Point-of-care image-based quantitative urinalysis with commercial reagent strips: Design and clinical evaluation.

Urinalysis is a useful test as an indicator of health or disease and as such, is a part of routine health screening. Urinalysis can be undertaken in many ways, one of which is reagent strips used in the general evaluation of health and to aid in the diagnosis and monitoring of kidney disease. To be effective, the test must be performed properly, and the results interpreted correctly. However, different light conditions and colour perception can vary between users leading to ambiguous readings. This has led to camera devices being used to capture and generate the estimated biomarker concentrations, but image colour can be affected by variations in illumination and inbuilt image processing. Therefore, a new portable device with embedded image processing techniques is presented in this study to provide quantitative measurements that are invariant to changes in illumination. The device includes a novel calibration process and uses the ratio of RGB values to compensate for variations in illumination across an image and improve the accuracy of quantitative measurements. Results show that the proposed calibration method gives consistent homogeneous illumination across the whole image. Comparisons against other existing methods and clinical results show good performance with a correlation to the clinical values. The proposed device can be used for point-of-care testing to provide reliable results consistent with clinical values.

Performance of Urinalysis Parameters in Predicting Urinary Tract Infection: Does One Size Fit All?

In a multihospital cohort study of 3392 patients, positive urinalysis parameters had poor positive predictive value for diagnosing urinary tract infection (UTI). Combined urinalysis parameters (pyuria or nitrite) performed better than pyuria alone for ruling out UTI. However, performance of all urinalysis parameters was poor in older women.

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.

Self-Sustaining Triboelectric Nanosensors for Real-Time Urine Analysis in Smart Toilets.

Healthcare has undergone a revolutionary shift with the advent of smart technologies, and smart toilets (STs) are among the innovative inventions offering non-invasive continuous health monitoring. The present technical challenges toward this development include limited sensitivity of integrated sensors, poor stability, slow response and the requirement external energy supply alongside manual sample collection. In this article, triboelectric nanosensor array (TENSA) is introduced featuring electrodes crafted from laser-induced 3D graphene with functional polymers like polystyrene, polyimide, and polycaprolactone for real-time urine analysis while generating 50 volts output via urine droplet-based triboelectrification. Though modulating interfacial double-layer capacitance, these sensors exhibit exceptional sensitivity and selectivity in detecting a broad spectrum of urinary biomarkers, including ions, glucose, and urea with a classification precision of 95% and concentration identification accuracy of up to 0.97 (R2), supported by artificial neural networks. Upon exposure to urine samples containing elevated levels of Na+, K+, and NH4 +, a notable decrease (ranging from 32% to 68%) is observed in output voltages. Conversely, urea induces an increase up to 13%. Experimental validation confirms the stability, robustness, reliability, and reproducibility of TENSA, representing a significant advancement in healthcare technology, offering the potential for improved disease management and prevention strategies.

Predictive performance of urinalysis for urine culture results according to causative microorganisms: an integrated analysis with artificial intelligence.

