significant correlation (r = 0.593, p < 0.001) was found between both methods for specific gravity with a strong positive linear correlation observed for red blood cell count (r = 0.951, R2 = 0.904, p < 0.001), white blood cell count (r = 0.907, R2 = 0.822, p < 0.001), and epithelial cell count (r = 0.729, R2 = 0.532, p < 0.001). A perfect agreement of urine chemistry results in both methods was observed for nitrite 67 (100%) (κ = 1.000, p < 0.001) with a fair agreement for protein 46 (68.7%) (κ = 0.395, p < 0.001). A strong agreement was found in both methods for the presence of cast 65 (97.0%) (κ = 0.734, p < 0.001) with no concordance observed for the presence of crystals (κ = 0.115, p = 0.326) and yeast-like cells (YLC) (κ = 0.171, p = 0.116).

Conclusion

The automated and manual methods showed similar performances and good correlation, especially for physical and chemical examination. However, manual microscopy remains necessary to classify urine sediments, particularly for bacteria and yeast-like cells. Future research with larger samples could help validate automated urinalysis for wider clinical use and identify areas requiring improved automated detection capabilities.

Analysis of factors with low positive predictive value in the diagnosis of urinary tract infection by flow cytometry

PURPOSE

Culture-negative urine specimens can be rapidly screened by urine flow cytometry (UFC), while low positive predictive value (PPV) limits the clinical application. We explored the factors associated with a low PPV.

METHODS

A total of 5095 urine specimens were analyzed with UFC and culture. Diagnostic performance of leukocytes, bacteria, and BACT-info flags was evaluated by sensitivity, specificity, PPV, and negative predictive value (NPV). The association of contaminated culture and squamous epithelial cell count and BACT-info flag was performed by logistic regression analysis.

RESULTS

The NPVs of parallel combination of bacteria and leucocytes were 98.9% in males and 97.9% in females, and PPVs of serial combination were 86.6% and 77.8% in men and women, respectively. The PPV of Gram-negative flag was higher than that of Gram-positive flag. The proportions of contamination in the urine culture results of false positive specimens were 86.9% in males and 98.5% in females at the cutoff points of the serial combination, and these parameters were 53.2% in males and 85.6% in females for the Gram-positive flag. There was a statistically significant association between contaminated cultures and squamous epithelial cells count in females, but not in males. Associations between contaminated cultures and Gram-positive flags or Gram-pos/-neg flags were statistically significant, but there was no association between contaminated cultures and Gram-negative flags.

CONCLUSIONS

A serial combination of leukocytes and bacteria may maximize PPV in the diagnosis of bacterial urinary tract infection by urine flow cytometry, and contamination is the main reason for a low PPV.

Urine transfer devices may impact urinary particle results: a pre-analytical study

OBJECTIVES

Well-standardized procedures in the pre-analytical phase of urine diagnostics is of utmost importance to obtain reliable results. We investigated the effect of different urine collection methods and the associated urine transfer tubes on urine test strip and particle results.

METHODS

In total, 146 selected urine samples were subdivided into three different collection containers and subsequently transferred into its accompanying transfer tube (BD, Greiner, Sarstedt vacuum and Sarstedt aspiration). As reference, the original urine sample was directly measured on the analyser. Both chemical test strip analysis (Sysmex UC-3500) and fluorescence flow cytometry particle analysis (Sysmex UF-5000) were performed on all samples.

RESULTS

No statistically significant differences in test strip results were found between the studied transfer methods. On the contrary, transfer of urine samples to the secondary tubes affected their particle counts. Clinically significant reductions in counts of renal tubular epithelial cells and hyaline casts were observed using the BD and Greiner transfer tubes and in counts of pathological casts using the BD, Greiner and Sarstedt vacuum tubes.

CONCLUSIONS

The results of this study indicate that the use of urine transfer tubes may impact counts of fragile urine particles. Clinical laboratories need to be aware about the variation that urine collection methods can induce on urine particle counts.

[Urinalysis in dogs and cats, part 2: Urine sediment analysis]

Examination of the urine sediment is part of a routine urinalysis and is undertaken in order to identify insoluble particles in the urine. This procedure is mainly used in the context of diagnostic evaluation of urinary tract diseases, but may also be useful for the diagnosis of systemic diseases and intoxications. Analysis of fresh urine is recommended as changes in cell morphology, cell lysis and in vitro crystal formation may occur in the course of its storage. Manual urine sediment analysis is still performed in many veterinary practices. Native wet-mount preparations are suitable for the identification and quantification of urine sediment particles. The examination of stained wet-mount preparations or air-dried smears may be necessary to further differentiate cells and to identify bacteria. For several years, automatic urine sediment analyzers have been available in veterinary medicine. These save considerable time and staff resources, however verification of the automatically generated results by an experienced observer remains necessary. Urine sediment particles that are frequently identified and clinically relevant include red blood cells, white blood cells, different types of epithelial cells, crystals, and casts as well as bacteria. Furthermore, parasite eggs, fungal hyphae, lipid droplets, spermatozoa, fibres, hair, mucus, plant parts or environmental contaminations may be found in the urine sediment and result in a complication of the result interpretation.

Comparison of the clinical performance of the Atyp.C parameter of the UF-5000 fully automated urine particle analyzer with that of microscopic urine sediment analysis

a Objectives

Urinalysis is one of the most common laboratory screening tests to detect problems in the renal and urinary system; however, they cannot detect atypical cells (Atyp.Cs). The Sysmex UF-5000, a fully automated urine particle analyzer, can detect Atyp.Cs via its Atyp.C parameter. This study aimed to compare the clinical value of the Atyp.C parameter with that of urine sediment microscopy.

b Method

A total of 471 leftover urine samples were submitted to the Department of Clinical Laboratory at the University of Tokyo Hospital for urinalysis by manual sediment microscopy examination and UF-5000 Atyp.C analysis.

c Result

Of 471 submitted samples, 117 were positive for Atyp.Cs by urine sediment and 354 samples were negative. The histological subtypes of the Atyp.Cs included 105 cases of suspected urothelial carcinoma cells, 10 suspected squamous carcinoma cells, and 2 of suspected adenocarcinoma cells. The Atyp.C values for the Atyp.C-positive and -negative groups were 2.64 ± 0.69 and 0.38 ± 0.16, respectively. The optimal Atyp.C cutoff value determined by the receiver operating characteristic curve analysis was 0.4/μL. The area under the curve was 0.856, with a sensitivity of 79.5% and specificity of 85.1%. Atyp.C values of the UF-5000 showed high predictive performance for Atyp.C-positive specimens identified by urine sediment microscopy.

d Conclusions

This study shows that a combination of UF-5000 analysis and microscopic examination of urine sediment improves Atyp.C detection in urine sediment analysis. These results suggest that Atyp.C measured by UF-5000 could be a useful screening parameter in routine testing of urine samples.

