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UTI Quick Test: Using The Urine Flow Cytometer For Reduced Health System Burden

UTI Quick Test: Using The Urine Flow Cytometer For Reduced Health System Burden



Urinary tract infections (UTIs) pose a substantial global health burden, necessitating efficient diagnostic methods. Traditional urine culture methods are time-consuming and resource-intensive. Urine flow cytometry emerges as a promising alternative for rapid and reliable UTI screening.


In this study, urine samples collected from both inpatients and outpatients between 2020 and 2022 were analyzed. Utilizing the UF-4000 urine flow cytometer, we established an optimal threshold of ≥100 bacteria/μL for positivity. Subsequently, we validated the prognostic value of bacterial (BACT), leukocyte (WBC), and yeast-like cell (YLC) counts in combination with bacterial morphology (UF gram-flag) for detecting UTI.


This approach offers a swift and accurate means of identifying UTIs, potentially streamlining diagnostic workflows and improving patient outcomes. By leveraging advanced technology, we aim to enhance the efficiency and precision of UTI diagnosis, thereby alleviating the global burden of this common yet consequential condition.



Urinary tract infections (UTIs) are prevalent bacterial infections worldwide, affecting millions annually. The increasing incidence of UTIs, especially in an aging population, has led to a significant burden on healthcare systems. This rise also contributes to the overuse of antibiotics, exacerbating the global challenge of antimicrobial resistance (AMR).


The conventional method for diagnosing UTIs involves urine culture, which is not only time-consuming but also labor-intensive and expensive. Patients often experience delays in receiving appropriate treatment while awaiting culture results, which can take up to 24-48 hours. Rapid diagnosis is crucial for guiding timely therapy and preventing the progression of UTIs to more severe complications such as kidney infections.


To address these challenges, automated urine flow cytometry (UFC) has emerged as a promising alternative. UFC systems, such as the Sysmex UF-4000, analyze urine samples to detect the presence of various cellular components, including bacteria, leukocytes, and yeast-like cells. By assessing these parameters, UFC can provide rapid indications of UTI presence, potentially expediting treatment initiation and reducing unnecessary antibiotic use.


Previous studies have demonstrated the efficacy of UFC in predicting bacterial growth based on bacterial and leukocyte counts. The UF-4000, equipped with advanced scatter and fluorescence technologies, offers high throughput, processing up to 80 samples per hour. Moreover, UFC systems utilize fluorocell dyes to stain cellular components, allowing for the assessment of bacterial morphology similar to traditional Gram staining.


Despite the promising prognostic value of UFC parameters, the clinical implementation of these systems and the interpretation of results remain areas of uncertainty. This study seeks to address these gaps by evaluating the performance of the UF-4000 in a clinical microbiology laboratory setting. By elucidating the utility of UFC in diagnosing UTIs and providing guidance on result interpretation, this research aims to enhance the efficiency and accuracy of UTI diagnosis, ultimately improving patient outcomes.

Also Read; Urine and Urination


Inclusion Criteria


  1. Urine Samples: Urine samples received between the specified time frame (October 2019 to March 2022) from both inpatients and outpatients were included.


  1. Age and Gender: Urine samples were collected from individuals of all ages and genders.


  1. Source of Samples: Samples were collected from various sources, including regional hospitals, general practitioners’ offices, and elderly care facilities.


  1. Transportation Time: Urine samples transported within a maximum time frame of 24 hours after collection were considered.


  1. Diagnostic Method: Samples processed using the UF-4000 urine flow cytometer as part of routine diagnostic analysis were included.


Exclusion Criteria


  1. High-Risk Samples


 Urine samples originating from high-risk populations or collected under specific conditions were excluded. This included samples from oncology or intensive care departments, children under 1 year old, women aged between 15 and 30, pregnant women, and samples collected via nephrostomy catheter, suprapubic aspiration/catheter, or single-use catheter.


  1. Non-Midstream Samples


Samples not collected as midstream urine (e.g., first-stream, catheter urine, collection bag) were excluded due to a higher risk of contamination or colonization.


  1. Manual Gram Stains


Samples for which manual Gram stains directly from urine were performed were excluded. Instead, comparisons were made between the UF-4000 Gram-flag and actual growth observed in culture.


  1. Unsuitable Transportation


Samples with transportation times exceeding 24 hours after collection were excluded from analysis to ensure sample integrity and reliability.


  1. Mixed Colonies


Samples with mixed growth on culture plates, where ≥3 different colony types were present, were excluded unless one colony type clearly dominated over the others.

Also Read; Urinalysis


In this study, the UF-4000 urine flow cytometer’s effectiveness in predicting the growth of uropathogens in urine culture was validated. Median cell counts of total bacterial count (BACT), white blood cell count (WBC), yeast-like cell count (YLC), and UF-4000 Gram-flag were compared between UF-negative and UF-positive samples. The UF-positive samples were further categorized based on culture results into pathogenic growth, non-pathogenic growth, mixed growth, and no growth. Statistical analysis, including the Kruskal-Wallis test, was employed due to non-normal distribution of BACT, WBC, and YLC counts. A significance level of p < 0.05 was set. Positive predictive values (PPV) for UTI presence were calculated using arbitrary cutoffs for bacterial and WBC counts, stratified by UF Gram-flag. Additionally, sensitivity, specificity, PPV, and negative predictive values (NPV) of the UF Gram-flag were evaluated against actual culture growth. Cohen’s kappa (κ) was calculated to assess agreement, with interpretations based on established literature thresholds. Data analysis was conducted using R version 4.0.3.


