A Novel Machine-Learning Algorithm to Predict Stone Recurrence with 24-Hour Urine Data.

Objectives: The absence of predictive markers for kidney stone recurrence poses a challenge for the clinical management of stone disease. The unpredictability of stone events is also a significant limitation for clinical trials, where many patients must be enrolled to obtain sufficient stone events for analysis. In this study, we sought to use machine learning methods to identify a novel algorithm to predict stone recurrence. Subjects/Patients and Methods: Patients enrolled in the Registry for Stones of the Kidney and Ureter (ReSKU), a registry of nephrolithiasis patients collected between 2015-2020, with at least one prospectively collected 24-hour urine test (Litholink 24-hour urine test; Labcorp) were included in the training set. A validation set was obtained from chart review of stone patients not enrolled in ReSKU with 24-hour urine data. Stone events were defined as either an office visit where a patient reports symptomatic passage of stones or a surgical procedure for stone removal. Seven prediction classification methods were evaluated. Predictive analyses and receiver operator characteristics (ROC) curve generation were performed in R. Results: A training set of 423 kidney stone patients with stone event data and 24-hour urine samples were trained using the prediction classification methods. The highest performing prediction model was a Logistic Regression with ElasticNet machine learning model (area under curve [AUC] = 0.65). Restricting analysis to high confidence predictions significantly improved model accuracy (AUC = 0.82). The prediction model was validated on a validation set of 172 stone patients with stone event data and 24-hour urine samples. Prediction accuracy in the validation set demonstrated moderate discriminative ability (AUC = 0.64). Repeat modeling was performed with four of the highest scoring features, and ROC analyses demonstrated minimal loss in accuracy (AUC = 0.63). Conclusion: Machine-learning models based on 24-hour urine data can predict stone recurrences with a moderate degree of accuracy.

Trends in urine sampling rates of general practice patients with suspected lower urinary tract infections in England, 2015-2022: a population-based study.

OBJECTIVES: Inappropriate prescribing of antibiotics is a key driver of antimicrobial resistance. This study aimed to describe urine sampling rates and antibiotic prescribing for patients with lower urinary tract infections (UTIs) in English general practice. DESIGN: A retrospective population-based study using administrative data. SETTING: IQVIA Medical Research Database (IMRD) data from general practices in England, 2015-2022. PARTICIPANTS: Patients who have consulted with an uncomplicated UTI in England general practices captured in the IMRD. OUTCOME MEASURES: Trends in UTI episodes (episodes were defined as UTI diagnosis codes occurring within 14 days of each other), testing and antibiotic prescribing on the same day as initial UTI consultation were assessed from January 2015 to December 2022. Associations, using univariate and multivariate logistic regressions, were examined between consultation and demographic factors on the odds of a urine test. RESULTS: There were 743 350 UTI episodes; 50.8% had a urine test. Testing rates fluctuated with an upward trend and large decline in 2020. Same-day UTI antibiotic prescribing occurred in 78.2% of episodes. In multivariate modelling, factors found to decrease odds of a urine test included age ≥85 years (0.83, 95% CI 0.82 to 0.84), consultation type (remote vs face to face, 0.45, 95% CI 0.45 to 0.46), episodes in London compared with the South (0.74, 95% CI 0.72 to 0.75) and increasing practice size (0.77, 95% CI 0.76 to 0.78). Odds of urine tests increased in males (OR 1.11, 95% CI 1.10 to 1.13), for those episodes without a same-day UTI antibiotic (1.10, 95% CI 1.04 to 1.16) for episodes for those with higher deprivation status (Indices of Multiple Deprivation 8 vs 1, 1.51, 95% CI 1.48 to 1.54). Compared with 2015, 2016-2019 saw increased odds of testing while 2020 and 2021 saw decreases, with 2022 showing increased odds. CONCLUSION: Urine testing for UTI in general practice in England showed an upward trend, with same-day antibiotic prescribing remaining consistent, suggesting greater alignment to national guidelines. The COVID-19 pandemic impacted testing rates, though as of 2022, they began to recover.

The Importance of Urine Sediment Analysis.

