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).

Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study.

INTRODUCTION: Because of its ease of collection, urine is one of the most commonly used matrices for metabolomics studies. However, unlike other biofluids, urine exhibits tremendous variability that can introduce confounding inconsistency during result interpretation. Despite many existing techniques to normalize urine samples, there is still no consensus on either which method is most appropriate or how to evaluate these methods. OBJECTIVES: To investigate the impact of several methods and combinations of methods conventionally used in urine metabolomics on the statistical discrimination of two groups in a simple metabolomics study. METHODS: We applied 14 different strategies of normalization to forty urine samples analysed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). To evaluate the impact of these different strategies, we relied on the ability of each method to reduce confounding variability while retaining variability of interest, as well as the predictability of statistical models. RESULTS: Among all tested normalization methods, osmolality-based normalization gave the best results. Moreover, we demonstrated that normalization using a specific dilution prior to the analysis outperformed post-acquisition normalization. We also demonstrated that the combination of various normalization methods does not necessarily improve statistical discrimination. CONCLUSIONS: This study re-emphasized the importance of normalizing urine samples for metabolomics studies. In addition, it appeared that the choice of method had a significant impact on result quality. Consequently, we suggest osmolality-based normalization as the best method for normalizing urine samples. TRIAL REGISTRATION NUMBER: NCT03335644.

Pyuria and Urine Concentration for Identifying Urinary Tract Infection in Young Children.

OBJECTIVES: Accuracy of pyuria for urinary tract infection (UTI) varies with urine concentration. Our objective of this study was to determine the optimal white blood cell (WBC) cutoff for UTI in young children at different urine concentrations as measured by urine specific gravity. METHODS: Retrospective cross-sectional study of children <24 months of age evaluated in the emergency department for suspected UTI with paired urinalysis and urine culture during a 6-year period. The primary outcome was positive urine culture result as described in the American Academy of Pediatrics clinical practice guideline culture thresholds. Test characteristics for microscopic pyuria cut points and positive leukocyte esterase (LE) were calculated across 3 urine specific gravity groups: low <1.011, moderate 1.011 to 1.020, and high >1.020. RESULTS: Of the total 24 171 patients analyzed, urine culture result was positive in 2003 (8.3%). Urine was obtained by transurethral in-and-out catheterization in 97.9%. Optimal WBC cutoffs per high-power field (HPF) were 3 (positive likelihood ratio [LR+] 10.5; negative likelihood ratio [LR-] 0.12) at low, 6 (LR+ 12; LR- 0.14) at moderate, and 8 (LR+ 11.1; LR- 0.35) at high urine concentrations. Likelihood ratios for small positive LE from low to high urine concentrations (LR+ 25.2, LR- 0.12; LR+ 33.1, LR- 0.15; LR+ 37.6, LR- 0.41) remained excellent. CONCLUSIONS: Optimal pyuria cut point in predicting positive urine culture results changes with urine concentration in young children. Pyuria thresholds of 3 WBCs per HPF at low urine concentrations whereas 8 WBCs per HPF at high urine concentrations have optimal predictive value for UTI. Positive LE is a strong predictor of UTI regardless of urine concentration.

The Urinary Polyomavirus-Haufen Test: A Highly Predictive Non-Invasive Biomarker to Distinguish "Presumptive" from "Definitive" Polyomavirus Nephropathy: How to Use It-When to Use It-How Does It Compare to PCR Based Assays?