Pitfalls in the diagnosis of hematuria

Detection of hemoglobin (Hb) and red blood cells in urine (hematuria) is characterized by a large number of pitfalls. Clinicians and laboratory specialists must be aware of these pitfalls since they often lead to medical overconsumption or incorrect diagnosis. Pre-analytical issues (use of vacuum tubes or urine tubes containing preservatives) can affect test results. In routine clinical laboratories, hematuria can be assayed using either chemical (test strips) or particle-counting techniques. In cases of doubtful results, Munchausen syndrome or adulteration of the urine specimen should be excluded. Pigmenturia (caused by the presence of dyes, urinary metabolites such as porphyrins and homogentisic acid, and certain drugs in the urine) can be easily confused with hematuria. The peroxidase activity (test strip) can be positively affected by the presence of non-Hb peroxidases (e.g. myoglobin, semen peroxidases, bacterial, and vegetable peroxidases). Urinary pH, haptoglobin concentration, and urine osmolality may affect specific peroxidase activity. The implementation of expert systems may be helpful in detecting preanalytical and analytical errors in the assessment of hematuria. Correcting for dilution using osmolality, density, or conductivity may be useful for heavily concentrated or diluted urine samples.

Rapid Antibiotic Susceptibility Testing of Gram-Negative Bacteria Directly from Urine Samples of UTI Patients Using MALDI-TOF MS

Urinary tract infections (UTIs) are one of the most common human infections and are most often caused by Gram-negative bacteria such as Escherichia coli. In view of the increasing number of antibiotic-resistant isolates, rapidly initiating effective antibiotic therapy is essential. Therefore, a faster antibiotic susceptibility test (AST) is desirable. The MALDI-TOF MS-based phenotypic antibiotic susceptibility test (MALDI AST) has been used in blood culture diagnostics to rapidly detect antibiotic susceptibility. This study demonstrates for the first time that MALDI AST can be used to rapidly determine antibiotic susceptibility in UTIs directly from patients' urine samples. MALDI-TOF MS enables the rapid identification and AST of Gram-negative UTIs within 4.5 h of receiving urine samples. Six urinary tract infection antibiotics, including ciprofloxacin, cotrimoxazole, fosfomycin, meropenem, cefuroxime, and nitrofurantoin, were analyzed and compared with conventional culture-based AST methods. A total of 105 urine samples from UTI patients contained bacterial isolates for MALDI AST. The combination of ID and AST by MALDI-TOF allowed us to interpret the result according to EUCAST guidelines. An overall agreement of 94.7% was found between MALDI AST and conventional AST for the urinary tract pathogens tested.

Combination of UC-3500 and UF-5000 as a quick and effective method to exclude bacterial urinary tract infection

BACKGROUND

Our study aims to evaluate the performance of the combination of Sysmex urine dry chemistry analyzer UC-3500 and urine particle analyzer UF-5000 in screening bacterial urinary tract infection (UTI).

METHODS

We analyzed 2000 urine specimens from patients with suspected UTI by using a urine dry chemistry analyzer (UC-3500) and a fully automated sediment analyzer (UF-5000). After being tested by the instrument, all specimens were sent to our clinical microbiology laboratory for culture. In addition, 600 urine specimens were selected to evaluate the accuracy of the six screening strategies established in this study.

RESULTS

The consistency of UF-5000 bacterial classification and bacterial culture was fair (Kappa = 0.339). The counts of WBC and BACT elevated with sequential group designs (P < 0.001). The cut-off value of WBC was 32.20/μL for males (AUC, 0.942, 95%CI, 0.930-0.955) and 39.15/μL for females (AUC, 0.931, 95%CI, 0.914-0.948). The sensitivity and specificity of WBC were relatively higher than those of BACT. Strategy④ and Strategy⑥ in all six strategies had a good negative predictive value (NPV) which was 98.73%.

CONCLUSION

UF-5000 bacterial classification cannot be used as a practical reference. 32.20/μL (male) and 39.15/μL (female) for WBC as well as 22.35/μL (male) and 127.25/μL (female) for BACT were used as cut-off values to effectively determine whether UTI occurs. WBC, BACT and LEU joint screening programs were suitable to rapidly and effectively exclude bacterial UTI.

Accuracy of the Sysmex UF-5000 analyzer for urinary tract infection screening and pathogen classification

The screening performance of urine flow cytometry parameters (e.g., white blood cell and bacteria) for urinary tract infection (UTI) has been widely recognized. The majority of previous studies, however, investigated the screening performance of Sysmex UF-1000i urine flow cytometer. This study aimed to investigate the screening performance of Sysmex UF-5000 analyzer, a third-generation urinary flow cytometer, for UTI and its novel parameter named Gram flag for discriminating gram-positive and negative pathogens. Urine specimens sent to the clinical microbiology laboratory of our hospital for bacterial culture between September 13, 2021, and November 15, 2021, were prospectively and consecutively collected. The Sysmex UF-5000 analyzer was used to determine urine white blood cell (WBC) and bacteria simultaneously. A chemical strip was used to assess urine nitrate. UTI was defined as positive urine bacterial culture > 104 CFU /ml. The receiver operating characteristics (ROC) curve, nomogram, decision tree, and decision curve were used to determine the screening performance of urine WBC, nitrate, and bacterial. A total of 246 UTIs and 425 non-UTIs were enrolled. The areas under the ROC curve (AUCs) for WBC and bacterial were 0.74 and 0.86, respectively. The decision curve showed that urine bacteria had a higher benefit than WBC. The nomogram indicated that urine bacterial had the largest effect on the probability of UTI. The sensitivity and specificity of the decision tree were 0.69 and 0.95, respectively. The flag of Gram-negative had a positive predictive value (PPV) of 0.93 in patients with urine bacteria > 1367 /μl. Therefore, we conclude that urine bacteria determined by the Sysmex UF-5000 had higher screening performance and greater benefit than WBC. The decision tree can be used to improve the screening performance of routine urinary parameters. The flag of Gram-negative is a reliable indicator to confirm gram-negative bacteria infection in UTI patients.

