[Evaluation of an algorithm based on quantitative assays of glomerular parameters (albuminuria and urinary IgG) for proteinuria typing]. / Évaluation d'un algorithme basé sur des dosages quantitatifs de paramètres glomérulaires (albuminurie et IgG urinaires) afin de typer une protéinurie.

The first orientation test for proteinuria typing is electrophoresis. However, this technique has several drawbacks, such as delayed turnaround time and subjective readings. Some laboratories therefore use quantitative assays of glomerular markers combined with tubular markers. However, the cost of reagents and the instability of certain markers are significant drawbacks for some peripheral laboratories. The aim of this study is to evaluate the implementation of an algorithm based on parameters that can be used by all laboratories for proteinuria typing within a timeframe compatible with the urgency of the situation. Albuminuria and urinary IgG were determined on 161 urines. ROC curves were produced, using urine electrophoresis read by an expert center as the reference method. The decision thresholds used are: glomerular proteinuria is defined by a Albumin+IgGproteinsratio greater than 75.4% (100% specificity), and tubular or overload proteinuria is defined by by a Albuminproteinsratio less than 37.3% (100% sensitivity). Agreement between the results of the algorithm selected and the reference method used in our study was 88 %, with a kappa value of 0.807 (95% CI [0.729 to 0.885]). The algorithm's performance suggests that it can find its place in the diagnostic strategy for clinically significant proteinuria, despite its limited indications. It is up to each biologist to assess the value of this algorithm in relation to the recruitment, habits and needs of clinicians.

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.

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.

Improving Management: Novel Guidelines for Diagnosing Urinary Tract Infection in Infants.

BACKGROUND: The diagnosis of urinary tract infection (UTI) in the pediatric field remains a topic of debate. In 2011, the American Academy of Pediatrics (AAP) introduced new guidelines for the diagnosis of UTI in patients 2-24 months old. However, concerns were raised regarding these guidelines. METHODS: This is a retrospective data extraction study that included patients 0-6 months of age who were examined in the pediatric emergency department between 2016 and 2021 and had a urine culture. For each patient, we recorded the diagnosis documented in the medical records, the diagnosis based on the AAP and Israeli guidelines, and the diagnosis according to our 2 proposed protocols. We then compared the percentage of UTI diagnoses according to each diagnostic guideline. RESULTS: A total of 1432 patients under the age of 6 months underwent urine culture testing during the study period. A total of 83 (5.81%) of these patients were diagnosed with UTI according to the AAP guidelines, 184 of the patients (12.8%) were diagnosed with UTI according to the Israeli guidelines, 102 (7.1%) and 109 (7.6%) of the patients were diagnosed with UTI according to our first and second proposed guidelines, respectively. CONCLUSIONS: We propose a new diagnostic method (guidelines II) that is suitable for patients older than 2 months, with obligatory criteria of abnormal urine test and a lower threshold for the colony count required for diagnosis compared to the AAP guidelines. Further research is required to examine the sensitivity and specificity of our proposed guidelines, so it may replace the current diverse guidelines.

Can sodium and potassium measured in timed voids be used as reference instruments for validating self-report instruments? Results from a urine calibration study.