The UF-4000, introduced in October 2019, underwent validation using 970 clinical urine samples to establish an optimal positivity threshold, ultimately set at ≥100 bacteria/μL. This threshold exhibited impressive sensitivity (96.2%) and reasonable specificity (61.9%), with a positive predictive value (PPV) of 72.7% and a negative predictive value (NPV) of 93.9%. Following implementation, validation was conducted with a substantial sample size, encompassing 66,019 urine samples collected between January 2020 and March 2022. 


Of these samples, 42,958 were deemed eligible for analysis, with exclusions made for ‘high-risk’ samples and those not collected mid-stream. Upon UF-4000 screening, 27,063 samples (63.0%) were flagged as positive, with a striking 94.4% demonstrating growth >103 CFU/mL upon urine culture. Notably, uropathogens were identified in 73.1% of cultured samples, with Escherichia coli, Klebsiella pneumoniae, and Enterococcus faecalis emerging as the most prevalent.


Analyzing bacterial and white blood cell (WBC) counts in relation to culture outcomes revealed significant elevations in both parameters in cultures with identified uropathogens, underscoring their utility as potential predictors of infection. Additionally, the UF Gram-flag demonstrated a propensity for Gram-negative results when uropathogens were present, indicating its potential as a valuable diagnostic indicator.


Further investigation into positive predictive values for uropathogens elucidated a primary association with increased bacterial counts, affirming the importance of this parameter in UTI diagnosis. Sensitivity and positive predictive values for both Gram-positive (GP) and Gram-negative (GN) uropathogens varied with UF Gram-flag results, with notable differences observed in their diagnostic performance.


Cohen’s kappa analysis underscored the UF GN-flag’s robust agreement with GN uropathogens, indicating substantial reliability in identifying such pathogens. Meanwhile, yeast-like cell count analysis revealed a significant association with yeast presence in urine culture, albeit with limitations due to false positives, necessitating cautious interpretation.


The UF-4000 demonstrated promising performance in screening for UTIs, with its parameters exhibiting considerable potential as diagnostic indicators. Continued research and refinement are warranted to optimize its clinical utility and enhance UTI management practices.



In our investigation, we implemented and assessed the clinical utility of the Sysmex UF-4000 urine flow cytometer within a microbiology laboratory setting. Initially, we explored its application as a diagnostic screening tool to triage urine samples for culture, resulting in a notable 37% reduction in the need for urine cultures in our laboratory. Subsequently, we delved into the clinical significance of multiple parameters provided by the UF-4000, particularly in identifying uropathogens in positive urine samples.


Our findings highlighted that bacterial cell counts (BACT) exceeding 10,000/μL were highly indicative of uropathogen presence, particularly when coupled with a Gram-negative (GN) flag. Conversely, the absence of a Gram-positive (GP) flag or yeast-like cells (YLC) exhibited high negative predictive values, effectively ruling out the presence of GP bacteria or yeasts.


This reduction in the need for urine cultures aligns with previous studies and contributes to significant cost savings, with the cost per sample screened by the UF-4000 approximately one-third of that of a negative urine culture in our laboratory. Despite the additional costs incurred when the UF screen is positive and subsequent urine culture is performed, overall costs remain comparable to solely conducting urine culture. Importantly, this diagnostic strategy ensures that 37% of cases receive a (negative) outcome within the same day, thus preventing unnecessary antibiotic prescriptions.


However, our study revealed limitations regarding the predictive value of white blood cell (WBC) counts for uropathogen presence. Significant WBC counts were observed in samples without uropathogen growth, as well as in samples screened negative by the UF-4000, suggesting constraints in using WBC counts as a reliable predictor.


Moreover, we encountered challenges related to false-negative cases resulting from the chosen screening threshold of 100 bacteria/μL for positivity. This threshold inevitably led to false-negative results, although sensitivity was consistent with previous studies. Additionally, we faced technical issues where the UF Gram-flag was unavailable for analysis in a subset of urine samples, potentially affecting the interpretability of our results.


Despite these limitations, our study’s strength lies in its large sample size, providing robust validation of the clinical utility of the UF-4000 in routine practice. Our findings offer valuable insights into predicting uropathogen presence, aiding clinical decision-making within a remarkably short timeframe after sample collection. However, further research is warranted to address the identified limitations and optimize the diagnostic accuracy of the UF-4000 in urinary tract infection management.



Implementation of the UF-4000 urine flow cytometer resulted in a significant 37% reduction in the number of urine cultures routinely conducted in our microbiology laboratory. Our study sheds light on the UF-4000’s efficacy in identifying urine samples more likely to harbor uropathogens. We underscored the clinical significance of bacterial cell counts and Gram-flag analysis in predicting uropathogen presence, offering valuable guidance on interpreting these parameters.


These findings hold potential for guiding the integration of the UF-4000 system into microbiology laboratories, empowering clinicians with enhanced insights to interpret results and make informed clinical decisions.


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