An updated approach to the evaluation of the urinary sediment.

Examination of the urinary sediment (U-sed) is an important non-invasive, rapid, and inexpensive tool for the diagnosis and surveillance over time of renal diseases. In this Educational Review, we describe first how to collect, prepare, and examine urine samples in order to obtain reliable results. Then, we describe the U-sed findings in isolated microscopic hematuria, glomerular diseases, acute interstitial nephritis, acute kidney injury, reactivation of the BK virus in kidney transplant recipients, and crystalluric genetic diseases.

Current urinalysis practices in Belgian laboratories towards the 2023 EFLM European urinalysis guideline.

OBJECTIVES/BACKGROUND: We aimed to investigate routine urinalysis practices in Belgian laboratories and verify these findings against the 2023 European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) European Urinalysis Guideline. METHODS: A questionnaire was developed to collect information on pre- to postanalytical aspects of urine test strip and particle analysis. The questionnaire was distributed by Sciensano to all Belgian laboratories, licensed to perform urine particle analysis. RESULTS: Sixty-six percent of the Belgian laboratories (75/113) participated. The responding laboratories served physicians in private (25%), hospital (60%) and university hospital (15%) setting. All laboratories performed test strip and particle analysis, predominantly automatically (97% and 96%, respectively). In addition, most laboratories (87%) used intelligent verification criteria to optimize diagnostic accuracy. Almost all laboratories (≥90%) screened and reported a minimal biochemistry panel (glucose, protein, pH, ketones) and particle count (red and white blood cells). Independent of the technology, a notable variability was observed regarding medical cut-off values and advanced particle differentiation and reporting. Internal quality control was extensively performed for urine test strip (91%) and particle analysis (96%), while external QC was less common (32% and 36%, respectively). Consequently, only few laboratories were ISO15189 accredited for urine test strip (15%) and particle analysis (17%). CONCLUSION: There is considerable variability in current urinalysis performed in Belgian laboratories. The 2023 EFLM urinalysis guideline has the potential to guide clinical laboratories towards improving their urinalysis practices. Additional efforts are required to implement these recommendations into clinical practice in Belgium.

Implementation of an Algorithm for Testing, Diagnosis, and Antibiotic Stewardship of Asymptomatic Bacteriuria in Pregnancy.

BACKGROUND: Asymptomatic bacteriuria (ASB) affects 2-15% of pregnant women, with 20-40% developing symptoms later. Symptomatic urinary tract infections (UTIs) are more common in pregnancy, with a prevalence of 33%, posing risks like preterm delivery, low birth weight, and maternal pyelonephritis. The gold standard for UTI detection is a urine culture, but point-of-care urinalysis dipsticks are frequently performed as screens during regular obstetric visits. Leukocyte esterase has been used to justify treatment in the asymptomatic, even with low sensitivity and specificity. Confirmatory tests are crucial to avoid false positives and ensure optimal outcomes. Current guidelines for urinalysis dipstick interpretation and the decision to treat ASB in pregnancy are limited. It remains unclear if an evidence-based algorithm can improve test utilization, diagnosis, and treatment decisions for ASB in pregnancy. OBJECTIVES: The primary objective of our study is to develop, implement, and evaluate an evidence-based algorithm to guide urinalysis interpretation, culturing, diagnosis, and antibiotic stewardship of asymptomatic bacteriuria in pregnant patients during routine obstetric visits. STUDY DESIGN: The project involves both retrospective and quasi-experimental prospective chart reviews of pregnant patients aged 18 and older, beyond 20 weeks gestation, from routine obstetric visits with urinalysis dipstick tests. A doctorate in clinical laboratory sciences student developed an educational algorithm to guide urinalysis dipstick interpretation, culturing necessity, and treatment decisions based on evidence-based practice. Our study considered patient records from February 1 - 28, 2022 as retrospective (pre-algorithm implementation) data and January 24 - February 22, 2023, as prospective (post-algorithm implementation) data. Data collected from the electronic medical record included de-identified patient information, urinalysis results, culture dates and outcomes, antibiotic prescriptions, UTI or ASB diagnoses, provider details, adverse pregnancy outcomes, and demographics. Data analysis using SPSS version 29 involved chi-square tests, likelihood ratios, and effect size calculations, with P-values <0.05 considered statistically significant. RESULTS: In our study, we examined a total of 1,176 patient records. Pre-implementation data included 440 records, with 224 abnormal and 216 normal urinalyses. Post-implementation data encompassed 736 records, of which 255 were abnormal and 481 were normal. The patient demographics predominantly featured White (87%), with a median maternal age of 27 years and a gestational age of 32 weeks. Our pre-implementation analyses revealed significant associations between algorithm deviations with both culture utilization (P <.001) and antibiotic stewardship (P <.001). However, no significant associations were observed between algorithm deviations and adverse patient outcomes. Culture underutilization decreased significantly from 43.0% (189/440) pre-implementation to 29.5% (217/736) post-implementation (P < .001). The overall reduction in ASB prevalence from 16.3% (8/49) to 6.7% (10/67) suggests a decrease of nearly 60%. Additionally, antibiotic overprescription decreased significantly from 1.6% (4/258) pre-implementation to 0.8% (4/522) post-implementation (P = .003), with a reduction from 7.1% (3/42) to 2.4% (1/41) among abnormal urinalyses. CONCLUSION: Our findings show a strong alignment between the use of the algorithm and subsequent clinical decisions, underscoring its potential to enhance patient care and management in obstetric settings. Notably, adherence to the algorithm was higher among providers displaying prudent antibiotic use.