BACKGROUND: Sodium and potassium measured in 24-h urine collections are often used as reference measurements to validate self-reported dietary intake instruments. OBJECTIVES: To evaluate whether collection and analysis of a limited number of urine voids at specified times during the day ("timed voids") can provide alternative reference measurements, and to identify their optimal number and timing. METHODS: We used data from a urine calibration study among 441 adults aged 18-39 y. Participants collected each urine void in a separate container for 24 h and recorded the collection time. For the same day, they reported dietary intake using a 24-h recall. Urinary sodium and potassium were analyzed in a 24-h composite sample and in 4 timed voids (morning, afternoon, evening, and overnight). Linear regression models were used to develop equations predicting log-transformed 24-h urinary sodium or potassium levels using each of the 4 single timed voids, 6 pairs, and 4 triples. The equations also included age, sex, race, BMI (kg/m2), and log creatinine. Optimal combinations minimizing the mean squared prediction error were selected, and the observed and predicted 24-h levels were then used as reference measures to estimate the group bias and attenuation factors of the 24-h dietary recall. These estimates were compared. RESULTS: Optimal combinations found were as follows: single voids-evening; paired voids-afternoon + overnight (sodium) and morning + evening (potassium); and triple voids-morning + evening + overnight (sodium) and morning + afternoon + evening (potassium). Predicted 24-h urinary levels estimated 24-h recall group biases and attenuation factors without apparent bias, but with less precision than observed 24-h urinary levels. To recover lost precision, it was estimated that sample sizes need to be increased by ∼2.6-2.7 times for a single void, 1.7-2.1 times for paired voids, and 1.5-1.6 times for triple voids. CONCLUSIONS: Our results provide the basis for further development of new reference biomarkers based on timed voids. CLINICAL TRIAL REGISTRY: clinicaltrials.gov as NCT01631240.

Nephron mass determines the excretion rate of urinary extracellular vesicles.

Urinary extracellular vesicles (uEVs) are emerging as non-invasive biomarkers for various kidney diseases, but it is unknown how differences in nephron mass impact uEV excretion. To address this, uEV excretion was measured before and after human kidney donor nephrectomy and rat nephrectomy. In male and female donors, uEVs were quantified in cell-free spot and 24-h urine samples using nanoparticle tracking analysis (NTA), EVQuant, and CD9-time-resolved fluorescence immunoassay. Female donors had significantly lower total kidney volume (TKV) and excreted 49% fewer uEVs than male donors. uEV excretion correlated positively with estimated glomerular filtration rate (eGFR), creatinine clearance, and TKV (R's between 0.6 and 0.7). uEV excretion rate could also be predicted from spot urines after multiplying spot uEV/creatinine by 24-h urine creatinine. Donor nephrectomy reduced eGFR by 36% ± 10%, but the excretion of uEVs by only 16% (CD9+ uEVs -37%, CD9- uEVs no decrease). Donor nephrectomy increased the podocyte marker WT-1 and the proximal tubule markers NHE3, NaPi-IIa, and cubilin in uEVs two- to four-fold when correcting for the nephrectomy. In rats, the changes in GFR and kidney weight correlated with the changes in uEV excretion rate (R = 0.46 and 0.60, P < 0.01). Furthermore, the estimated degree of hypertrophy matched the change in uEV excretion rate (1.4- to 1.5-fold after uninephrectomy and four-fold after 5/6th nephrectomy). Taken together, our data show that uEV excretion depends on nephron mass, and that nephrectomy reduces uEV excretion less than expected based on nephron loss due to compensatory hypertrophy. The major implication of our findings is that a measure for nephron mass or uEV excretion rate should be included when comparing uEV biomarkers between individuals.

Reference values of urine protein/creatinine ratio in healthy Dalian adults.

BACKGROUND: The urine protein/creatinine ratio (UPCR) is commonly used in current clinical practice. However, there are only few published clinical data on UPCR from large cohorts of Chinese adults. This study aimed to determine the overall and age- and sex-specific UPCR reference values for healthy Dalian adults. METHODS: According to the Clinical & Laboratory Standards Institute EP28-A3c guidelines, 1321 healthy Dalian adults (646 men and 675 women) aged 20-69 years were enrolled. Urine protein and creatinine levels were analyzed in the random morning spot urine samples, and UPCR was calculated. The 95th percentile of the UPCR was used as the normal upper limit. The Mann-Whitney U test was used to test differences among groups. RESULTS: The UPCR reference value was 141.7 mg/g for the entire cohort, 128.7 mg/g for men, and 150.8 mg/g for women. In addition, women had relatively higher UPCR values than men in the same age group. We also compared the UPCR reference values between different estimated glomerular filtration rate (eGFR) groups and found that women had significantly higher UPCR values than men in the normal eGFR groups. CONCLUSIONS: This study provides the overall and age- and sex-specific UPCR reference values for healthy Dalian adults.