Urinary tract infections (UTIs) are pervasive and prevalent in both community and hospital settings. Recent trends in the changes of the causative microorganisms in these infections could affect the effectiveness of urinalysis (UA). We aimed to evaluate the predictive performance of UA for urinary culture test results according to the causative microorganisms. In addition, UA results were integrated with artificial intelligence (AI) methods to improve the predictive power. A total of 360,376 suspected UTI patients were enrolled from two university hospitals and one commercial laboratory. To ensure broad model applicability, only a limited range of clinical data available from commercial laboratories was used in the analyses. Overall, 53,408 (14.8%) patients were identified as having a positive urine culture. Among the UA tests, the combination of leukocyte esterase and nitrite tests showed the highest area under the curve (AUROC, 0.766; 95% CI, 0.764-0.768) for predicting urine culture positivity but performed poorly for Gram-positive bacteriuria (0.642; 0.637-0.647). The application of an AI model improved the predictive power of the model for urine culture results to an AUROC of 0.872 (0.870-0.875), and the model showed superior performance metrics not only for Gram-negative bacteriuria (0.901; 0.899-0.902) but also for Gram-positive bacteriuria (0.745; 0.740-0.749) and funguria (0.872; 0.865-0.879). As the prevalence of non-Escherichia coli-caused UTIs increases, the performance of UA in predicting UTIs could be compromised. The addition of AI technologies has shown potential for improving the predictive performance of UA for urine culture results.IMPORTANCEUA had good performance in predicting urine culture results caused by Gram-negative bacteria, especially for Escherichia coli and Pseudomonas aeruginosa bacteriuria, but had limitations in predicting urine culture results caused by Gram-positive bacteria, including Streptococcus agalactiae and Enterococcus faecalis. We developed and externally validated an AI model incorporating minimal demographic information of patients (age and sex) and laboratory data for UA, complete blood count, and serum creatinine concentrations. The AI model exhibited improved performance in predicting urine culture results across all the causative microorganisms, including Gram-positive bacteria, Gram-negative bacteria, and fungi.

A lipidomics-based method to eliminate negative urine culture in general population.

Urinary tract infections (UTIs) pose a significant challenge to human health. Accurate and timely detection remains pivotal for effective intervention. Current urine culture techniques, while essential, often encounter challenges where urinalysis yields positive results, but subsequent culture testing produces a negative result. This highlights potential discrepancies between the two methods and emphasizes the need for improved correlation in urinary tract infection (UTI) detection. Employing advanced lipidomics techniques, we deployed the fast lipid analysis technique (FLAT) on a clinical cohort suspected of having UTIs. Lipid fingerprinting by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), directly from urine samples without ex vivo growth, correctly identified the common uropathogens within a 1 hour timeframe when compared to urine culture. FLAT analysis also identified urine samples without culturable pathogens (negative UTIs) with 99% microbial identification (ID) agreement, whereas urinalysis showed 37% ID agreement with the gold standard urine culture. In 402 urine samples suspected for UTI from outpatients, FLAT assay rapidly ruled out negative urines without the need for culture in 77% of all cases. The potential impact of this innovative lipidomic-based approach extends beyond conventional diagnostic limitations, offering new avenues for early detection and targeted management of urinary tract infections. This research marks a paradigm shift in urine culture methodology, paving the way for improved clinical outcomes and public health interventions. IMPORTANCE: This study employs a lipidomics-based method that promises to enhance the accuracy and reliability of urine culture diagnostics within 1 hour of sample collection. Our findings underscore the potential of lipidomics as a valuable tool in identifying and characterizing microbial populations present in urine samples and efficiently rule out negative urines, ultimately leading to improved patient care and management of urinary tract infections.

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.

The EFLM European Urinalysis Guideline 2023.

BACKGROUND: The EFLM Task and Finish Group Urinalysis has updated the ECLM European Urinalysis Guidelines (2000) on urinalysis and urine bacterial culture, to improve accuracy of these examinations in European clinical laboratories, and to support diagnostic industry to develop new technologies. RECOMMENDATIONS: Graded recommendations were built in the following areas. MEDICAL NEEDS AND TEST REQUISITION: Strategies of urine testing are described to patients with complicated or uncomplicated urinary tract infection (UTI), and high or low-risk to kidney disease. SPECIMEN COLLECTION: Patient preparation, and urine collection are supported with two quality indicators: contamination rate (cultures), and density of urine (chemistry, particles). CHEMISTRY: Measurements of both urine albumin and α1-microglobulin are recommended for sensitive detection of kidney disease in high-risk patients. Performance specifications are given for urine protein measurements and quality control of multiproperty strip tests. PARTICLES: Procedures for microscopy are reviewed for diagnostic urine particles, including urine bacteria. Technologies in automated particle counting and visual microscopy are updated with advice how to verify new instruments with the reference microscopy. BACTERIOLOGY: Chromogenic agar is recommended as primary medium in urine cultures. Limits of significant growth are reviewed, with an optimised workflow for routine specimens, using leukocyturia to reduce less important antimicrobial susceptibility testing. Automation in bacteriology is encouraged to shorten turn-around times. Matrix assisted laser desorption ionization time-of-flight mass spectrometry is applicable for rapid identification of uropathogens. Aerococcus urinae, A. sanguinicola and Actinotignum schaalii are taken into the list of uropathogens. A reference examination procedure was developed for urine bacterial cultures.