BACKGROUND: Urinary sediment is an important part of routine urine test, which plays an irreplaceable role in the diagnosis of diseases, monitoring of treatment effect, and prognosis judgment [1]. METHODS: Through the results of urine dry chemistry and microscopic examination of urinary sediment, we inter-preted and analyzed the clinical significance of urinary casts in urinary sediment. RESULTS: In patients with new urinary system diseases abnormal urine results appear earlier than changes in serum renal function indicators, especially when the urine sediment shows typical casts, which can provide an important basis for clinical diagnosis. CONCLUSIONS: Clinical laboratory personnel should attach great importance to the morphological examination of urinary sediment and master the diagnostic significance of the formed components of urinary sediment for various diseases, so as to better assist clinical disease diagnosis.

Detection of Mycobacterium tuberculosis transrenal DNA in urine samples among adults in Peru.

Diagnosis of pulmonary tuberculosis (TB) relies on a sputum sample, which cannot be obtained from all symptomatic individuals. Mycobacterium tuberculosis (Mtb) transrenal DNA (trDNA) has been detected in urine, an easily obtainable, noninvasive, alternative sample type. However, reported sensitivities have been variable and likely depend on collection and assay procedures and aspects of trDNA biology. We analyzed three serial urine samples from each of 75 adults with culture-confirmed pulmonary TB disease in Lima, Peru for detection of trDNA using short-fragment real-time PCR. Additionally, we examined host, urine, and sampling factors associated with detection. Overall per-sample sensitivity was 38 % (95 % Confidence Interval [CI] 30-45 %). On an individual level (i.e., any of the three samples positive), sensitivity was 73 % (95 % CI: 62-83 %). Sensitivity was highest among samples from patients with smear-positive TB, 92 % (95 % CI: 62-100 %). Specificity from a single sample from each of 10 healthy controls was 100 % (95 % CI: 69-100 %). Adjusting our assay positivity threshold increased individual-level sensitivity to 88 % (95 % CI: 78-94 %) overall without affecting the specificity. We did not find associations between Mtb trDNA detection and individual characteristics or urine sample characteristics. Overall, our results support the potential of trDNA detection for TB diagnosis.

Automated identification and segmentation of urine spots based on deep-learning.

Micturition serves an essential physiological function that allows the body to eliminate metabolic wastes and maintain water-electrolyte balance. The urine spot assay (VSA), as a simple and economical assay, has been widely used in the study of micturition behavior in rodents. However, the traditional VSA method relies on manual judgment, introduces subjective errors, faces difficulty in obtaining appearance time of each urine spot, and struggles with quantitative analysis of overlapping spots. To address these challenges, we developed a deep learning-based approach for the automatic identification and segmentation of urine spots. Our system employs a target detection network to efficiently detect each urine spot and utilizes an instance segmentation network to achieve precise segmentation of overlapping urine spots. Compared with the traditional VSA method, our system achieves automated detection of urine spot area of micturition in rodents, greatly reducing subjective errors. It accurately determines the urination time of each spot and effectively quantifies the overlapping spots. This study enables high-throughput and precise urine spot detection, providing important technical support for the analysis of urination behavior and the study of the neural mechanism underlying urination.

Utilizing Raman spectroscopy for urinalysis to diagnose acute kidney injury stages in cardiac surgery patients.

BACKGROUND: The successful treatment and improvement of acute kidney injury (AKI) depend on early-stage diagnosis. However, no study has differentiated between the three stages of AKI and non-AKI patients following heart surgery. This study will fill this gap in the literature and help to improve kidney disease management in the future. METHODS: In this study, we applied Raman spectroscopy (RS) to uncover unique urine biomarkers distinguishing heart surgery patients with and without AKI. Given the amplified risk of renal complications post-cardiac surgery, this approach is of paramount importance. Further, we employed the partial least squares-support vector machine (PLS-SVM) model to distinguish between all three stages of AKI and non-AKI patients. RESULTS: We noted significant metabolic disparities among the groups. Each AKI stage presented a distinct metabolic profile: stage 1 had elevated uric acid and reduced creatinine levels; stage 2 demonstrated increased tryptophan and nitrogenous compounds with diminished uric acid; stage 3 displayed the highest neopterin and the lowest creatinine levels. We utilized the PLS-SVM model for discriminant analysis, achieving over 90% identification rate in distinguishing AKI patients, encompassing all stages, from non-AKI subjects. CONCLUSIONS: This study characterizes the incidence and risk factors for AKI after cardiac surgery. The unique spectral information garnered from this study can also pave the way for developing an in vivo RS method to detect and monitor AKI effectively.