Comparison of the Anvajo Vet Fluidlab 1 urine sediment analyzer to manual microscopy and Idexx SediVue analysis for analysis of urine samples from cats and dogs

The Vet Fluidlab 1 (Anvajo), a new urine sediment analyzer for use in veterinary medicine, uses holographic microscopy to detect urine sediment particles in uncentrifuged urine. We compared the performance of the Fluidlab to manual microscopy and Idexx SediVue analysis for the detection of RBC, WBC, epithelial cells (EC), struvite crystals (STR), all crystals (CRY), and casts (CST) in urine samples from cats and dogs. The performance of the Fluidlab for the detection of bacteria was compared to bacterial culture. We included 624 urine samples from feline (238; 38%) and canine (386; 62%) patients; 227 samples had been submitted for bacterial culturing. The sensitivity of the Fluidlab compared to manual microscopy was 92.1% for RBC, 90.1% for WBC, 87.5% for EC, 67.6% for STR, 53.9% for CRY, and 12.5% for CST. Specificity was >97% for STR and CST, 90.0% for CRY, 78.4% for WBC, 59.4% for EC, and 55.1% for RBC. Sensitivities and specificities of the Fluidlab for analytes compared to manual microscopy were found to be similar to those obtained by the Fluidlab compared to SediVue analysis. Miscellaneous materials (e.g., lipid droplets, sperm, cell detritus) seemed to be the main reason for the high false-positive rate in RBC and EC classification by the Fluidlab. Detection of bacteria by the Fluidlab compared to bacterial culture had a sensitivity of 89.8% and a specificity of 72.3%. The performance of the Fluidlab is acceptable for the detection of WBC and bacteria; sensitivity for the detection of CRY and CST, and specificity for the detection of RBC and EC, require improvement.

Improving clinical performance of urine sediment analysis by implementation of intelligent verification criteria

OBJECTIVES

Urinary test strip and sediment analysis integrated with intelligent verification criteria can help to select samples that need manual review. This study aimed to evaluate the improvement in the diagnostic performance of combined urinary test strip and urinary sediment analysis using intelligent verification criteria on the latest generation automated test strip and urinary fluoresce flow cytometry (UFFC) analysers.

METHODS

Urine test strip and sediment analysis were performed using the Sysmex UC-3500 and UF-5000 (Kobe, Japan) on 828 urinary samples at the clinical laboratory of the Ghent University Hospital. The results were compared to manual microscopy using phase-contrast microscopy as a reference. After the application of the intelligent verification criteria, we determined whether the diagnostic performance of urine sediment analysis could be improved.

RESULTS

Application of intelligent verification criteria resulted in an increase in specificity from 88.5 to 96.8% and from 88.2 to 94.9% for red blood cells and white blood cells, respectively. Implementing review rules for renal tubular epithelial cells and pathological casts increased the specificity from 66.7 to 74.2% and from 96.2 to 100.0%, respectively; and improved the diagnostic performance of urinary crystals and atypical cells.

CONCLUSIONS

The implementation of review rules improved the diagnostic performance of UFFC, thereby increasing the reliability and quality of urine sediment results.

A novel approach to screening and managing the urinary tract infections suspected sample in the general human population

Background

Automated urine technology providing standard urinalysis data can be used to support clinicians in screening and managing a UTI-suspected sample. Fully automated urinalysis systems have expanded in laboratory practice. Commonly used were devices based on digital imaging with automatic particle recognition, which expresses urinary sediment results on an ordinal scale. There were introduced fluorescent flow cytometry analyzers reporting all parameters quantitatively. There is a need to harmonize the result and support comparing bacteria and WBC qualitative versus semiquantitative results.

Methods

A total of 1,131 urine samples were analyzed on both automated urinalysis systems. The chemical components of urinalysis (leukocyte esterase and nitrate reductase) and the sediment results (leukocytes and bacteria) were investigated as potential UTI indicators. Additionally, 106 specimens were analyzed on UF-5000 and compared with culture plating to establish cut-offs that can be suitable for standard urinalysis requirements and help to guide on how to interpret urinalysis results in the context of cultivation reflex.

Results

The medians of bacteria counts varies from 16.2 (absence), 43.0 (trace), 443.5 (few), 5,389.2 (moderate), 19,356.6 (many) to 32,545.2 (massive) for particular digital microscopic bacteriuria thresholds. For pyuria thresholds, the medians of WBC counts varies from 0.8 (absence), 2.0 (0-1), 7.7 (2-3), 21.3 (4-6), 38.9 (7-10), 61.3 (11-15) to 242.2 (>30). Comparing the culture and FFC data (bacterial and/or WBC counts) was performed. Satisfactory sensitivity (100%), specificity (83.7%), negative predictive value (100%), and positive predictive value (75%) were obtained using indicators with the following cut-off values: leukocytes ≥40/µl or bacteria ≥300/µl.

Conclusions

Accurate urinalysis gives information about the count of bacteria and leukocytes as useful indicators in UTIs, in general practice it can be a future tool to cross-link clinical and microbiology laboratories. However, the cut-off adjustments require individual optimization.

Searching for the urine osmolality surrogate: an automated machine learning approach

OBJECTIVES

Automated machine learning (AutoML) tools can help clinical laboratory professionals to develop machine learning models. The objective of this study was to develop a novel formula for the estimation of urine osmolality using an AutoML tool and to determine the efficiency of AutoML tools in a clinical laboratory setting.

METHODS

Three hundred routine urinalysis samples were used for reference osmolality and urine clinical chemistry analysis. The H2O AutoML engine completed the machine learning development steps with minimum human intervention. Four feature groups were created, which include different urinalysis measurements according to the Boruta feature selection algorithm. Method comparison statistics including Spearman correlation, Passing-Bablok regression analysis were performed, and Bland Altman plots were created to compare model predictions with the reference method. The minimum allowable bias (24.17%) from biological variation data was used as the limit of agreement.

RESULTS

The AutoML engine developed a total of 183 ML models. Conductivity and specific gravity had the highest variable importance. Models that include conductivity, specific gravity, and other urinalysis parameters had the highest R² (0.70-0.83), and 70-84% of results were within the limit of agreement.