Machine learning-assisted image label-free smartphone platform for rapid segmentation and robust multi-urinalysis.

This study presents a groundbreaking approach for the early detection of chronic kidney disease (CKD) and other urological disorders through an image-label-free, multi-dipstick identification method, eliminating the need for complex machinery, label libraries, or preset coordinates. Our research successfully identified reaction pads on 187 multi-dipsticks, each with 11 pads, leveraging machine learning algorithms trained on human urine data. This technique aims to surpass traditional colourimetric methods and concentration-colour curve fitting, offering more robust and precise community screening and home monitoring capabilities. The developed algorithms enhance the generalizability of machine learning models by extracting primary colours and correcting urine colours on each reaction pad. This method's cost-effectiveness and portability are significant, as it requires no additional equipment beyond a standard smartphone. The system's performance rivals professional medical equipment without auxiliary lighting or flash under regular indoor light conditions, effectively managing false positives and negatives across various categories with remarkable accuracy. In a controlled experimental setting, we found that random forest algorithms, based on a Bagging strategy and applied in the HSV colour space, showed optimal results in smartphone-assisted urinalysis. This study also introduces a novel urine colour correction method, significantly improving machine learning model performance. Additionally, ISO parameters were identified as crucial factors influencing the accuracy of smartphone-based urinalysis in the absence of additional lighting or optical configurations, highlighting the potential of this technology in low-resource settings.

Validity of combined hydration self-assessment measurements to estimate a low vs. high urine concentration in a small sample of (tactical) athletes.

PURPOSE: Relationships between body weight, urine color (Uc), and thirst level (WUT) have been proposed as a simple and inexpensive self-assessment method to predict dehydration. This study aimed to determine if this method also allowed us to accurately identify a low vs. high urine concentration in (tactical) athletes. METHODS: A total of n = 19 Army Reserve Officer Training Corps cadets and club sports athletes (22.7 ± 3.8 years old, of which 13 male) were included in the analysis, providing morning body weight, thirst sensation, and Uc for five consecutive days. Each item received a score 0 or 1, resulting in a WUT score ranging from 0 (likely hydrated) to 3 (very likely dehydrated). WUT model and individual item outcomes were then compared with a ≥ 1.020 urine specific gravity (USG) cut-off indicating a high urine concentration, using descriptive comparisons, generalized linear mixed models, and logistic regression (to calculate the area under the curve (AUC)). RESULTS: WUT score was not significantly predictive of urine concentration, z = 1.59, p = 0.11. The AUC ranged from 0.54 to 0.77 for test days, suggesting a fair AUC on most days. Only Uc was significantly related to urine concentration, z = 2.49, p = 0.01. The accuracy of the WUT model for correctly classifying urine samples with a high concentration was 68% vs. 51% of samples with a low concentration, resulting in an average accuracy of 61%. CONCLUSION: This study shows that WUT scores were not predictive of urine concentration, and the method did not substantially outperform the accuracy of Uc scoring alone.

Urine dipstick blood and acute kidney injury in infants undergoing cardiopulmonary bypass.