Evaluation of the DxU 850m Iris automated urine microscopy analyzer for identifying culture-negative urine samples: From a perspective of reducing urine culture number.

We evaluated the DxU 850m Iris Urine Microscopy analyzer as a screening tool for excluding negative urine samples (n = 1337). At a cutoff of 103 colony counts·mL-1, sensitivity was 55.1 %, specificity 68.6 %. The DxU 850m Iris does not offer acceptable prediction of culture-negative urine samples at the tested cutoff.

The applicability of the urine color scale in pediatric urology.

AIMS: To determine the level of agreement between healthcare professionals, patients and their parents/guardians in the interpretation of the urine color scale (UCS) in cases of urinary dysfunction, analyzing the applicability of the scale as a diagnostic tool determining the hydration status. METHODS: This was a cross-sectional study involving 5-17-year-old patients with lower urinary tract symptoms (LUTS) and enuresis. The study was conducted in a public healthcare referral center for pediatric urology in the Brazilian state of Bahia between October 2019 and March 2020. The Kolmogorov-Smirnov test was used to assess the distribution of the variables. Agreement was assessed using the kappa coefficient and weighted kappa. The z-test was used to determine significant differences between the kappa and weighted kappa. The statistical analysis was conducted using SPSS, version 14, and significance was established at p < 0.05. RESULTS: Forty-four patients were included. The kappa value was 32.4% (p = 0.000) for the agreement between healthcare professionals and patients, 41.9% (p = 0.000) for agreement between healthcare professionals and parents/guardians, and 25.0% (p = 0.001) for agreement between patients and parents/guardians. The weighted kappa was 70.6% (p = 0.000) for agreement between healthcare professionals and patients, 82.4% (p = 0.000) for agreement between healthcare professionals and parents/guardians, and 51.5% (p = 0.001) for agreement between patients and parents/guardians. There was a statistically significant difference in kappa values when the healthcare professionals were compared with the other groups. CONCLUSIONS: Although there were some inconsistencies in interpretation, the UCS proved to be a useful tool with which to evaluate patients' hydration status.

Urine beyond electrolytes: diagnosis through extracellular vesicles.

BACKGROUND AND OBJECTIVE: Extracellular vesicles (EV) reflect the pathophysiological state of their cells of origin and are a reservoir of renal information accessible in urine. When biopsy is not an option, EV present themselves as sentinels of function and damage, providing a non-invasive approach. However, the analysis of EV in urine requires prior isolation, which slows down and hinders transition into clinical practice. The aim of this study is to show the applicability of the "single particle interferometric reflectance imaging sensor" (SP-IRIS) technology through the ExoView® platform for the direct analysis of urine EV and proteins involved in renal function. MATERIALS AND METHODS: The ExoView® technology enables the quantification and phenotyping of EV present in urine and the quantification of their membrane and internal proteins. We have applied this technology to the quantification of urinary EV and their proteins with renal tubular expression, amnionless (AMN) and secreted frizzled-related protein 1 (SFRP1), using only 5 µl of urine. Tubular expression was confirmed by immunohistochemistry. RESULTS: The mean size of the EV analysed was 59 ± 16 nm for those captured by tetraspanin CD63, 61 ± 16 nm for those captured by tetraspanin CD81, and 59 ± 10 for tetraspanin CD9, with CD63 being the majority EV subpopulation in urine (48.92%). The distribution of AMN and SFRP1 in the three capture tetraspanins turned out to be similar for both proteins, being expressed mainly in CD63 (48.23% for AMN and 52.1% for SFRP1). CONCLUSIONS: This work demonstrates the applicability and advantages of the ExoView® technique for the direct analysis of urine EV and their protein content in relation to the renal tubule. The use of minimum volumes, 5 µl, and the total analysis time not exceeding three hours facilitate the transition of EV into daily clinical practice as sources of diagnostic information.

Blending Samples to Increase Accuracy and Precision of 1H NMR Urine Metabolomics.