Plasma procalcitonin and urine interleukin-8, Neutrophil Gelatinase-Associated Lipocalin, and calprotectin in the diagnostic process of a urinary tract infection at the emergency department.

OBJECTIVES: To assess the usefulness of plasma procalcitonin and urine interleukin-8 (IL-8), Neutrophil Gelatinase-Associated Lipocalin (NGAL), and calprotectin for diagnosis of urinary tract infections (UTIs) at the emergency department (ED). METHODS: In adults presenting at the ED with UTI suspicion, biomarker performance was compared to routine diagnostics (urine dipstick, automated urinalysis). Patients with a urine catheter, leukopenia or neither (standard) were analysed separately. RESULTS: A UTI was clinically diagnosed in 91/196 episodes (46.4%); standard: 29/67 (43.2%), catheter 46/73 (63.0%), leukopenia 17/60 (28.3%) (4 had both). Procalcitonin did not discriminate between UTI and no UTI. Urinary biomarker levels were elevated in UTI episodes (median, µg/mmol creatinine): NGAL 7.8 vs. 46.3, IL-8 6.1 vs. 76.6, calprotectin 23.9 vs. 265.4; the three subgroups also had higher levels. Biomarker cut-off values (90% sensitivity) showed a low specificity (range 20.8%-64.9%) and moderate accuracy (58.6%-75.4%). The biomarkers performed comparable with routine diagnostics, except for leukopenic patients with not-significantly higher AUC values. All urinary biomarkers correlated positively with urine leucocyte count. CONCLUSIONS: Plasma procalcitonin could not accurately diagnose a UTI. Urine IL-8, NGAL, and calprotectin showed no additional value to routine diagnostics, except a minor improvement in leukopenic patients. These urine biomarkers seem to predominantly reflect leukocyturia.

Lean methodology in health care practice: an example of application for clinical laboratory urinalysis processing.

BACKGROUND: Improving quality and laboratory testing turnaround time (TAT) is a constant challenge for a clinical laboratory. The formulas that describe the best way to manage these goals are outlined in International Organization for Standardization standards. According to standards, improvement must be timely and continuous. Lean methodology is a tool to meet this requirement. One of the fundamental elements of Lean is a systematic approach to process improvement by removing waste to create value for the end-user (eg, patient) of the service. This methodology can be adapted in resource-limited settings. OBJECTIVE: The aim of this study was to test the application of Lean methodology in urinalysis. METHODS: Lean has various collections of tools and concepts. We applied the most useful for the clinical laboratory: Gemba walk, Takt time, cycle time, and value-stream mapping. Finally, we created and approved workplace standards to improve the performance of urinalysis. RESULTS: We compared the TATs of urinalysis tests before optimization, immediately after, and long after (~5 months). We found that TATs had significantly shortened. The TATs of emergency (STAT) urine tests immediately after optimization improved: automated microscopy to 16% (P =.194), fully automated test-strip to 23% (P = .0172), and standardized urine sediment examination to 20% (P =.0048). The TATs of routine urine tests also improved immediately after optimization: automated microscopy to 18% (P <.0001), fully automated test-strip to 11% (P =.0025), and standardized urine sediment examination to 18% (P =.0011). After 5 months of Lean application within the urinalysis laboratory, TATs of routine urine tests remained improved; however, the improvement of STAT urine test TATs dropped to approximately 4%. CONCLUSION: The application of the Lean methodology shows significant improvement in TATs of processes in our laboratory.