Acute kidney injury detection using refined and physiological-feature augmented urine output.

Acute kidney injury (AKI) is common in the intensive care unit, where it is associated with increased mortality. AKI is often defined using creatinine and urine output criteria. The creatinine-based definition is more reliable but less expedient, whereas the urine output based definition is rapid but less reliable. Our goal is to examine the urine output criterion and augment it with physiological features for better agreement with creatinine-based definitions of AKI. The objectives are threefold: (1) to characterize the baseline agreement of urine output and creatinine definitions of AKI; (2) to refine the urine output criteria to identify the thresholds that best agree with the creatinine-based definition; and (3) to build generalized estimating equation (GEE) and generalized linear mixed-effects (GLME) models with static and time-varying features to improve the accuracy of a near-real-time marker for AKI. We performed a retrospective observational study using data from two independent critical care databases, MIMIC-III and eICU, for critically ill patients who developed AKI in intensive care units. We found that the conventional urine output criterion (6 hr, 0.5 ml/kg/h) has specificity and sensitivity of 0.49 and 0.54 for MIMIC-III database; and specificity and sensitivity of 0.38 and 0.56 for eICU. Secondly, urine output thresholds of 12 hours and 0.6 ml/kg/h have specificity and sensitivity of 0.58 and 0.48 for MIMIC-III; and urine output thresholds of 10 hours and 0.6 ml/kg/h have specificity and sensitivity of 0.49 and 0.48 for eICU. Thirdly, the GEE model of four hours duration augmented with static and time-varying features can achieve a specificity and sensitivity of 0.66 and 0.61 for MIMIC-III; and specificity and sensitivity of 0.66 and 0.64 for eICU. The GLME model of four hours duration augmented with static and time-varying features can achieve a specificity and sensitivity of 0.71 and 0.55 for MIMIC-III; and specificity and sensitivity of 0.66 and 0.60 for eICU. The GEE model has greater performance than the GLME model, however, the GLME model is more reflective of the variables as fixed effects or random effects. The significant improvement in performance, relative to current definitions, when augmenting with patient features, suggest the need of incorporating these features when detecting disease onset and modeling at window-level rather than patient-level.

Application of a six sigma model to evaluate the analytical performance of urinary biochemical analytes and design a risk-based statistical quality control strategy for these assays: A multicenter study.

BACKGROUND: The six sigma model has been widely used in clinical laboratory quality management. In this study, we first applied the six sigma model to (a) evaluate the analytical performance of urinary biochemical analytes across five laboratories, (b) design risk-based statistical quality control (SQC) strategies, and (c) formulate improvement measures for each of the analytes when needed. METHODS: Internal quality control (IQC) and external quality assessment (EQA) data for urinary biochemical analytes were collected from five laboratories, and the sigma value of each analyte was calculated based on coefficients of variation, bias, and total allowable error (TEa). Normalized sigma method decision charts for these urinary biochemical analytes were then generated. Risk-based SQC strategies and improvement measures were formulated for each laboratory according to the flowchart of Westgard sigma rules, including run sizes and the quality goal index (QGI). RESULTS: Sigma values of urinary biochemical analytes were significantly different at different quality control levels. Although identical detection platforms with matching reagents were used, differences in these analytes were also observed between laboratories. Risk-based SQC strategies for urinary biochemical analytes were formulated based on the flowchart of Westgard sigma rules, including run size and analytical performance. Appropriate improvement measures were implemented for urinary biochemical analytes with analytical performance lower than six sigma according to the QGI calculation. CONCLUSIONS: In multilocation laboratory systems, a six sigma model is an excellent quality management tool and can quantitatively evaluate analytical performance and guide risk-based SQC strategy development and improvement measure implementation.

Improving data quality in liquid chromatography-mass spectrometry metabolomics of human urine.