Urine is an equally attractive biofluid for metabolomics analysis, as it is a challenging matrix analytically. Accurate urine metabolite concentration estimates by Nuclear Magnetic Resonance (NMR) are hampered by pH and ionic strength differences between samples, resulting in large peak shift variability. Here we show that calculating the spectra of original samples from mixtures of samples using linear algebra reduces the shift problems and makes various error estimates possible. Since the use of two-dimensional (2D) NMR to confirm metabolite annotations is effectively impossible to employ on every sample of large sample sets, stabilization of metabolite peak positions increases the confidence in identifying metabolites, avoiding the pitfall of oranges-to-apples comparisons.

An artificial intelligence-driven support tool for prediction of urine culture test results.

BACKGROUND AND AIMS: We aimed to develop an easily deployable artificial intelligence (AI)-driven model for rapid prediction of urine culture test results. MATERIAL AND METHODS: We utilized a training dataset (n = 34,584 urine samples) and two separate, unseen test sets (n = 10,083 and 9,289 samples). Various machine learning models were compared for diagnostic performance. Predictive parameters included urinalysis results (dipstick and flow cytometry), patient demographics (age and gender), and sample collection method. RESULTS: Although more complex models achieved the highest AUCs for predicting positive cultures (highest: multilayer perceptron (MLP) with AUC of 0.884, 95% CI 0.878-0.89), multiple logistic regression (MLR) using only flow cytometry parameters achieved a very good AUC (0.858, 95% CI 0.852-0.865). To aid interpretation, prediction results of the MLP and MLR models were categorized based on likelihood ratio (LR) for positivity: highly unlikely (LR 0.1), unlikely (LR 0.3), grey zone (LR 0.9), likely (LR 5.0), and highly likely (LR 40). This resulted in 17%, 28%, 34%, 9%, and 13% of samples falling into each respective category for the MLR model and 20%, 26%, 31%, 7%, and 16% for the MLP model. CONCLUSIONS: In conclusion, this robust model has the potential to assist clinicians in their decision-making process by providing insights prior to the availability of urine culture results in a significant portion of samples (∼2/3rd).

Host urinary biomarkers in HIV positive and HIV negative patients with tubercular uveitis and other uveitic diseases.

PURPOSE: To determine if host urinary biomarker profiles could distinguish between tubercular uveitis (TBU) and other uveitic diseases (OUD) in patients with and without HIV infection. METHODS: Concentrations of 29 different host biomarkers were measured in urine samples using the Luminex platform. Data were analyzed to describe differences between patients diagnosed with and without TBU and with and without HIV co-infection. RESULTS: One-hundred-and-eighteen urine samples were collected and 39% participants were diagnosed as TBU+. Mean age TBU+ was 39.3±13.6 years with 45.7% males. Anterior and panuveitis and unilateral involvement were most common. 32.6% were TBU+HIV+ (median CD4+=215) while 40.2% were OUD+HIV+ (median CD4+=234). Only sVEGF3 was decreased in TBU+ versus OUD+ (p=0.03), regardless of HIV status. Some biomarkers were significantly raised in HIV+ TBU+ compared to HIV- TBU+: sIL-6Rα, CD30, sRAGE , sTNFR I&-II, IP-10, MIP-1ß, sEGFR and Ferritin. HIV+ OUD+ had increased sVEGFR3, CD30, sIL-6Rα, IP-10, sTNFR I&-II, Ferritin and Haptoglobin compared to HIV- OUD+. VEGF-A (p = 0.04) was decreased in HIV+ OUD+ versus HIV- OUD+. CONCLUSION: Decreased urinary concentrations of VEGFR3 were observed in TBU+ compared to TBU-. HIV+ individuals demonstrated increased concentrations of multiple urinary analytes when compared to HIV- patients with uveitis.

The Impact of Polymerase Chain Reaction Urine Testing on Clinical Decision-Making in the Management of Complex Urinary Tract Infections.