Spectrochip-based Calibration Curve Modeling (CCM) for Rapid and Accurate Multiple Analytes Quantification in Urinalysis.

Most urine test strips are intended to enable the general population to rapidly and easily diagnose potential renal disorders. It is semi-quantitative in nature, and although the procedure is straightforward, certain factors will affect the judgmental outcomes. This study describes rapid and accurate quantification of twelve urine test strip parameters: leukocytes, nitrite, urobilinogen, protein, pH, occult blood, specific gravity, ketone, bilirubin, glucose, microalbumin, and creatinine using a micro-electromechanical system (MEMS)-based spectrophotometer, known as a spectrochip. For each parameter, absorption spectra were measured three times independently at eight different concentration levels of diluted standard solutions, and the average spectral intensities were calculated to establish the calibration curve under the characteristic wavelength ( λ c ). Then, regression analysis on the calibration curve was performed with GraphPad Prism software, which revealed that the coefficient of determination ( R 2 ) of the modeled calibration curves was greater than 0.95. This result illustrates that the measurements exceed standard levels, confirming the importance of a spectrochip for routine multi-parameter urine analysis. Thus, it is possible to obtain the spectral signal strength for each parameter at its characteristic wavelength in order to compare directly with the calibration curves in the future, even in situations when sample concentration is unknown. Additionally, the use of large testing machines can be reduced in terms of cost, time, and space by adopting a micro urine testing platform based on spectrochip, which also improves operational convenience and effectively enables point-of-care (POC) testing in urinalysis.

The Effects of Diet, Dietary Supplements, Drugs and Exercise on Physical, Diagnostic Values of Urine Characteristics.

Background/Objectives: This review summarizes the current knowledge about factors that affect the physical characteristics of urine. It highlights proper urine sample collection and displays factors like diet, hydration status, and medications that can alter urine color, odor, clarity, specific gravity and pH. Results: Urinalysis is a minimally invasive examination of a patient's health, especially concerning nephrological and endocrinological abnormalities, as well as dietary habits and stimulants used. Certain deviations in appearance, composition or frequency/pain during urination may indicate an ongoing disease process in the body. Based on laboratory results, further medical treatment is determined. The reason for a change in the color of the urine, for its clouding or intense odor may be a disease, as well as the consumption of food, medication, intensive physical exercise or inadequate hydration of the body. Well-standardized procedures for collecting, transporting, preparing and analyzing samples should become the basis for an effective diagnostic strategy in urinalysis. It is worth noting that pharmacists in pharmaceutical care are often the first people to whom a patient turns for health advice and for the interpretation of simple laboratory tests. Acquiring the ability to interpret the results of laboratory tests and the principles of proper sampling for laboratory tests is indispensable in the process of possible counseling and providing reliable answers to patients' questions. Conclusions: Although urinalysis is not recommended as a routine screening tool for the general population, it can prove to be a valuable source of patient health data in some cases as the data will be useful to physicians and pharmacists to more effectively diagnose and better care for patients.

Chronic Kidney Disease and Kidney Stone in a Wild Chimpanzee (Pan troglodytes verus) in Côte d'Ivoire.

An older wild female chimpanzee (Pan troglodytes) was found dead with a large calcium oxalate stone in the renal pelvis. Histopathological changes included glomerulosclerosis, interstitial nephritis and fibrosis, focal mineralization, and medial hypertrophy. Urinary albumin-creatinine-ratio showed increased values from 15 months before death. Causes of the kidney disease remain unconfirmed.

Pyuria in hospitalized general medical patients without urinary tract infection.