Signal variation is a common drawback in untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS), mainly due to the complexity of biological matrices and reduced sample preparation, which results in the accumulation of sample components in the column and the ion source. Here we propose a simple, easy to implement approach to improve data quality in untargeted metabolomics by LC-MS. This approach involves the use of a divert valve to direct the column effluent to waste at the beginning of the chromatographic run and during column cleanup and equilibration, in combination with longer column cleanups in between injections. Our approach was tested using urine samples collected from patients after renal transplantation. Analytical responses were contrasted before and after introducing these modifications by analyzing a batch of untargeted metabolomics data. A significant improvement in peak area repeatability was observed for the quality controls, with relative standard deviations (RSDs) for several metabolites decreasing from ∼60% to ∼10% when our approach was introduced. Similarly, RSDs of peak areas for internal standards improved from ∼40% to ∼10%. Furthermore, calibrant solutions were more consistent after introducing these modifications when comparing peak areas of solutions injected at the beginning and the end of each analytical sequence. Therefore, we recommend the use of a divert valve and extended column cleanup as a powerful strategy to improve data quality in untargeted metabolomics, especially for very complex types of samples where minimum sample preparation is required, such as in this untargeted metabolomics study with urine from renal transplanted patients.

Individual variability in human urinary metabolites identifies age-related, body mass index-related, and sex-related biomarkers.

BACKGROUND: Metabolites present in human urine can be influenced by individual physiological parameters (e.g., body mass index [BMI], age, and sex). Observation of altered metabolites concentrations could provide insight into underlying disease pathology, disease prognosis and diagnosis, and facilitate discovery of novel biomarkers. METHODS: Quantitative metabolomics analysis in the urine of 183 healthy individuals was performed based on high-resolution liquid chromatography-mass spectrometry (LC-MS). Coefficients of variation were obtained for 109 urine metabolites of all the 183 human healthy subjects. RESULTS: Three urine metabolites (such as dehydroepiandrosterone sulfate, acetaminophen glucuronide, and p-anisic acid) with CV183  > 0.3, for which metabolomics studies have been scarce, are considered highly variable here. We identified 30 age-related metabolites, 18 BMI-related metabolites, and 42 sex-related metabolites. Among the identified metabolites, three metabolites were found to be associated with all three physiological parameters (age, BMI, and sex), which included dehydroepiandrosterone sulfate, 3-methylcrotonylglycine and N-acetyl-aspartic acid. Pearson's coefficients demonstrated that some age-, BMI-, and sex-related compounds are strongly correlated, suggesting that age, BMI, and sex could affect them concomitantly. CONCLUSION: Metabolic differences between distinct physiological statuses were found to be related to several metabolic pathways (such as the caffeine metabolism, the amino acid metabolism, and the carbohydrate metabolism), and these findings may be key for the discovery of new diagnostics and treatments as well as new understandings on the mechanisms of some related diseases.

Application of Bar Adsorptive Microextraction for the Determination of Levels of Tricyclic Antidepressants in Urine Samples.

This work entailed the development, optimization, validation, and application of a novel analytical approach, using the bar adsorptive microextraction technique (BAµE), for the determination of the six most common tricyclic antidepressants (TCAs; amitriptyline, mianserin, trimipramine, imipramine, mirtazapine and dosulepin) in urine matrices. To achieve this goal, we employed, for the first time, new generation microextraction devices coated with convenient sorbent phases, polymers and novel activated carbons prepared from biomaterial waste, in combination with large-volume-injection gas chromatography-mass spectrometry operating in selected-ion monitoring mode (LVI-GC-MS(SIM)). Preliminary assays on sorbent coatings, showed that the polymeric phases present a much more effective performance, as the tested biosorbents exhibited low efficiency for application in microextraction techniques. By using BAµE coated with C18 polymer, under optimized experimental conditions, the detection limits achieved for the six TCAs ranged from 0.2 to 1.6 µg L-1 and, weighted linear regressions resulted in remarkable linearity (r2 > 0.9960) between 10.0 and 1000.0 µg L-1. The developed analytical methodology (BAµE(C18)/LVI-GC-MS(SIM)) provided suitable matrix effects (90.2-112.9%, RSD ≤ 13.9%), high recovery yields (92.3-111.5%, RSD ≤ 12.3%) and a remarkable overall process efficiency (ranging from 84.9% to 124.3%, RSD ≤ 13.9%). The developed and validated methodology was successfully applied for screening the six TCAs in real urine matrices. The proposed analytical methodology proved to be an eco-user-friendly approach to monitor trace levels of TCAs in complex urine matrices and an outstanding analytical alternative in comparison with other microextraction-based techniques.