While urinary polymerase chain reaction (PCR) testing is effective in organism identification in patients with complex urinary tract infections (cUTI), limited data exists on the clinical usefulness of this test. We serially surveyed physicians treating symptomatic patients with cUTI both at presentation and after PCR, and urine culture (UC) results were available to ascertain how the test results modified the therapy. A total of 96 unique surveys completed by 21 providers were included in the data analysis. The mean age for female and male patients was 69.4 ± 15.5 and 71.6 ± 12.7 years, respectively. The test positivity and line-item concordance for UC and PCR were consistent with prior reports. The PCR results modified or confirmed treatment in 59/96 (61.5%) and 25/96 (26.0%) of the cases, respectively, with 12/29 (41.4%) and 47/67 (70.1%) having negative and positive PCR results, respectively, resulting in treatment change (difference 28.7%, p < 0.01). Of these, 55/59 (57.3%) were alterations in the antibiotic regimen. PCR use to modify treatment was similar across providers and not statistically different when stratified by patient age, gender, or prior empiric therapy. In 31/59 (52.5%) of the cases, the PCR results modified the treatment where UC would not; conversely, UC would have modified the treatment in 3/37 (8.1%) of the cases where PCR did not (difference 44.4%, p < 0.01). We find that PCR test results are used by clinicians in managing cUTI, and use of this test provides an opportunity to improve antibiotic stewardship in this difficult-to-treat subset of patients.

Accuracy of Urinalysis for UTI in Spina Bifida.

OBJECTIVES: Urinary tract infections (UTIs) are common, but overdiagnosed, in children with spina bifida. We sought to evaluate the diagnostic test characteristics of urinalysis (UA) findings for symptomatic UTI in children with spina bifida. METHODS: Retrospective cross-sectional study using data from 2 centers from January 1, 2016, to December 31, 2021. Children with myelomeningocele aged <19 years who had paired UA (and microscopy, when available) and urine culture were included. The primary outcome was symptomatic UTI. We used generalized estimating equations to control for multiple encounters per child and calculated area under the receiver operating characteristics curve, sensitivity, and specificity for positive nitrites, pyuria (≥10 white blood cells/high-powered field), and leukocyte esterase (more than trace) for a symptomatic UTI. RESULTS: We included 974 encounters from 319 unique children, of which 120 (12.3%) met our criteria for UTI. Pyuria had the highest sensitivity while nitrites were the most specific. Comparatively, nitrites were the least sensitive and pyuria was the least specific. When the cohort was limited to children with symptoms of a UTI, pyuria remained the most sensitive parameter, whereas nitrites remained the least sensitive. Nitrites continued to be the most specific, whereas pyuria was the least specific. Among all encounters, the overall area under the receiver operating characteristics curve for all components of the UA was lower in children who use clean intermittent catheterizations compared with all others. CONCLUSIONS: Individual UA findings have moderate sensitivity (leukocyte esterase or pyuria) or specificity (nitrites) but overall poor diagnostic accuracy for symptomatic UTIs in children with spina bifida.

Spot urinary sodium in CKD patients: correlation with 24h-excretion and evaluation of commonly used prediction equations.

BACKGROUND: Salt intake in CKD patients can affect cardiovascular risk and kidney disease progression. Twenty-four hour (24h) urine collections are often used to investigate salt metabolism but are cumbersome to perform. We assessed urinary sodium (U-Na) concentration in spot urine samples and investigated the correlation with 24h U-Na excretion and concentration in CKD patients under nephrological care. Further, we studied the role of CKD stage and diuretics and evaluated the performance of commonly used formulas for the prediction of 24h U-Na excretion from spot urine samples. METHODS: One hundred eight patients of the German Chronic Kidney Disease (GCKD) study were included. Each participant collected a 24h urine and two spot urine samples within the same period. The first spot urine sample (AM) was part of the second morning urine. The second urine sample was collected before dinner (PM). Patients were advised to take their medication as usual without changing dietary habits. U-Na concentrations in the two spot urine samples and their average ((AM + PM)/2) were correlated with U-Na concentration and total Na excretion in the 24h urine collections. Correlations were subsequently studied after stratification by CKD stage and diuretic intake. The usefulness of three commonly applied equations to estimate 24h U-Na excretion from spot urine samples (Kawasaki, Tanaka and Intersalt) was determined using Bland-Altman plots, analyses of sensitivity, specificity, as well as positive (PPV) and negative predictive values (NPV). RESULTS: Participants (42 women, 66 men) were on average (± SD) 62.2 (± 11.9) years old, with a mean serum creatinine of 1.6 (± 0.5) mg/dl. 95% had arterial hypertension, 37% diabetes mellitus and 55% were on diuretics. The best correlation with 24h U-Na total excretion was found for the PM spot U-Na sample. We also found strong correlations when comparing spot and 24h urine U-Na concentration. Correction of spot U-Na for U-creatinine did not improve strength of correlations. Neither CKD stage, nor intake of diuretics had significant impact on these correlations. All examined formulas revealed a significant mean bias. The lowest mean bias and the strongest correlation between estimated and measured U-Na excretion in 24h were obtained using the Tanaka-formula. Also, application of the Tanaka-formula with PM U-Na provided best sensitivity, specificity, PPV and NPV to estimate U-Na excretion > 4g/d corresponding to a salt consumption > 10g/d. CONCLUSION: U-Na concentration of spot urine samples correlated with 24h U-Na excretion especially when PM spot U-Na was used. However, correlation coefficients were relatively low. Neither CKD stage nor intake of diuretics appeared to have an influence on these correlations. There was a significant bias for all tested formulas with the Tanaka-formula providing the strongest correlation with measured 24h U-Na excretion. In summary, using spot urine samples together with the Tanaka-formula in epidemiological studies appears feasible to determine associations between approximate salt intake and outcomes in CKD patients. However, the usefulness of spot-urine samples to guide and monitor salt consumption in individual patients remains limited.