Urine microscopy for detecting pus cells is a common investigation ordered in hospitalized general medical patients as part of routine care. A few previous studies have shown that sterile pyuria is not uncommon in this population. We studied the prevalence of pyuria among patients hospitalized with non-urinary tract infection (UTI) diagnosis in the medical wards. We excluded patients with asymptomatic bacteriuria (ASB). Pyuria was quantified in uncentrifuged urine using the chamber counting method, and ≥ 10 pus cells per mm 3 was considered significant. We also compared this method with the commonly used but less accurate method of counting pus cells/high power field using centrifuged urine (routine method). We studied 196 patients; 113 (57.7%) were males. Most (175[89.3%]) patients were hospitalized for an infection. We found that 18.4% of the study group had sterile pyuria, and it was strongly associated with the presence of concomitant microscopic hematuria (unadjusted odds ratio, 3.74 [1.65 to 8.50]; P=0.002). We found no association of pyuria with female gender, diabetes, acute kidney injury, or current antibiotic use. By routine method, 56 (28.6 %) patients had significant pyuria. In comparison to the chamber counting method, the routine method was 69.4(63-75.8) % sensitive and 80.6(75.1-86.2) % specific. The positive and negative predictive values were 44.6 (37.7- 51.6) % and 92.1 (88.4 - 95.9) %. We concluded that sterile pyuria and microscopic hematuria could be present in a proportion of hospitalized general medical patients without UTI or ASB. Clinical judgment is essential in interpreting the significance of abnormal urinalysis reports.

Atypical cells in urine sediment: a novel biomarker for early detection of bladder cancer.

OBJECTIVES: Atypical cells (Atyp.C), as a new parameter determined by an automated urine analyzer, can be suspected of being malignant tumor cells. We evaluated the extent to which the Atyp.C can predict the existence of malignant tumor cells. METHODS: A total of 3,315 patients (1,751 in the training cohort and 1,564 in the testing cohort) were recruited and divided into five groups, namely, primary bladder cancer (BCa), recurrent BCa, post-treatment monitoring of BCa, other urological tumors, and controls. Urine Atyp. C, bacteria, white blood cell, and red blood cell were measured by a Sysmex UF-5000 analyzer. We compared the Atyp.C values across the different groups, sexes, and tumor stages. The diagnostic performance of Atyp.C alone and in combination with other parameters for detecting BCa was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: The Atyp.C value of the primary BCa group was significantly higher than that in the other groups, except recurrent BCa group. The Atyp.C value was closely related to tumor staging. Atyp.C combined with bacteria had the highest diagnostic performance for primary BCa [training cohort AUC: 0.781 (95 % CI: 0.761-0.801); testing cohort AUC: 0.826 (95 % CI: 0.806-0.845)]. The AUC value of diagnosed recurrent BCa by Atyp.C plus bacteria for the training cohort was 0.784 (95 % CI: 0.762-0.804). CONCLUSIONS: Atyp.C was high in primary BCa patients and the combination of bacteria and Atyp.C showed high predictive value for primary BCa, suggesting that Atyp.C may be a useful objective indicator for the early detection of BCa.

On the Value of Measuring Recent Drug Use by Self-Reports and Urinalysis in Clinical Trials.

Background: Valid measurement of drug use in patients enrolled in clinical trials that treat substance use disorder is vital to determine the trial's outcome. Self-reports are often used but their validity has been studied with mixed results. Urinalysis may sometimes be employed as an alternative or supplement to self-reports. Objectives: This study examined how estimating drug use by either method would affect the results from a randomized clinical trial conducted in a methadone treatment program. At the initial Baseline interview and four follow-up interviews, participants were asked about their drug use history and provided a urine specimen for drug testing. Results: In most cases, the urinalyses detected more drugs than the patients had reported using. A major exception was heroin, whose use was an eligibility criterion for enrollment in the study and methadone treatment. Conclusions: The patients' self-reports would have led us to conclude that the use of heroin and fentanyl had declined from the initial Baseline interview to the final follow-up interview, while the urinalysis results indicated no change in exposure to heroin and an increase in exposure to fentanyl. Clinical trials would be well served to employ the use of biological tests in addition to self-reports to measure recent drug use and to accurately estimate the efficacy of the experimental protocols and patients' exposure to drugs.

A step forward in the diagnosis of urinary tract infections: from machine learning to clinical practice.

Objectives: Urinary tract infections (UTIs) are common infections within the Emergency Department (ED), causing increased laboratory workloads and unnecessary antibiotics prescriptions. The aim of this study was to improve UTI diagnostics in clinical practice by application of machine learning (ML) models for real-time UTI prediction. Methods: In a retrospective study, patient information and outcomes from Emergency Department patients, with positive and negative culture results, were used to design models - 'Random Forest' and 'Neural Network' - for the prediction of UTIs. The performance of these predictive models was validated in a cross-sectional study. In a quasi-experimental study, the impact of UTI risk assessment was investigated by evaluating changes in the behaviour of clinicians, measuring changes in antibiotic prescriptions and urine culture requests. Results: First, we trained and tested two different predictive models with 8692 cases. Second, we investigated the performance of the predictive models in clinical practice with 962 cases (Area under the curve was between 0.81 to 0.88). The best performance was the combination of both models. Finally, the assessment of the risk for UTIs was implemented into clinical practice and allowed for the reduction of unnecessary urine cultures and antibiotic prescriptions for patients with a low risk of UTI, as well as targeted diagnostics and treatment for patients with a high risk of UTI. Conclusion: The combination of modern urinalysis diagnostic technologies with digital health solutions can help to further improve UTI diagnostics with positive impact on laboratory workloads and antimicrobial stewardship.

