Reduction of misdiagnosis in urinary tract infections during pregnancy: The role of adjusted urine flow cytometry parameters.

INTRODUCTION: Urinary tract infections (UTIs) pose a significant health concern, particularly among pregnant women, for whom accurate diagnosis is essential. However, the use of Urine flow cytometry (UF) for detecting UTIs in this demographic often results in misdiagnosis. The objective of this study was to explore the reasons behind these diagnostic errors and to develop a strategy to minimize the rate of UTI misdiagnosis in pregnant women. MATERIAL AND METHODS: The study enrolled 1,200 women aged 18 to 40 years, categorized into pregnant and non-pregnant groups. UTIs were diagnosed using urine bacterial culture, microscopic examination, and UF, followed by statistical analysis to identify any discrepancies in diagnosis between the groups. Following the calibration of UF analyzer's parameters, the most effective CR(WBC)-CW-FSC-P Gain setting for diagnosing UTIs in pregnant women through UF was ascertained by applying the Youden index. RESULTS: The clinical diagnosis rate of UTIs was significantly higher in pregnant women (40.91%) compared to non-pregnant women (20.26%). However, urine microscopy and bacterial culture showed no significant difference in the rates of UTIs between the two groups, suggesting a potential for misdiagnosis. The false-positive rate for WBCs detected by UF was 30.43%, and adjusting the CR(WBC)-CW-FSC-P Gain value of UF reduced the false-positive rate to 9.45%. CONCLUSION: The incidence of UTIs in pregnant women may be overestimated because of the limitations inherent to UF. Adjusting the parameters of the UF analyzer, particularly the CR(WBC)-CW-FSC-P Gain value, can significantly reduce the rate of UTI misdiagnosis in pregnant women.

Reliability of the spot urine protein/creatinine ratio for assessing proteinuria in patients with renal disease.

OBJECTIVE: This study aimed to assess the reliability of the spot urine protein/creatinine (sP/Cr) ratio for evaluating proteinuria across different ranges and renal functions. METHODS: In this retrospective study, we analyzed 24-hour urine protein excretion (24 h UP) and sP/Cr measurements in 216 patients with renal disease. Pearson correlation and Bland-Altman analyses were performed to evaluate the correlation and agreement between 24 h UP and the sP/Cr. RESULTS: The patients were categorized into the following three 24 h UP groups: 150 to 299 mg/24 hours, 300 to 3499 mg/24 hours, and >3500 mg/24 hours. Significant positive correlations were found between the sP/Cr and the first two 24 h UP groups (r = 0.9104 and r = 0.9721, respectively) but not between the third group (r = 0.3110). Bland-Altman analysis confirmed good agreement in the group with <3500 mg/day proteinuria. Estimated glomerular filtration rates ≥60 mL/minute and <60 mL/minute were significantly correlated with the sP/Cr (r = 0.8714 and r = 0.4516, respectively). CONCLUSION: The sP/Cr ratio is a reliable indicator for non-nephrotic proteinuria, irrespective of renal function, but is unreliable for nephrotic-range proteinuria.

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.

Dryfilm-ATR-FTIR analysis of urinary profiles as a point-of-care tool to evaluate aerobic exercise.

The understanding of metabolic alterations triggered by intense exercise can provide a biological basis for the development of new training and recovery methods. One popular way to monitor these changes is the non-invasive analysis of the composition of urine. This work evaluates the use of attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and multivariate analysis as a rapid and cost-effective way to investigate changes in urine composition after intense exercise. The urine FTIR spectra of 21 volunteers (14 going through aerobic exercise and 7 controls) were measured before and immediately, 2, 5, 11, and 24 h after running 10 km. Principal component analysis (PCA) and partial least squares analysis (PLS) regression were used to investigate the changes in the spectra as a function of the recovery time. PLS models obtained for the prediction of the time points in the exercise group were deemed significant (p < 0.05, rand t-test permutation testing in cross-validation), showing changes in the urine composition after the exercise, reaching a maximum after 11 hours as opposed to the control group which did not show any significant relationship with the recovery time. In a second step, spectra of the protean extract isolated from urines at significant timepoints (before, immediately after, and 11 hours after exercise) were measured. The PCA of the protein spectra showed clear differences in the spectra obtained at the separation between the recovery time points, especially after the end of the exercise, where the protein profile was significantly different from the other times. Results indicate that the technique was able to find differences in the urine after physical exertion and holds strong potential for an easy-to-use and simple screening metabolic evaluation of recovery methods.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

Role of urine Gram stain in young febrile infants with a suspected urinary tract infection: a cohort study.

OBJECTIVE: To analyse the performance of the urine Gram stain for predicting a positive urine culture (UC) in young infants with fever without source (FWS) and pyuria. DESIGN: Observational study; secondary analysis of a prospective registry-based cohort study. SETTING: Paediatric emergency department; tertiary teaching hospital. PATIENTS: Infants ≤90 days old with FWS, pyuria and urine Gram stain requested seen between 2010 and 2022. MAIN OUTCOME MEASURE: Performance of the Gram stain, defined as positive if any bacteria were seen, for predicting urinary tract infection (UTI: UC by urethral catheterisation growing >10 000 CFU/mL of a single bacterial pathogen). RESULTS: Among 367 febrile infants with pyuria, 281 (76.6%) had a positive Gram stain and 306 (83.3%) had a positive UC (277; 90.5% Escherichia coli).Rates of positive UC in patients with positive and negative Gram stains were 97.2% and 38.4%, respectively (p<0.01), showing a sensitivity of 89.2% (95% CI: 85.2% to 92.2%) and a specificity of 86.9% (95% CI: 76.2% to 93.2%). Sensitivity was lower for diagnosing UTIs caused by bacteria other than E. coli (69.0% vs 91.3% for UTIs caused by E. coli; p<0.01).Two (2.1%) of the 86 infants with negative Gram stains were diagnosed with bacteraemia unrelated to a UTI (Streptococcus pneumoniae and Staphylococcus aureus). CONCLUSIONS: Around a third of infants with pyuria and a negative Gram stain will eventually be diagnosed with a UTI. These patients have a higher rate of UTIs caused by bacteria other than E. coli. Bacterial infections other than UTIs should also be considered in such cases.

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.