Hepatic Biotransformation of Renal Clearable Gold Nanoparticles for Noninvasive Detection of Liver Glutathione Level via Urinalysis.

Renal clearable nanoparticles have been drawing much attention as they can avoid prolonged accumulation in the body by efficiently clearing through the kidneys. While much effort has been made to understand their interactions within the kidneys, it remains unclear whether their transport could be influenced by other organs, such as the liver, which plays a crucial role in metabolizing and eliminating both endogenous and exogenous substances through various biotransformation processes. Here, by utilizing renal clearable IRDye800CW conjugated gold nanocluster (800CW4-GS18-Au25) as a model, we found that although 800CW4-GS18-Au25 strongly resisted serum-protein binding and exhibited minimal accumulation in the liver, its surface was still gradually modified by hepatic glutathione-mediated biotransformation when passing through the liver, resulting in the dissociation of IRDye800CW from Au25 and biotransformation-generated fingerprint message of 800CW4-GS18-Au25 in urine, which allowed us to facilely quantify its urinary biotransformation index (UBI) via urine chromatography analysis. Moreover, we observed the linear correlation between UBI and hepatic glutathione concentration, offering us a noninvasive method for quantitative detection of liver glutathione level through a simple urine test. Our discoveries would broaden the fundamental understanding of in vivo transport of nanoparticles and advance the development of urinary probes for noninvasive biodetection.

The impact of Boric Acid tubes on quantitative urinary bacterial cultures in hospitalized patients.

INTRODUCTION: The accuracy of urine culture results can be affected by pre-analytical factors such as transport delays and storage conditions. The objectives of this study were to analyze urine collection practices and assess the impact of introducing boric acid tubes for urine collection on quantitative urinary bacterial cultures of hospitalized patients in medical wards. METHODS: A quasi-experimental pre-post study conducted in an acute care facility. In the pre-intervention phase (2020-2021), urine samples were transported without preservatives at room temperature. In 2022 (post-intervention), we transitioned to boric acid transport tubes, evaluating its effect on significant bacterial growth (≥ 105 CFU/ml). Bivariate and multivariate analyses identified predictors of culture positivity. RESULTS: Throughout the duration of the study, a total of 12,660 urine cultures were analyzed. Date and time documentation was complete for 38.3% of specimens. Culture positivity was higher with longer processing times: positivity was 21.3% (220/1034) when specimens were processed within 4 h, 28.4% (955/3364) when processed in 4-24 h, and 32.9% (137/417) when processed after 24 h (p < 0.0001). For 4-24-hour processing, positivity decreased from 30.4% (704/2317) pre-intervention to 24.0% (251/1047) post-intervention (p < 0.001), with no significant changes in < 4 or ≥ 24-hour specimens. Stratified analysis by processing time revealed that the intervention was associated with reduced positivity only in cultures processed within 4-24 h (OR 0.80, 95% CI 0.67-0.94; p = 0.008). CONCLUSION: The introduction of boric acid transport tubes predominantly influenced cultures transported within a 4-24-hour window. This presents an opportunity to improve urine tract infection diagnostic practices in healthcare settings.

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.