"Definitive" biopsy proven polyomavirus nephropathy (PyVN), usually caused by BK polyomavirus (BKPyV), remains a significant infection of kidney transplants. Diagnosis depends upon an allograft biopsy and outcome depends upon early intervention. Here, we report data on a non-invasive biomarker for PyVN, the urinary PyV-Haufen test. Test results were compared to those of conventional laboratory assays targeting PyV replication, i.e., BKPy-viremia, -viruria and urinary decoy cell shedding. Of 809 kidney transplant recipients, 228 (28%) showed PyV replication with decoy cell shedding and/or BKPy-viremia by quantitative PCR; only a subset of 81/228 (36%) showed "definitive" PyVN. Sensitivity and specificity for identifying patients with PyVN was: 100% and 98%, respectively, urinary PyV-Haufen test; 50% and 54%, respectively, urinary decoy cell shedding; 97% and 32%, respectively, BKPy-viremia with cut-off of ≥250 viral copies/mL; 66% and 80%, respectively, for BKPy-viremia ≥104 viral copies/mL. The PyV-Haufen test showed a very strong correlation with the severity of PyVN (Spearman's ρ = 0.84) and the Banff PyVN disease classes (p < 0.001). In comparison, BKPy-viremia and -viruria levels by PCR displayed modest correlations with PyVN severity (Spearman's ρ = 0.35 and 0.36, respectively) and were not significantly associated with disease classes. No association was found between decoy cell shedding and PyVN severity or disease classes. Pilot data demonstrated that PyVN resolution with decreasing Banff pvl-scores was reflected by a gradual decrease in PyV-Haufen shedding; such a tight association was not noted for BKPy-viremia. In conclusion, urinary PyV-Haufen testing is a highly specific, non-invasive method to accurately diagnose patients with "definitive" PyVN and to optimize patient management. Assay specifics are discussed.

Urine and stone analysis for the investigation of the renal stone former: a consensus conference.

The Consensus Group deliberated on a number of questions concerning urine and stone analysis over a period of months, and then met to develop consensus. The Group concluded that analyses of urine and stones should be routine in the diagnosis and treatment of urinary stone diseases. At present, the 24-h urine is the most useful type of urine collection, and accepted methods for analysis are described. Patient education is also important for obtaining a proper urine sample. Graphical methods for reporting urine analysis results can be helpful both for the physician and for educating the patient as to proper dietary changes that could be beneficial. Proper analysis of stones is also essential for diagnosis and management of patients. The Consensus Group also agreed that research has shown that evaluation of urinary crystals could be very valuable, but the Group also recognizes that existing methods for assessment of crystalluria do not allow this to be part of stone treatment in many places.

Evaluation of conductivity-based osmolality measurement in urine using the Sysmex UF5000.

BACKGROUND: Automated flow cytometry-based urine analyzer is increasingly being used to identify and enumerate cells and particles in urine specimens. It measures electrical conductivity which could be transformed to osmolality. Using this machine, all urine specimens could be screened for osmolality without requiring a separate dedicated device. We evaluated the performance of the new instrument, the UF-5000 (Sysmex Corporation), in the measurement of urine osmolality. METHODS: The precision of urine osmolality measurement by the UF-5000 was evaluated for 20 days and 4 times a day for 2 concentrations. The linearity and detection capability were evaluated according to the Clinical and Laboratory Standards Institute guidelines. For comparison, 270 random urine specimens from patients were tested simultaneously using the UF5000 and the OsmoPro micro-osmometer (Advanced instruments). RESULTS: The laboratory-based coefficient variations were less than 5%. Urine osmolality using the UF-5000 has a verified linear range (y = 1.097x + 16.91, R2  = .997). Within the comparison analysis, the mean difference was not large (-7.72%) but each differences were largely dispersed with 95% limits of agreement (LoA) from -70.5 to 55.06%, and the mean absolute difference -28.3 mOsm/kg with 95% LoA from -295.13 to 238.45 mOsm/kg. Cohen's kappa value was 0.54 (95% CI, 0.45-0.63). CONCLUSIONS: The UF-5000 measured conductivity and generated an acceptable quantitative analysis of urine osmolality. When compared with the results of the freezing point depression method used by the OsmoPro, a percentage of the measured urine osmolality by the UF-5000 was outside the allowable limit.

A new 7-point method facilitates accurate diagnosis of high-grade urothelial carcinoma in routine urinary cytology practice.

