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

Using the Sysmex UF-4000 urine flow cytometer for rapid diagnosis of urinary tract infection in the clinical microbiological laboratory

BACKGROUND

Urinary tract infections are responsible for a significant worldwide disease burden. Performing urine culture is time consuming and labor intensive. Urine flow cytometry might provide a quick and reliable method to screen for urinary tract infection.

METHODS

We analyzed routinely collected urine samples received between 2020 and 2022 from both inpatients and outpatients. The UF-4000 urine flow cytometer was implemented with an optimal threshold for positivity of ≥100 bacteria/μL. We thereafter validated the prognostic value to detect the presence of urinary tract infection (UTI) based on bacterial (BACT), leukocyte (WBC), and yeast-like cell (YLC) counts combined with the bacterial morphology (UF gram-flag).

RESULTS

In the first phase, in 2019, the UF-4000 was implemented using 970 urine samples. In the second phase, between 2020 and 2022, the validation was performed in 42,958 midstream urine samples. The UF-4000 screen resulted in a 37% (n = 15,895) decrease in performed urine cultures. Uropathogens were identified in 18,673 (69%) positively flagged urine samples. BACT > 10.000/μL combined with a gram-negative flag had a >90% positive predictive value for the presence of gram-negative uropathogens. The absence of gram-positive flag or YLC had high negative predictive values (99% and >99%, respectively) and are, therefore, best used to rule out the presence of gram-positive bacteria or yeast. WBC counts did not add to the prediction of uropathogens.

CONCLUSION

Implementation of the UF-4000 in routine practice decreased the number of cultured urine samples by 37%. Bacterial cell counts were highly predictive for the presence of UTI, especially when combined with the presence of a gram-negative flag.

Urine Flow Cytometry and Dipstick Analysis in Diagnosing Bacteriuria and Urinary Tract Infections among Adults in the Emergency Department-A Diagnostic Accuracy Trial

Urinary tract infections (UTIs) are a leading infectious cause of emergency department admission. Early UTI diagnosis is challenging, and a faster, preferably point-of-care urine analysis is necessary. We aimed to evaluate the diagnostic accuracy of urine flow cytometry (UFC) and urine dipstick analysis (UDA) in identifying bacteriuria and UTIs. This study included adults suspected of an infection admitted to three Danish emergency departments. UFC and UDA were the index tests, and urine culture and an expert panel diagnosis were the reference tests. We used logistic regression and receiver operator characteristics curves to find each test's optimal model and cut-off. We enrolled 966 patients and performed urine cultures on 786. Urine culture was positive in 337, and 200 patients were diagnosed with a UTI. The UFC model ruled out bacteriuria in 10.9% with a negative predictive value (NPV) of 94.6% and ruled out UTI in 38.6% with an NPV of 97.0%. UDA ruled out bacteriuria in 52.1% with an NPV of 79.2% and UTI in 52.8% with an NPV of 93.9%. Neither UFC nor UDA performed well in ruling out bacteriuria in our population. In contrast, both tests ruled out UTI safely and in clinically relevant numbers.

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.

Usefulness of UF-5000 automatic screening system in UTI diagnosis

We have evaluated the Sysmex UF-5000 cytometer use in microbiology for the screening of negative urines, looking for cut-off points to detect bacteria and leukocytes. The number of processed urines was 3569, the highest to date in these studies. The best general cut-off point has been 100 bact/μl, giving an area under the ROC curve of 0.868, a sensitivity of 96%, a specificity of 50%, 1.17% of false negatives, and saving 40% of cultures. The PPV and NPV have been 35.5 and 95.4 respectively. The leukocyte count has not been useful. Finally, we have evaluated urine screening usefulness, concluding that in laboratories such as ours (284 urines/working day) or smaller, it is not cost-effective.

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.

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.

POCT urine dipstick versus central laboratory analyses: Diagnostic performance and logistics in the medical emergency department

OBJECTIVES

The aim of this study was to evaluate the logistics and diagnostic performances of dipstick analyses compared to their counterpart central laboratory analyses for detection of bacteriuria, proteinuria, hyperglycemia, ketosis and hematuria.

DESIGN AND METHODS

Urine dipstick results, urine culture results, flow cytometric cell counts, U-albumin-to-creatinine ratio, P-glucose and P-beta-hydroxybutyrate were retrospectively reviewed in a cohort of consecutive patients admitted to the medical emergency departments of two Danish hospitals. Sensitivity, specificity and predictive values of traditional dipstick analysis were estimated and dipstick was compared to flow cytometry for detection of significant bacteriuria using logistic regression. Turn-around-time for central laboratory analyses were assessed.

RESULTS

For each comparison, 1,997 patients or more were included. Traditional dipstick analyses for proteinuria, bacteriuria and ketosis reached sensitivities of up to 90%, while sensitivity for hyperglycemia was 59%. Flow cytometry outperformed traditional dipstick analysis for detection of bacteriuria with a difference in the area under the ROC-curve of 0.07. Turn-around-times for 95% delivery of central laboratory analysis results ranged from approximately 1½ to 2 h.

CONCLUSIONS

For the detection of bacteriuria and albuminuria, central laboratory analyses reach better performance than dipstick analysis while achieving acceptable turn-around-times and are thus viable alternatives to dipstick analysis. For detection of ketosis and hyperglycemia, dipstick analysis does not perform adequately, but as very short turn-around-time is often required, these conditions may be best diagnosed by point-of-care blood test rather than dipstick or central laboratory analyses. Dipstick hemoglobin analysis, flow cytometry and microscopic evaluation may serve each their distinct purposes, and thus are relevant in the emergency department.

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.

Urine Flow Cytometry Parameter Cannot Safely Predict Contamination of Urine—A Cohort Study of a Swiss Emergency Department Using Machine Learning Techniques

Background: Urine flow cytometry (UFC) analyses urine samples and determines parameter counts. We aimed to predict different types of urine culture growth, including mixed growth indicating urine culture contamination. Methods: A retrospective cohort study (07/2017–09/2020) was performed on pairs of urine samples and urine cultures obtained from adult emergency department patients. The dataset was split into a training (75%) and validation set (25%). Statistical analysis was performed using a machine learning approach with extreme gradient boosting to predict urine culture growth types (i.e., negative, positive, and mixed) using UFC parameters obtained by UF-4000, sex, and age. Results: In total, 3835 urine samples were included. Detection of squamous epithelial cells, bacteria, and leukocytes by UFC were associated with the different types of culture growth. We achieved a prediction accuracy of 80% in the three-class approach. Of the n = 126 mixed cultures in the validation set, 11.1% were correctly predicted; positive and negative cultures were correctly predicted in 74.0% and 96.3%. Conclusions: Significant bacterial growth can be safely ruled out using UFC parameters. However, positive urine culture growth (rule in) or even mixed culture growth (suggesting contamination) cannot be adequately predicted using UFC parameters alone. Squamous epithelial cells are associated with mixed culture growth.