CONCLUSIONS

Combining urinary conductivity with other urinalysis parameters using validated machine learning models can yield a promising surrogate. Additionally, AutoML tools facilitate the machine learning development cycle and should be considered for developing ML models in clinical laboratories.

Evaluation of the Atellica® UAS 800: a new member of the automated urine sediment analyzer family

BACKGROUND

In 2017 the Atellica^® UAS 800 urine sediment analyzer was introduced by Siemens Healthineers. We investigated its applicability in the standardization and automation of the laboratory urinalysis workflow, including the prediction of urine culture outcome and glomerular pathology.

METHODS

We evaluated the performance characteristics of the Atellica^® UAS 800 and its correlation with the iQ200 (Beckman Coulter). In addition, we studied the agreement between Atellica^® UAS 800 and CLINITEK Novus^® and determined the predictive value of bacteria and leukocyte counts for urine culture outcome. Furthermore, we investigated the ability of Atellica^® UAS 800 to identify pathological casts and dysmorphic erythrocytes in comparison to manual microscopy.

RESULTS

Erythrocyte and leukocyte analyses indicated high intra- and inter-run precisions and good correlations with the iQ200. We found that the Atellica^® UAS 800 detects bacteria with higher sensitivity than the iQ200. The Atellica^® UAS 800 and CLINITEK Novus^® showed a high degree of conformity. We determined seven combinations of clinical cut-off values of bacteria and leukocytes for predicting urine culture outcome with sensitivity, specificity, and negative predictive values of 95%, 52%, and 93%, respectively. Using the Atellica^® UAS 800, hyaline casts, erythrocyte casts, leukocyte casts, and dysmorphic erythrocytes were correctly recognized in 76%, 22%, 2%, and 39% of the samples, respectively.

CONCLUSIONS

The Atellica^® UAS 800 is a robust, fast, and user-friendly analyzer, which accurately quantifies erythrocytes, leukocytes, bacteria and squamous epithelial cells, and may be utilized for predicting positive urine cultures. The detection of clinically important pathological casts and dysmorphic erythrocytes proved insufficient.

Performance evaluation of automated cell counts compared with reference methods for body fluid analysis

OBJECTIVES

Cellular analysis of body fluids (BFs) can assist clinicians for the diagnosis of many medical conditions. The aim of this work is the evaluation of the analytical performance of the UF-5000 body fluid mode (UF-BF) analyzer compared to the gold standard method (optical microscopy, OM) and to XN-1000 (XN-BF), another analyzer produced by the same manufacturer (Sysmex) and with a similar technology for BF analysis.

METHODS

One hundred BF samples collected in K₃EDTA tubes were analyzed by UF-BF, XN-BF and OM. The agreement was evaluated using Passing and Bablok regression and Bland-Altman plot analysis. The receiver operating characteristic (ROC) curves were selected for evaluating the diagnostic agreement between OM classification and UF-BF parameters.

RESULTS

Comparison between UF-BF and OM, in all BF types, showed Passing and Bablok's slope comprised between 0.99 (polymorphonuclear cells count, PMN-BF) and 1.39 (mononuclear cells count, MN-BF), the intercepts ranged between 26.47 (PMN-BF parameter) and 226.80 (white blood cell count). Bland-Altman bias was comprised between 7.3% (total cell count, TC-BF) and 52.9% (MN-BF). Comparison between UF-BF and XN-BF in all BF showed slopes ranged between 1.07 (TC-BF and PMN-BF) and 1.16 (MN-BF), intercepts ranged between 8.30 (PMN) and 64.78 (WBC-BF). Bland-Altman bias ranged between 5.8 (TC-BF) and 21.1% (MN-BF). The ROC curve analysis showed an area under the curve ranged between 0.9664 and 1.000.

CONCLUSIONS

UF-BF shows very good performance for the differential counts of cells in ascitic, pleural and synovial fluids and therefore it is useful to screen and count cells in this type of BF.

Evaluation of the sysmex UF-5000 fluorescence flow cytometer as a screening platform for ruling out urinary tract infections in elderly patients presenting at the Emergency Department

In this study, we evaluated the performance of the flow cytometer-based Sysmex UF-5000 automated urine analyzer as a screening tool for ruling out urinary tract infections in elderly patients presenting at the emergency department. A total of 1119 unselected patient samples (including 544 samples from elderly patients) submitted for urine culture were included in this study. Samples were measured on UF-5000 and dipsticks and the results were compared with interpretation of culture results, which is the gold standard. We obtained a diagnostic sensitivity of 99% and specificity of 51% with a low rate of false negatives (0.2%) and a negative predictive value of 99% at 10⁸ colony forming bacteria/L (CFB/L). A bacterial count ≥ 50x10⁶/L or yeast like cells ≥ 25x10⁶/L was used as the cutoff value. At this cutoff value, 30% of the urine cultures would have been redundant. This resulted in 35% false positive samples, mainly due to particle contamination or nongrowing bacteria. In comparison, at best, the dipsticks have a diagnostic sensitivity of 89%, a specificity of 52% and a negative predictive value of 92% at 10⁸ CFB/L.

Rapid diagnosis and reduced workload for urinary tract infection using flowcytometry combined with direct antibiotic susceptibility testing

BACKGROUND

We evaluated if flowcytometry, using Sysmex UF-5000, could improve diagnosis of urinary tract infections by rapid identification of culture negative and contaminated samples prior to culture plating, thus reducing culture plating workload and response time. We also evaluated if it is possible to reduce the response time for antibiotic susceptibility profiles using the bacteria information flag on Sysmex UF-5000 to differentiate between Gram positive and negative bacteria, followed by direct Antibiotic Susceptibility Testing (dAST) on the positive urine samples.

METHODS

One thousand urine samples were analyzed for bacteria, white blood cells and squamous cells by flowcytometry before culture plating. Results from flowcytometric analysis at different cut-off values were compared to results of culture plating. We evaluated dAST on 100 urine samples that were analyzed as positive by flowcytometry, containing either Gram positive or Gram negative bacteria.

RESULTS

Using a cut-off value with bacterial count ≥100.000/mL and WBCs ≥10/μL, flowcytometry predicted 42,1% of samples with non-significant growth. We found that most contaminated samples contain few squamous cells. For 52/56 positive samples containing Gram negative bacteria dAST was identical to routine testing. Overall, there was concordance in 555/560 tested antibiotic combinations.