BACKGROUND: Cardiopulmonary bypass (CPB) is associated with hemolysis and acute kidney injury (AKI). The study aim was to determine if urine dipstick blood in infants after CPB was associated with AKI and urine neutrophil gelatinase-associated lipocalin (NGAL). METHODS: Infants who underwent CPB at a single center were enrolled prospectively between October 2017 and June 2019. Urine samples prior to CPB and 6 h after CPB cessation were analyzed in batch for NGAL and dipstick blood. AKI was defined using creatinine-based KDIGO criteria within 72 h of CPB. Spearman correlation examined associations between urine dipstick blood and NGAL at each time point. Linear regression estimated the associations between urine dipstick blood and log-transformed NGAL 6 h after CPB. Logistic regression estimated associations and compared discrimination between urine dipstick blood and NGAL for predicting AKI. RESULTS: At baseline, 7/63 samples (11%) had > trace blood. Six hours after CPB, 62/98 samples (63%) had > trace blood and 26% had 3 + (large) blood. In total, 18/98 (18%) with a 6-h post-CPB sample had postoperative AKI. Urine dipstick blood values correlated with urine NGAL 6 h after CPB (r = 0.52, p < 0.01), but not at baseline (r = 0.06, p = 0.66). Those with 3 + (large) blood on urine dipstick had 6 times higher mean NGAL values compared to those with negative/trace blood (mean ratio 6.6, 95%CI 3.1-14.4, p < 0.01). Those with 3 + (large) blood had 8 times higher odds of AKI (OR 7.99, 95%CI 1.5-41.9, p = 0.01). CONCLUSIONS: Urine dipstick blood post CPB may be a simple and inexpensive tool to help predict AKI in infants.

Usefulness of urine dipstick test in the management of adverse events associated with immune checkpoint inhibitors.

PURPOSE: The usefulness of urine dipstick tests (UDTs) in patients with diabetes has been reported. The aim of the present study was to investigate the utility of self-performed UDTs and patient diaries in the management of impaired glucose tolerance, one of the adverse events of immune checkpoint inhibitors (ICIs). METHODS: Patients receiving ICIs underwent self-checks with UDTs twice a week for up to 6 months. Pharmacists checked the results at every patient visit, and by phone every 3 months. The primary endpoint was to prospectively assess whether symptoms recorded in patient diaries and UDTs could reduce unscheduled hospital admissions due to impaired glucose tolerance. The secondary endpoint was the correlation between the symptoms in the patient diaries and UDT results. RESULTS: A total of 112 patients were enrolled in the study. Out of the 3197 planned self-UDTs, 3128 (97.8%) were performed. Forty-four patients (39.3%) were admitted to the hospital, two (1.8%) of whom were admitted due to abnormal glucose tolerance, with one having a positive UDT. There were 46 unscheduled outpatient visits (41.1%), of which five (4.5%) were due to abnormal glucose tolerance symptoms and four were due to a positive UDT. The correlation between descriptions of fatigue or dry mouth in the patient diaries and positive glucose UDTs was 52.4% in sensitivity and 62.4% in specificity. CONCLUSION: Self-monitoring of symptoms and self-performing of UDTs could not reduce the emergency hospitalization rate. However, this approach could be effective in the objective monitoring of patient status, especially regarding glucose intolerance occurrences.

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.

Consistency Analysis of the UF-1500 and UF-5000 Automated Urine Particle Analyzers.

BACKGROUND: The aim was to evaluate the consistency of the results between the UF-1500 and UF-5000, fully automated urine particle analyzers. METHODS: A total of 554 randomly selected inpatient and outpatient urine samples were collected for analysis using the UF-1500, the UF-5000, and by manual microscopic examination. The coincidence rate, intraday repeatability, and interday reproducibility were evaluated on the UF-1500 and UF-5000. To analyze the review flags from the UF-1500, the UF-1500 results were compared to manual microscopy as the gold standard. RESULTS: The repeatability of red blood cells (RBCs), white blood cells (WBCs), epithelial cells (ECs), casts, and bacteria using the UF-1500 and UF-5000 is expressed as the relative standard deviations of the intraday and inter-day measurements. For the UF-1500, the relative standard deviation values ranged from 5.9% to 12.6% and 4.9% to 17.2% for the low and 1.6% to 9.3% and 2.3% to 16.9% for the high samples, respectively. The correlation co-efficient for RBCs, WBCs, ECs, SECs, casts, crystals, and bacteria for the UF-1500 were 0.981, 0.993, 0.968, 0.963, 0.821, 0.783, and 0.992, respectively. Review samples from the UF-1500 were confirmed by microscopic examination. Review flags for all 554 samples included 3 samples with "DEBRIS High" and 23 samples with "RBCs/YLC Abnormal classification". CONCLUSIONS: The identification of various urine components by both instruments meets laboratory requirements. These two instruments with different performances have specific characteristics and should be used based upon the needs of each laboratory.