INTRODUCTION: This study was designed to identify the minimal and necessary cell morphologies to be considered for high-precision diagnosis of high-grade urothelial carcinoma (HGUC) in a routine urinary cytology practice. MATERIALS AND METHODS: We included 338 urine cytology specimens from 11 medical facilities in Japan. Six experts evaluated these Papanicolaou-stained specimens using their own diagnostic criteria to categorize them within an initial 4-tiered classification system. Of the 338 cases, 70 HGUC and 32 benign cases (with a complete consensus diagnosis of 6 experts) were included for the analysis. Two of the cytologists evaluated the specimens for 20 specific cellular features. The results were analyzed using a contingency table and by discriminant analysis. RESULTS: Of the original 338 cases, 165 were originally diagnosed as HGUC, but only 70 (42.4%) were scored as malignant by all participating cytologists; of the 101 benign cases, only 32 (31.7%) were classified as such in all examinations. These specimens were re-evaluated by 6 experts using a panel of 20 specific cellular features used to distinguish between HGUC and benign diseases; tests of significance and discriminant analyses identified 7 critical features that were most useful for cytological diagnosis. Statistical analysis revealed that a focus on these 7 features led to a diagnosis of HGUC with a probability of over 95%. CONCLUSIONS: The accuracy of our presently used method to evaluate urinary cytology is not consistently high. This novel classification system, which focuses on 7 critical features, facilitates the high accurate diagnosis of HGUC in routine cytology practice.

How Reliable Is Automated Urinalysis in Acute Kidney Injury?

OBJECTIVE: Examination of urine sediment is crucial in acute kidney injury (AKI). In such renal injury, tubular epithelial cells, epithelial cell casts, and dysmorphic red cells may provide clues to etiology. The aim of this study was to compare automated urinalysis findings with manual microscopic analysis in AKI. METHODS: Samples from patients diagnosed with AKI and control patients were included in the study. Red blood cells, white blood cells, renal tubular epithelial cells/small round cells, casts, and pathologic (path) cast counts obtained microscopically and by a UF1000i cytometer were compared by Spearman test. Logistic regression analysis was used to assess the ability to predict AKI from parameters obtained from the UF1000i. RESULTS: There was poor correlation between manual and automated analysis in AKI. None of the parameters could predict AKI using logistic regression analysis. However, the increment in the automated path cast count increased the odds of AKI 93 times. CONCLUSION: Automated urinalysis parameters are poor predictors of AKI, and there is no agreement with manual microscopy.

Are individual analyses of multiple short urine collections throughout the 24 hours superior to a standard 24-hour urine collection in precipitation risk assessment of healthy subjects?

PURPOSE: The commonly used 24-hour collection technique has been the mainstay of diagnosis for supersaturation but has some certain limitations. Hence, superiority of multiple short urine collections as a new alternative in precipitation risk assessment was assessed compared to the standard 24-hour urine collection among healthy subjects. MATERIALS AND METHODS: Individual urine samples of 26 healthy subjects were acquired every 2 to 3 hours throughout the 24 hours. Urine samples were obtained and the time and volume of each sample were recorded. Urinary constituents involved in precipitation including, sodium-potassium, chloride, calcium, phosphate, citrate, magnesium, urea, creatinine and pH were measured. A simulated 24-hour collection was recalculated by the totalling of all shorter urine collections volume and urinary constituents excretions throughout the day. RESULTS: Urine pH, urine creatinine and precipitation rate had a significantly lower values in 24-hours urine collection compared to one individual value of multiple urine collections by -0.769 (P < .0001), -7.305 (P < .0001), and - 12.838 (P < .0001), respectively. However, calcium (2.697, P < .0001), citrate (3.54, P < .0001), total phosphate (19.961, P < .0001) and total creatinine (9.579, P < .0001) had statistically significantly higher values in the 24-hours urine collection compared to individual value of multiple urine collections. CONCLUSION: Based on the results, individual analysis of multiple shorter urine collections throughout the day improves the ability of identifying supersaturation points, precipitation risk zones and may potentially improve risk assessment compared to the 24-hour urine collection method.