CONCLUSION

Flowcytometry offers advantages for diagnosis of urinary tract infections. Screening for negative urine samples on the day of arrival reduces culture plating and workload, and results in shorter response time for the negative samples. The bacteria information flag predicts positive samples containing Gram negative bacteria for dAST with high accuracy, thus Antibiotic Susceptibility Profile can be reported the day after arrival. For the positive samples containing Gram negative bacteria the concordance was very good between dAST and Antibiotic Susceptibility Testing in routine. For positive samples containing Gram positive bacteria the results were not convincing. We did not find any correlation between epithelial cells and contamination.

Renal tubular epithelial cells add value in the diagnosis of upper urinary tract pathology

Background Diagnosis of upper urinary tract infections (UTI) is challenging. We evaluated the analytical and diagnostic performance characteristics of renal tubular epithelial cells (RTECs) and transitional epithelial cells (TECs) on the Sysmex UF-5000 urine sediment analyzer. Methods Urinary samples from 506 patients presenting with symptoms of a UTI were collected. Only samples for which a urinary culture was available were included. Analytical (imprecision, accuracy, stability and correlation with manual microscopy) and diagnostic performance (sensitivity and specificity) were evaluated. Results The Sysmex UF-5000 demonstrated a good analytical performance. Depending on the storage time, storage conditions (2-8 °C or 20-25 °C) and urinary pH, RTECs and TECs were stable in urine for at least 4 h. Using Passing-Bablok and Bland-Altman analysis, an acceptable agreement was observed between the manual and automated methods. Compared to TECs, RTECs demonstrated an acceptable diagnostic performance for the diagnosis of upper UTI. Conclusions While TECs do not seem to serve as a helpful marker, increased urinary levels of RTECs add value in the diagnosis of upper UTI and may be helpful in the discrimination between upper and lower UTIs.

A performance comparison of the fully automated urine particle analyzer UF-5000 with UF-1000i and Gram staining in predicting bacterial growth patterns in women with uncomplicated urinary tract infections

Background

The aim of this study was to compare the performance of the new flow cytometer UF-5000 with the UF-1000i and Gram staining for determining bacterial patterns in urine samples.

Methods

Women who attended our clinic with symptoms suggestive of urinary tract infection were enrolled in the study. Mid-stream urine samples were collected for gram staining, urine analysis and urine cultures. Bacterial patterns were classified using the UF-1000i (none, cocci bacteria or rods/mixed growth), the UF-5000 (none, cocci, rods or mixed growth) and Gram staining.

Results

Among the 102 included samples, there were 10 g-positive cocci, 2 g-positive bacilli, 66 g-negative rods, and 24 mixed growth. The sensitivity/specificity of the UF-1000i was 81.8/91.1% for gram-negative rods and 23.5/96.9% for cocci/mixed. The sensitivity/specificity of the UF-5000 was 80.0/88.2% for gram negative rods and 70.0/86.5% for gram-positive cocci.

Conclusions

The UF-5000 demonstrated good sensitivity and specificity for Gram-negative bacilli and demonstrated an improved sensitivity for detecting Gram-positive cocci compared with the UF-1000i.

Verification of UriSed 3 PRO automated urine microscope in regional laboratory environment

BACKGROUND AND AIMS

Ten UriSed 3 PRO automated microscopes (77 Elektronika, Hungary) were verified for nine HUSLAB laboratories with 160 000 annual urine samples.

MATERIALS AND METHODS

Particle counting of the primary UriSed 3 PRO instrument (77 Elektronika, Hungary) was verified against reference visual microscopy with 463 urine specimens, and against urine culture on chromogenic agar plates with parallel 396 specimens. Nine secondary instruments were compared pairwise with the primary instrument.

RESULTS

Relative imprecisions compared to Poisson distribution, R(CV), were estimated to be 1.0 for white blood cell (WBC) and 1.5 for red blood cell (RBC) counts, respectively. Spearman's correlations against visual microscopy were r_S = 0.94 for WBC, r_S = 0.87 for RBC, and r_S = 0.82 for squamous epithelial cell (SEC) counts. Agreement with visual microscopy (Cohen's weighted kappa) was 0.94 for WBC, 0.89 for RBC, 0.88 for SEC, 0.59 for combined casts, and 0.49 for non-squamous epithelial cells (NEC). Bacteria were detected with a sensitivity of 90% and specificity of 39 against culture at 10⁷ CFB/L (10⁴ CFU/mL). Created flagging limits allowed automated reporting for 70-75% of patient results.

CONCLUSIONS

UriSed 3 PRO instruments were adopted into routine use after acceptance of the verification.

Pneumatic tube transportation of urine samples

Objectives

Pneumatic tube transportation of samples is an effective way of reducing turn-around-time, but evidence of the effect of pneumatic tube transportation on urine samples is lacking. We thus wished to investigate the effect of pneumatic tube transportation on various components in urine, in order to determine if pneumatic tube transportation of these samples is feasible.

Methods

One-hundred fresh urine samples were collected in outpatient clinics and partitioned with one partition being carried by courier to the laboratory, while the other was sent by pneumatic tube system (Tempus600). Both partitions were then analysed for soluble components and particles, and the resulting mean difference and limits of agreement were calculated.

Results

Albumin, urea nitrogen, creatinine, protein and squamous epithelial cells were unaffected by transportation in the Tempus600 system, while bacteria, renal tubular epithelial cells, white blood cells and red blood cells were affected and potassium and sodium may have been affected.

Conclusions

Though pneumatic tube transportation did affect some of the investigated components, in most cases the changes induced were clinically acceptable, and hence samples could be safely transported by the Tempus600 pneumatic tube system. For bacteria, white blood cells and red blood cells local quality demands will determine if pneumatic tube transportation is appropriate.