A Model Based on Automated Urinalysis Parameters for Urothelial Carcinoma Risk Stratification in Suspected Patients.

BACKGROUND: The aim of this study was to develop and validate a risk stratification model for the screening of patients with suspected urothelial carcinoma (UC). METHODS: We enrolled 671 consecutive patients with suspected UC and generated a risk stratification model based on urinary parameters by using an automated urinalysis analyzer (Sysmex UN-9000). All patients received urine cytology examination from January 1, 2019, to October 31, 2022. RESULTS: Out of the 671 patients, 191 (28.5%) were ultimately diagnosed with UC. The four features associated with the presence of malignancy on multivariable analysis can be summarized by using the mnemonic UC-PAAS: UC, protein vs. creatinine ratio (P/C), age, atypical cells (Atyp.C), and small round epithelial cell (SRC). Major criteria include Atyp.C ≥ 0.1/µL (2 points) and age ≥ 65 years (2 points); minor criteria include SRC ≥ 2.7/µL (1 point) and abnormal P/C results (1 point). The model evidenced good discrimination (area under the curve = 0.802, 95% confidence interval [0.756, 0.848]) in the training group. A UC-PAAS cutoff of more than 4 points identified a high-risk population, of whom 37 of 59 (62.7%) had UC; the negative predictive value was 0.867. The validation group yielded similar findings. CONCLUSIONS: We present a urinalysis-based screening model, the UC-PAAS, that may serve as an accessory clinical tool for the evaluation of patients with suspected UC, because the model identifies patients at higher risk who require closer follow-up than others or additional examinations.

Reviving Sternheimer stain: A single-center retrospective study to detect the diagnostic utility of urinary tract infections in the emergency department.

INTRODUCTION: Qualitative urinalysis using the Sternheimer stain is a common method in Japan for identifying bacteriuria, but there is a lack of studies examining its test characteristics. In this study, we aimed to investigate the sensitivity and specificity of the Sternheimer stain for urine culture results and compare it with the sensitivity and specificity of the Gram stain. Our goal was to determine the usefulness of the Sternheimer stain in identifying bacteriuria. PATIENTS AND METHODS: Among 986 patients aged 16 years or older from whom samples for both urinalysis and urine culture were obtained at the emergency room of Tenri Hospital from January 2019 to December 2019, 342 patients with pyuria, defined as the presence of 10 or more white cells per cubic millimeter in a urine specimen, who had not received prior antimicrobial therapy were included. Urine cultures were used for comparison to determine the sensitivity and specificity of Sternheimer and Gram stain in this patient group. A positive Sternheimer stain result was defined as bacteriuria ≥ (1+), and that of Gram stain was defined as ≥ 1/1 field of high-power ( × 1000) oil immersion. RESULTS: Using urine culture results for comparison, the sensitivity of Sternheimer stain was 92.2%, the specificity was 48.5%, the positive likelihood ratio was 1.79, and the negative likelihood ratio was 0.16. DISCUSSION: Sternheimer stain is a rapid and useful method to exclude bacteriuria in a group of patients with pyuria in the emergency department.

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.

The role of age and sex in non-linear dilution adjustment of spot urine arsenic.