Laboratory Reporting Parameters of Microhematuria: Implications for Interpreting the 2020 AUA Guideline.

OBJECTIVE: To explore how laboratories in the United States (U.S.) report red blood cell per high powered field (RBC/HPF) counts on urinalysis and to evaluate whether this methodology permits effective risk stratification in accordance with the 2020 AUA/SUFU microhematuria guidelines. MATERIALS AND METHODS: Reporting methods for RBC/HPF counts (ranges, or actual counts) were collected by querying urologists in U.S. academic medical institutions or commercial laboratories. We explore whether (1) the reporting schemes were concordant with the risk strata in the new microhematuria guideline (3-10 [low risk], 11-25 [intermediate risk], and more than 25 [high risk]), and (2) evaluate the potential for risk group misclassification based on reporting methodology. RESULTS: Data were available for 141 laboratories. Seventy-two (51%) use RBC/HPF ranges, while the remainder use actual counts (or counts to a threshold). Sixty (42%) report range cutoffs which are not concordant with the microhematuria guidelines risk groups. Furthermore, fifty-six (40%) do not include the cutoff of 25 RBC/HPF which could potentially misclassify intermediate and high risk groups. Finally, sixteen (11%) do not include the cut-off of 3 RBC/HPF that defines the presence of microhematuria. CONCLUSION: A significant number of laboratories report RBC/HPF counts in ranges that differ from thresholds in the 2020 AUA/SUFU guideline. The implication is potential misclassification of microhematuria both at minimum threshold diagnosis (3 RBC/HPF), and additionally between intermediate and high risk groups. Standardization of reporting schemes to actual RBC/HPF counts may allow improved adherence to guidelines while providing data for future guideline development.

Accuracy of urine flow cytometry and urine test strip in predicting relevant bacteriuria in different patient populations.

BACKGROUND: Urinary tract infection (UTI) is diagnosed combining urinary symptoms with demonstration of urine culture growth above a given threshold. Our aim was to compare the diagnostic accuracy of Urine Flow Cytometry (UFC) with urine test strip in predicting bacterial growth and in identifying contaminated urine samples, and to derive an algorithm to identify relevant bacterial growth for clinical use. METHODS: Species identification and colony-forming unit (CFU/ml) quantification from bacterial cultures were matched to corresponding cellular (leucocytes/epithelial cells) and bacteria counts per µl. Results comprise samples analysed between 2013 and 2015 for which urine culture (reference standard) and UFC and urine test strip data (index tests, Sysmex UX-2000) were available. RESULTS: 47,572 urine samples of 26,256 patients were analysed. Bacteria counts used to predict bacterial growth of ≥105 CFU/ml showed an accuracy with an area under the receiver operating characteristic curve of > 93% compared to 82% using leukocyte counts. The relevant bacteriuria rule-out cut-off of 50 bacteria/µl reached a negative predictive value of 98, 91 and 89% and the rule-in cut-off of 250 bacteria/µl identified relevant bacteriuria with an overall positive predictive value of 67, 72 and 73% for microbiologically defined bacteriuria thresholds of 105, 104 or 103 CFU/ml, respectively. Measured epithelial cell counts by UFC could not identify contaminated urine. CONCLUSIONS: Prediction of a relevant bacterial growth by bacteria counts was most accurate and was a better predictor than leucocyte counts independently of the source of the urine and the medical specialty ordering the test (medical, surgical or others).