Establishment of the intelligent verification criteria for a routine urinalysis analyzer in a multi-center study

Background Although laboratory information system (LIS) is widely used nowadays, the results of routine urinalysis still need 100% manual verification. We established intelligent verification criteria to perform the automated verification process and reduce manual labor. Methods A total of 4610 urine specimens were obtained from the patients of three hospitals in Beijing, China. Firstly, 895 specimens were measured to establish the reference intervals of formed-element parameters in UF5000. Secondly, 2803 specimens were analyzed for setting up the intelligent verification criteria (including the microscopic review rules and manual verification rules). Lastly, 912 specimens were used to verify the efficacy and accuracy of the intelligent verification criteria. Phase-contrast microscopes were used for the microscopic review. Results Employing a results level corresponding relationship in specific parameters including hemoglobin (red blood cell [RBC]), leukocyte esterase (white blood cell [WBC]) and protein (cast) between the dry-chemistry analysis and formed-element analysis, as well as instrument flags, we established seven WBC verification rules, eight RBC verification rules and four cast verification rules. Based on the microscopy results, through analyzing the pre-set rules mentioned earlier, we finally determined seven microscopic review rules, nine manual verification rules and three auto-verification rules. The microscopic review rate was 21.98% (616/2803), the false-negative rate was 4.32% (121/2803), the total manual verification rate was 35.71% (1001/2803) and the auto-verification rate was 64.29% (1802/2803). The validation results were consistent. Conclusions The intelligent verification criteria for urinary dry-chemistry and urinary formed-element analysis can improve the efficiency of the results verification process and ensure the reliability of the test results.

Comparison of five automated urine sediment analyzers with manual microscopy for accurate identification of urine sediment

Background While the introduction of automated urine analyzers is expected to reduce the labor involved, turnaround time and potential assay variations, microscopic examination remains the "gold standard" for the analysis of urine sediments. In this study, we evaluated the analytical and diagnostic performance of five recently introduced automated urine sediment analyzers. Methods A total of 1016 samples were examined using five automated urine sediment analyzers and manual microscopy. Concordance of results from each automated analyzer and manual microscopy were evaluated. In addition, image and microscopic review rates of each system were investigated. Results The proportional bias for red blood cells (RBCs), white blood cells (WBCs) and squamous epithelial cells in the automated urine sediment analyzers were within ±20% of values obtained using the manual microscope, except in the cases of RBCs and WBCs analyzed using URiSCAN PlusScope and Iris iQ200SPRINT, respectively. The sensitivities of Roche Cobas® u 701 and Siemens UAS800 for pathologic casts (73.6% and 81.1%, respectively) and crystals (62.2% and 49.5%, respectively) were high, along with high image review rates (24.6% and 25.2%, respectively). The detection rates for crystals, casts and review rates can be changed for the Sysmex UF-5000 platform according to cut-off thresholds. Conclusions Each automated urine sediment analyzer has certain distinct features, in addition to the common advantages of reducing the burden of manual processing. Therefore, laboratory physicians are encouraged to understand these features, and to utilize each system in appropriate ways, considering clinical algorithms and laboratory workflow.

Investigation of Atyp.C using UF-5000 flow cytometer in patients with a suspected diagnosis of urothelial carcinoma: a single-center study

BACKGROUND

This study evaluated the predictive power of Atyp.C (a parameter of UF-5000 flow cytometer) for patients with a suspected diagnosis of urothelial carcinoma.

METHODS

We analyzed 163 urine specimens from 128 patients with suspected urothelial carcinoma using a fully automated fluorescence flow cytometry analyzer (UF-5000) and evaluated its performance on identifying atypical/malignant urothelial cells. From January 1, 2019 to April 4, 2019, all consecutive specimens for urinary cytopathology were enrolled.

RESULTS

Of the specimens with urinary cytopathology, 67 specimens (41.1%) revealed abnormal findings in cytology analysis. Among them, 20 specimens (12.3%) were diagnosed as atypical urothelial cells, 26 specimens (16.0%) as suspicious for malignancy (S-malignancy), and 21 specimens (12.9%) as confirmed malignancy. The UF-5000 findings were positive in 59 specimens (36.2%); therefore, the agreement with cytopathology was 73.0%. Using follow-up histologic diagnosis of urothelial carcinoma with or without urinary tract cytology (UTCy) as a reference standard (suspicious and confirmed malignancy were the positive criteria for UTCy), the sensitivity was 59.0%, specificity was 82.1%, positive predictive value was 75.0%, negative predictive value was 68.8%, and the agreement was 71.1%.

CONCLUSIONS

It is worth knowing and reporting that the Atyp.C assay may be used as an accessory test for patients with suspected urothelial carcinoma, based on its ability to identify high-risk patients who might need closer follow-up or additional medical treatment.

Estimated urinary osmolality based on combined urinalysis parameters: a critical evaluation

Background Urinary conductivity allows a coarse prediction of urinary osmolality in most cases but is insensitive to the osmolal contribution of uncharged particles and the presence of roentgen contrast media. Urinary osmolality can be estimated on the recently introduced Sysmex UF-5000 urine analyzer using conductivity. In this study, we evaluated the analytical performance of this research parameter. Secondly, we aimed to improve the manufacturer's algorithm for estimating urinary osmolality, based on standard urinalysis parameters (creatinine, glucose, relative density). Methods The analytical performance was determined and a prediction model to estimate urinary osmolality based on urinalysis parameters was developed. We further developed and validated a prediction model using another set of routine urine samples. In addition, the influence of roentgen contrast media on urinary osmolality was studied. Results The within-run and between imprecision for osmolality and conductivity measured on the Sysmex UF-5000 ranged from 1.1% to 4.9% and 0.7% to 4.8%, respectively. Multiple regression analysis revealed urinary creatinine, conductivity and relative density to be the strongest predictors to estimate urinary osmolality. A mean difference of 1.3 mOsm/kg between measured and predicted osmolality demonstrated that the predictive performance of our model was favorable. An excellent correlation between the relative density and % contrast media was demonstrated. Conclusions Urinary osmolality is an important parameter for assessing specimen dilution in urinalysis. Urinary conductivity, along with relative density and urinary creatinine allows a coarse prediction of urinary osmolality and is insensitive to the osmolal contribution of uncharged particles and the presence of roentgen contrast media.

Performance Evaluation of Body Fluid Cellular Analysis Using the Beckman Coulter UniCel DxH 800, Sysmex XN-350, and UF-5000 Automated Cellular Analyzers

BACKGROUND

Automated cellular analyzers are expected to improve the analytical performance in body fluid (BF) analysis. We evaluated the analytical performance of three automated cellular analyzers and established optimum reflex analysis guidelines.