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.

Diagnosing probable urinary tract infections in nursing home residents without indwelling catheters: a narrative review.

BACKGROUND: Overdiagnosis of urinary tract infections (UTIs) is one of the most common reasons for the unnecessary use of antibiotics in nursing homes, increasing the risk of missing serious conditions. Various decision tools and algorithms aim to aid in UTI diagnosis and the initiation of antibiotic therapy for residents. However, due to the lack of a clear reference standard, these tools vary widely and can be complex, with some requiring urine testing. As part of the European-funded IMAGINE project, aimed at improving antibiotic use for UTIs in nursing home residents, we have reviewed the recommendations. OBJECTIVES: This review provides a comprehensive summary of the more relevant tools and algorithms aimed at identifying true UTIs among residents living in nursing homes and discusses the challenges in using these algorithms based on updated research. SOURCES: The discussion is based on a relevant medical literature search and synthesis of the findings and published tools to provide an overview of the current state of improving the diagnosis of UTIs in nursing homes. CONTENT: The following topics are covered: prevalence of asymptomatic bacteriuria, diagnostic challenges, clinical criteria, urinary testing, and algorithms to be implemented in nursing home facilities. IMPLICATIONS: Diagnosing UTIs in residents is challenging due to the high prevalence of asymptomatic bacteriuria and nonspecific urinary tract signs and symptoms among those with suspected UTIs. The fear of missing a UTI and the perceived antibiotic demands from residents and relatives might lead to overdiagnosis of this common condition. Despite their widespread use, urine dipsticks should not be recommended for geriatric patients. Patients who do not meet the minimum diagnostic criteria for UTIs should be evaluated for alternative conditions. Adherence to a simple algorithm can prevent unnecessary antibiotic courses without compromising resident safety.

Age-related trends in urine specific gravity in apparently healthy cats.

OBJECTIVES: Urine specific gravity (USG) is the most common method for the estimation of urine concentration in cats. Utilization of USG as a screening tool is easily accessible and is of low cost to the client if strategically utilized in settings of higher diagnostic value. There is currently minimal population information regarding how USG changes across ages in cats. METHODS: Data were collected from electronic pet medical records from more than 1000 hospitals and screened for cats with an apparently healthy clinical status and complete diagnostic information. USG was compared with age in multiple analyses to examine the relationship between the variables. RESULTS: In the absence of other indicators of disease, renal concentrating ability begins to diminish, on average, starting at approximately 9 years of age. By age group, cats aged 11-15 years (1.044, 95% confidence interval [CI] 1.043-1.044) had statistically significantly lower mean USGs compared with cats aged less than 1 year (1.049, 95% CI 1.048-1.051; P <0.001), 1-6 years (1.049, 95% CI 1.049-1.050; P <0.001) or 7-10 years (1.049, 95% CI 1.048-1.049; P <0.001). Cats aged ⩾15 years (1.038, 95% CI 1.036-1.040) had statistically significantly lower mean USGs compared with cats aged less than 1 year (P <0.001), 1-6 years (P <0.001), 7-10 years (P <0.001) or 11-15 years (P <0.001). CONCLUSIONS AND RELEVANCE: Renal concentrating ability begins to diminish, on average, starting at approximately 9 years of age and is progressive as cat age increases. This study provides important and new information to help improve screening practices for disorders of concentrating ability in cats.

Silver-based bimetallic nanozyme fabrics with peroxidase-mimic activity for urinary glucose detection.