BACKGROUND: Previous research introduced V-PFCRC as an effective spot urinary dilution adjustment method for various metal analytes, including the major environmental toxin arsenic. V-PFCRC normalizes analytes to 1 g/L creatinine (CRN) by adopting more advanced power-functional corrective equations accounting for variation in exposure level. This study expands on previous work by examining the impacts of age and sex on corrective functions. METHODS: Literature review of the effects of sex and age on urinary dilution and the excretion of CRN and arsenic. Data analysis included a Data Set 1 of 5,752 urine samples and a partly overlapping Data Set 2 of 1,154 combined EDTA blood and urine samples. Both sets were classified into age bands, and the means, medians, and interquartile ranges for CRN and TWuAs in uncorrected (UC), conventionally CRN-corrected (CCRC), simple power-functional (S-PFCRC), sex-aggregated (V-PFCRC SA), and sex-differentiated V-PFCRC SD modes were compared. Correlation analyses assessed residual relationships between CRN, TWuAs, and age. V-PFCRC functions were compared across three numerically similar age groups and both sexes. The efficacy of systemic dilution adjustment error compensation was evaluated through power-functional regression analysis of residual CRN and the association between arsenic in blood and all tested urinary result modes. RESULTS: Significant sex differences in UC and blood were neutralized by CCRC and reduced by V-PFCRC. Age showed a positive association with blood arsenic and TWuAs in all result modes, indicating factual increments in exposure. Sex-differentiated V-PFCRC best matched the sex-age kinetics of blood arsenic. V-PFCRC formulas varied by sex and age and appeared to reflect urinary osmolality sex-age-kinetics reported in previous research. V-PFCRC minimized residual biases of CRN on TWuAs across all age groups and sexes, demonstrating improved standardization efficacy compared to UC and CCRC arsenic. INTERPRETATION: Sex differences in UC and CCRC arsenic are primarily attributable to urinary dilution and are effectively compensated by V-PFCRC. While the sex and age influence on V-PFCRC formulas align with sex- and age-specific urinary osmolality and assumed baseline vasopressor activities, their impact on correction validity for entire collectives is minimal. CONCLUSION: The V-PFCRC method offers a robust correction for urinary arsenic dilution, significantly reducing systemic dilution adjustment errors. Its application in various demographic contexts enhances the accuracy of urinary biomarker assessments, benefiting clinical and epidemiological research. V-PFCRC effectively compensates for sex differences in urinary arsenic. Age-related increases in TWuAs are exposure-related and should be additionally accounted for by algebraic normalization, covariate models, or standard range adjustments.

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.

Urinary tract infections in young infants with a normal urine dipstick.

AIM: To assess missed urinary tract infections (UTI) in febrile infants ≤2 months when adhering to recent guidelines suggesting not to send a urine culture with a negative dipstick. METHODS: A retrospective cohort study of 308 infants ≤2 months with a positive urine culture admitted in 2013-2023, divided into subgroups without exposure to urine dipstick results: 'urosepsis' (UTI with bacteraemia), 'UTI' (positive urine culture, elevated inflammatory markers, no other cause of fever) and 'bacteriuria' (positive urine culture, not meeting the above-mentioned criteria). After retrieving the dipstick results, the 'missed UTI' group (UTI+ negative dipstick) was described. RESULTS: A negative dipstick was found in 2/20 (10%), 32/127 (25%) and 126/161 (78%) of infants with 'urosepsis', 'UTI' and 'bacteriuria', respectively. In the 'missed UTI' group (n = 34), there were more non-Escherichia coli UTI (68% vs. 9% with positive dipstick, p < 0.001), and lower inflammatory markers (leukocytes 15.5 vs. 17.2 k/µL, p = 0.007, C-reactive protein 21 vs. 58 mg/L, p < 0.001). Three infants had high-grade vesicoureteral reflux (VUR) and renal scarring. CONCLUSIONS: There is a non-negligible rate of infants ≤2 months with UTI and without pyuria, including those with urosepsis, VUR and renal scarring. We suggest obtaining a urine culture regardless of dipstick results.