METHODS

A total of 542 BF samples (88 cerebrospinal fluid [CSF] samples and 454 non-CSF samples) were examined using manual counting and three automated cellular analyzers: UniCel DxH 800 (Beckman Coulter), XN-350 (Sysmex), and UF-5000 (Sysmex). Additionally, 2,779 BF analysis results were retrospectively reviewed. For malignant cell analysis, the receiver operating characteristic (ROC) curve was used, and the detection of high fluorescence-BF cells (HF-BFs) using the XN-350 analyzer was compared with cytology results.

RESULTS

All three analyzers showed good agreement for total nucleated cell (TNC) and red blood cell (RBC) counts, except for the RBC count in CSF samples using the UniCel DxH 800. However, variable degrees of differences were observed during differential cell counting. For malignant cell analysis, the area under the curve was 0.63 for the XN-350 analyzer and 0.76 for manual counting. We established our own reflex analysis guidelines as follows: HF-BFs <0.7/100 white blood cells (WBCs) is the criterion for quick scans with 100× magnification microscopic examination as a rule-out cut-off, while HF-BFs >83.4/100 WBCs or eosinophils >3.8% are the criteria for mandatory double check confirmation with 1,000× magnification examination.

CONCLUSIONS

The three automated analyzers showed good analytical performances. Application of reflex analysis guidelines is recommended for eosinophils and HF-BFs, and manual confirmation is warranted.

UF-5000 flow cytometer: A new technology to support microbiologists' interpretation of suspected urinary tract infections

This case study aims to describe the adoption of an innovative flow cytometer (i.e., UF-5000), which can support the microbiologists' process of diagnosing suspected urinary tract infections (UTIs). The new clinical information provided can be used to improve the identification of both contamination and colonization, thus reducing inappropriate antibiotic prescriptions. In July and August 2017, the Microbiology Laboratory of Alessandria (Italy) conducted a retrospective monocentric study analyzing data about 1,295 urine specimens from inpatients and outpatients with symptoms of UTIs. The results of this study show that the innovative technology can successfully support the diagnostic process in microbiology laboratories and, consequently, the supply of sustainable treatments by hospitals.

Comparison of the diagnostic performance of two automated urine sediment analyzers with manual phase-contrast microscopy

Background

Recently, several manufacturers have launched automated urinalysis platforms. This study aimed to compare the diagnostic performance of the UF-5000 (Sysmex Corporation, Kobe, Japan) and the cobas® u 701 (Roche Diagnostics, Rotkreuz, Switzerland) urine sediment analyzers with manual phase-contrast microscopy as the reference method.

Methods

A total of 195 urine samples were analyzed on both automated platforms and subjected to manual microscopic examination. Agreement was assessed by Cohen’s kappa (κ) analysis. Sensitivities and specificities were calculated.

Results

The agreement of the UF-5000 with manual microscopy was almost perfect (κ > 0.8) for red (RBC) and white blood cells (WBC), renal tubular epithel cells, hyaline casts, bacteria (BACT) and yeast (YLC), substantial (κ = 0.61–0.80) for squamous epithel cells (SEC) and pathologic casts, and moderate (κ = 0.41–0.60) for transitional epithel cells. The cobas® u 701 showed substantial agreement (κ = 0.61–0.80) for WBC, moderate agreement (κ = 0.41–0.60) for hyaline casts, and fair agreement (κ = 0.21–0.40) for RBC, SEC, non-squamous epithel (NEC), pathologic casts, BACT and YLC. The UF-5000 sensitivities ranged between 98.5% for RBC and 83.3% for pathological casts. The cobas® u 701 showed sensitivities between 83.0% for WBC and 31.6% for YLC.

Conclusions

The UF-5000 (Sysmex) analyzer showed a better diagnostic agreement with manual phase-contrast microscopy compared to the cobas® u 701 (Roche) module. The Sysmex platform showed reliable results for urine sediment analysis. However, pathological samples should be verified with manual microscopy.

Increased effectiveness of urinalysis testing via the integration of automated instrumentation, the lean management approach, and autoverification

Background

In 2014, the Department of Clinical Pathology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand developed and implemented a new process that uses fully automated instrumentation, the lean management approach, and autoverification to improve the productivity and efficiency of the urinalysis workflow process. The aim of this study was to evaluate analytical turnaround time compared with our old urinalysis workflow process and our new urinalysis workflow process that was launched in 2014.

Methods

This study was performed at the Central Laboratory of our center during June 2017 using data collected from the July 2012 (old process) and July 2014 (new process) study periods. We used our laboratory information system to compute and analyze turnaround time of urinalysis tests, and those results were compared between processes.

Results

The 90th percentile turnaround time in overall data was dramatically decreased from approximately 60 minutes in 2012 to <50 minutes in 2014. The mean during both 6:00 am to 9:00 am and 9:00 am to 12:00 pm was approximately 42 minutes in 2012; however, that duration was reduced to approximately 30 minutes for both of those time periods in 2014. Specimens within 60 minutes in both intervals increase from approximately 80% to more than 90%.

Conclusion

The results of this study revealed our new urinalysis workflow process that incorporates fully automated instrumentation, the lean management approach, and autoverification to be effective for significantly increasing productivity as measured by analytical turnaround time and removing 1 staff to another section.

Performance of automated urine analyzers using flow cytometric and digital image-based technology in routine urinalysis

The purpose of this study was to evaluate the analytical performances of Sysmex UF-5000 and Dirui FUS-200 and to compare the results with manual microscopy and between each other. Two hundred fifty urine samples were analyzed for evaluation. Mid-stream specimens were studied sequentially using Dirui FUS-200 and Sysmex UF-5000, and also with manual microscopy within one hour. The physical and chemical components of urinalysis, and sediment results were investigated. The precision results of the FUS-200 and UF-5000 for WBCs, RBCs, and ECs were acceptable. The both analyzers demonstrated good linearity (r > 0.97), with no carry-over. The comparisons of FUS-200 and UF-5000 with manual microscopy for RBCs, WBCs, and ECs on 250 samples exhibited good agreement with little bias (R > 0.780). Only, the moderate agreements were obtained for calcium oxalate for both analyzers (R = 0.512, and 0.648, respectively). The sensitivities of the FUS-200 and UF-5000 were 75.8% and 86.8%, with specificities of 92.3% and 87.8% for WBCs, for RBCs the sensitivities were 91.1%, and 84.4% with specificities of 82.2%, and 89.6% for both analyzers. Kappa values of the UF-5000 were higher than FUS-200 for WBCs, RBCs, ECs, and calcium oxalate. The FUS-200 and UF-5000 urine analyzers, are both accurate, very precise systems and can be safely used in clinical laboratories. However, due to the technological characteristics of the UF-5000 analyzer, its positive impacts on the morphologic recognition and enumeration of RBCs and WBCs should be taken into account, particularly in university hospital laboratories with high patient volumes.