The enhanced catalytic properties of bimetallic nanoparticles have been extensively investigated. In this study, bimetallic Ag-M (M = Au, Pt, or Pd) cotton fabrics were fabricated using a combination of electroless deposition and galvanic replacement reactions, and improvement in their peroxidase-mimicking catalytic activity compared to that of the parent Ag fabric was studied. The Ag-Pt bimetallic nanozyme fabric, which showed the highest catalytic activity and ability to simultaneously generate hydroxyl (•OH) and superoxide (O2•-) radicals, was assessed as a urine glucose sensor. This nanozyme fabric sensor could directly detect urinary glucose in the pathophysiologically relevant high millimolar range without requiring sample predilution. The sensor could achieve performance on par with that of the current clinical gold standard assay. These features of the Ag-Pt nanozyme sensor, particularly its ability to avoid interference effects from complex urinary matrices, position it as a viable candidate for point-of-care urinary glucose monitoring.

Methylated DNA Markers in Voided Urine for the Identification of Clinically Significant Prostate Cancer.

INTRODUCTION: Non-invasive assays are needed to better discriminate patients with prostate cancer (PCa) to avoid over-treatment of indolent disease. We analyzed 14 methylated DNA markers (MDMs) from urine samples of patients with biopsy-proven PCa relative to healthy controls and further studied discrimination of clinically significant PCa (csPCa) from healthy controls and Gleason 6 cancers. METHODS: To evaluate the panel, urine from 24 healthy male volunteers with no clinical suspicion for PCa and 24 men with biopsy-confirmed disease across all Gleason scores was collected. Blinded to clinical status, DNA from the supernatant was analyzed for methylation signal within specific DNA sequences across 14 genes (HES5, ZNF655, ITPRIPL1, MAX.chr3.6187, SLCO3A1, CHST11, SERPINB9, WNT3A, KCNB2, GAS6, AKR1B1, MAX.chr3.8028, GRASP, ST6GALNAC2) by target enrichment long-probe quantitative-amplified signal assays. RESULTS: Utilizing an overall specificity cut-off of 100% for discriminating normal controls from PCa cases across the MDM panel resulted in 71% sensitivity (95% CI: 49-87%) for PCa detection (4/7 Gleason 6, 8/12 Gleason 7, 5/5 Gleason 8+) and 76% (50-92%) for csPCa (Gleason ≥ 7). At 100% specificity for controls and Gleason 6 patients combined, MDM panel sensitivity was 59% (33-81%) for csPCa (5/12 Gleason 7, 5/5 Gleason 8+). CONCLUSIONS: MDMs assayed in urine offer high sensitivity and specificity for detection of clinically significant prostate cancer. Prospective evaluation is necessary to estimate discrimination of patients as first-line screening and as an adjunct to prostate-specific antigen (PSA) testing.

Clinical usefulness of urine Gram stain for diagnosing urinary tract infections at the emergency department.

BACKGROUND: Diagnosis of urinary tract infections (UTIs) is a frequent challenge at the emergency department (ED). The clinical usefulness of the urine Gram stain (GS) is uncertain. OBJECTIVE: We studied the GS performance to clarify its clinical utility at the ED. METHODS: Urine dipstick (UD), automated urinalysis (UF-1000i), GS and urine culture (UC) were performed in a cohort of consecutive adults presenting at the ED suspected of a UTI. GS performance was assessed and compared to UD and UF-1000i. RESULTS: A UTI diagnosis was established in 487/1358 (35.9%) episodes. Sensitivity and specificity for 'many' GS leucocytes was 33.7% and 95.4%; for 'many' GS bacteria 51.3% and 91.0%. GS diagnostic performance by ROC analysis was 0.796 for leucocytes and 0.823 for bacteria. GS bacteria performed better than UD nitrite comparable to UF-1000i bacteria. GS leucocytes underperformed compared to UD leucocyte esterase and UF-1000i leucocytes. UC was positive in 455 episodes. GS correctly predicted urine culture of gram-negative rods (PPV 84.6%). Prediction was poor for gram-positive bacteria (PPV 38.4% (cocci), 1.0% (rods)). CONCLUSION: With the exception of a moderate prediction of gram-negative bacteria in the UC, urine GS does not improve UTI diagnosis at the ED compared to other urine parameters.

Advances and Progress in Automated Urine Analyzers.

The clinical analysis of urine has classically focused on conventional chemical-based urinalysis and urine microscopy. Contemporary advances in both analysis subsets have started to employ new technologies such as automated image analysis, flow cytometry, and mass spectrometry. In addition to new detection technologies, current analyzers have incorporated more advanced imaging, automated sample handing, and machine learning analyses into their workflow. The most advanced semiautomated analyzers can be interfaced with hospital medical record systems, and in the point-of-care setting, smartphones can be used for image analysis. This review will discuss current technological advancements in the field of urinalysis and urine microscopy.