Quantitative urine test strip reading for leukocyte esterase and hemoglobin peroxidase

BACKGROUND

Recently, urine test strip readers have become available for automated test strip analysis. We explored the possibilities of the Sysmex UC-3500 automated urine chemistry analyzer based on complementary metal oxide semiconductor (CMOS) sensor technology with regard to accuracy of leukocyte esterase and hemoglobin peroxidase results. We studied the influence of possible confounders on these measurements.

METHODS

Reflectance data of leukocyte esterase and hemoglobin peroxidase were measured using CMOS technology on the Sysmex UC-3500 automated urine chemistry analyzer. Analytical performance (imprecision, LOQ) as well as the correlation with white blood cell (WBC) and red blood cell (RBC) counts (Sysmex UF-5000) were studied. Furthermore, the influence of urinary dilution, haptoglobin, pH and ascorbic acid as confounders was determined.

RESULTS

Within- and between-run imprecision (reflectance signal) ranged from 1.1% to 3.6% and 0.9% to 4.2% for peroxidase and 0.4% to 2.5% and 0.4% to 3.3% for leukocyte esterase. Good agreement was obtained between the UF-5000 for RBCs and peroxidase reflectance (r=0.843) and for WBCs and leukocyte esterase (r=0.821). Specific esterase activity decreased for WBC counts exceeding 100 cells/μL. Haptoglobin influenced the peroxidase activity, whereas leukocyte esterase and peroxidase activities showed a pH optimum between 5.0 and 6.5. A sigmoidal correlation was observed between urinary osmolality and peroxidase activity.

CONCLUSIONS

CMOS technology allows to obtain high quality test strip results for assessing WBC and RBC in urine. Quantitative peroxidase and leukocyte esterase are complementary with flow cytometry and have an added value in urinalysis, which may form a basis for expert system development.

Semiquantitative, fully automated urine test strip analysis

Objectives

Urinalysis is one of the most frequently ordered diagnostic laboratory tests. In order to reduce workload and costs, rapid screening tests such as urine test strip analyses are applied. The aim of this study was to evaluate the analytical performance of the UC‐3500 as well as the diagnostic performance in comparison with reference methods.

Design and methods

We measured within‐run and between‐run imprecision based on quantitative reflectance values. 347 prospectively included urine specimens were investigated for the presence of glucose, protein, albumin, leukocyte esterase, and hemoglobin peroxidase activity, and ordinal scale results were compared to an automated urine particle analyzer (UF‐5000, Sysmex, Kobe, Japan) and wet chemistry (Roche Cobas 8000, Mannheim, Germany).

Results

Within‐run and between‐run imprecision results based on reflectance data for both the 9 and 11 parameter test strips ranged from 0.07% to 1.36% for the low‐level control and from 0.37% to 6.13% for the high‐level control, depending on the parameter. Regarding diagnostic performance, the sensitivity/specificity for glucose, protein, albumin, leukocyte esterase, and hemoglobin peroxidase was 100/60%, 94.2/88.2%, 81.8/89.2%, 81.7/92.8%, and 85.1/88.6%, respectively; the negative predictive value was 100%, 83.3%, 89.1%, 94.6%, and 96.1%. The Spearman correlation coefficients of the UC‐3500 vs reference methods ranged from 0.915 to 0.967, depending on the parameter.

Conclusion

This fully automated urine test strip analyzer overall shows a satisfying performance and can reliably screen out negative urine samples in order to focus on further characterization of positive samples in the following steps of the workflow.

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.

Rapidly discriminating culture-negative urine specimens from patients with suspected urinary tract infections by UF-5000

Aim: A rapid and reliable method of discriminating such specimens would be very useful. Materials & methods: We analyzed 566 urine specimens from patients with suspected urinary tract infections using a fully automated urine particle analyzer (UF-5000) and evaluated its performance for culture-negative urine specimens. Results: Using the algorithm cutoff values of bacteria less than 30/μl and/or white blood cell less than 200/μl, we obtained a sensitivity of 97.8%, a specificity of 74.6%, a positive predictive value of 46.9%, a negative predictive value of 99.3%, an agreement of 78.9% with the culture method and reduced 61% unnecessary urine culture. Regarding the discrimination of bacterial Gram groups, 67.7% (63/93) of cases were correctly analyzed using the UF-5000 bacteria information, with a Cohen's kappa concordance coefficient of 0.775 (χ² = 31.65, p < 0.001). Conclusion: The performance of UF-5000 for rapidly discriminating culture-negative specimens was quite acceptable for clinical use.

Evaluation of the new Sysmex UF-5000 fluorescence flow cytometry analyser for ruling out bacterial urinary tract infection and for prediction of Gram negative bacteria in urine cultures

BACKGROUND

We evaluated the new flow cytometer UF-5000 with a blue semiconductant laser as a screening tool for ruling out urine samples negative for UTI and its ability to predict Gram negatives in culture.

METHODS

Flow cytometry and microbiological analysis were performed on 2719 urine samples, sent to our microbiology laboratory with a request for urine culture.

RESULTS

UF-5000 showed a very good performance in the screening process. Carryover and cross-contamination was negligible. 797 samples were culture positive at a cut-off of ≥10⁵CFU/mL. ROC curve analysis for BACT count demonstrated AUC between 0.973, on 2714 samples, 0.959, on 1516 female samples, and 0.988 on 1198 male samples, respectively. At the cut-off of BACT ≥58/μL AND/OR YLC ≥150/μL, SE was 99.4%, SP 78.2%, PPV 65.4% and NPV 99.7%; false negatives were 0.6%, avoiding unnecessary cultures in 55.5% of specimens. "Gram Neg?" flag predicted Gram negatives in culture with a SE of 81.6% and SP of 93.3%.

CONCLUSION

The new Sysmex UF-5000 showed high diagnostic accuracy in UTI-screening with a very low rate of false negatives. The instrument is capable of predicting Gram negatives with a good SE and a high agreement with the culture, even if this performance needs further evaluation.