scholarly journals The Article COMPARISON OF LX-8000R AND URISED 2 FULL-AUTOMATED URINE ANALIZERS WITH MANUAL MICROSCOPIC EXAMINATION

2021 ◽  
Author(s):  
Revşa evin Canpolat erkan ◽  
Özgür Aslan
Keyword(s):  
Diagnostic Performance ◽  
Leukocyte Count ◽  
Urine Samples ◽  
Altman Analysis ◽  
Urine Sediment ◽  
Important Place ◽  
Bland Altman Analysis ◽  
Cell Counts ◽  
Sensitivity Assessment ◽  
Tract Infections

Background: Urinalysis has an important place in evaluating kidney and urinary tract infections. Automated urine analyzers enhance productivity and turnover in laboratories and economize time and labor required for analysis. In the present study, we evaluated and compared analytic and diagnostic performance of UriSed2 with LX-8000R, which is a novel image-based automated urine sediment analyzer. Methods: A total of 178 urine samples sent to our laboratory were evaluated by the two urine analyzers and standard manual microscopy. Precision and comparison studies were done in accordance with CLSI guidelines. Results: Sensitivity assessment revealed similar outcomes with both UriSed2 and LX-8000R devices for erythrocyte count (RBC), whereas UriSed2 device yielded higher outcomes for leukocyte count (WBC) and epithelial cells (EPI) than LX-8000R analyzer. Specificity of UriSed2 for WBC and RBC was higher than that of LX-8000R device. According to Gamma statistics, both urine analyzers showed perfect consistency for WBC, RBC and EPI cell counts. Manuel microscopy revealed statistically significant correlation between LX-8000R and UriSed2 in terms of WBC and RBC. Manual evaluation by Bland-Altman analysis demonstrated lower WBC and RBC values and higher EPI as compared to both UriSed2 and LX-8000R devices. As the result of Passing-Bablok regression analysis, both devices were found to be inconsistent with manual microscopy. Conclusion: We think that evaluation of automated urine analyzers will be more meaningful when they are evaluated together with urine samples and patient clinic in addition to comparing with manual microscopy.

A New Screening Approach For Fast And Accurate Prediction Of Positive And Negative Urine Cultures By SediMAX Compared With The Standard Urine Culture

2021 ◽  
Vol 1 (1) ◽  
pp. 46-55
Author(s):  
Massimo Pieri ◽  
Flaminia Tomassetti ◽  
Paola Cerini ◽  
Roberta Felicetti ◽  
Lucia Ceccaroni ◽  
...  
Keyword(s):  
Blood Cell ◽  
Bacterial Infections ◽  
Urine Culture ◽  
Screening Method ◽  
Significant Proportion ◽  
Bacterial Count ◽  
Urine Samples ◽  
Screening Methods ◽  
Urine Sediment ◽  
Tract Infections

Urinary tract infections (UTI) are the most frequent bacterial infections, and the detection of infection in urine samples is expensive and time-consuming. Also, in laboratories a significant proportion of samples processed yield negative results. For this, screening methods represent an important improvement towards the final UTI diagnosis. SediMAX is an automated microscopy, easier to use in laboratories due to its basic procedure and it is widely used for urine sediment analysis. In our study, we evaluated the performance of SediMAX, applying some screening parameters, compared with the gold standard methods, urine culture, to identify all the positive cases for UTI. We analysed 1185 urine samples from our daily laboratory routine. The basis of our screening model was to establish a cut-off for bacterial count (BACT), as 300 bacteria/µL in order to avoid missing positive cases. However, the sensitivity and the specificity achieved were not enough to identify all UTI infection in urine samples. So, in addition to BACT we have considered other parameters, such as White Blood Cell (WBC), Red Blood Cell (RBC), Yeasts (YEST), Age and Nitrates (NIT). The second screening method reached a sensitivity of 100%, that could be reliably employed in detect of UTIs.

scholarly journals Urinary tract infection and indwelling urinary catheters: prospective study in gynecological surgery with antibiotic prophylaxis

2015 ◽  
Vol 133 (6) ◽  
pp. 517-520 ◽  
Author(s):  
José Carlos Carraro-Eduardo ◽  
Daniela da Silva Alves ◽  
Ingrid Ellis Hinden ◽  
Ivan Penaloza Toledano ◽  
Sarah Gomes Freitas ◽  
...  
Keyword(s):  
Urinary Tract ◽  
Bacterial Growth ◽  
Antibiotic Sensitivity ◽  
Urine Samples ◽  
Gynecological Surgery ◽  
Urinary Catheters ◽  
Sensitivity Testing ◽  
Cell Counts ◽  
Catheter Design ◽  
Tract Infections

ABSTRACT CONTEXT AND OBJECTIVES: Urinary tract infections are the most common cause of hospital-acquired infections, and the use of indwelling urinary catheters is a predisposing factor for their development. The aims of this study were to estimate the frequency of pre and postoperative bacteriuria, identify the microorganisms involved, count the colony-forming units, determine the antibiotic sensitivity profile and compare the results from pre and postoperative urinalyses among women undergoing gynecological surgery with implantation of a urinary catheter. DESIGN AND SETTING: Non-controlled prospective observational single-cohort epidemiological study carried out at a university hospital. METHODS: Urine samples were collected before and 24 hours after catheterization for urinalysis, culturing and antibiotic sensitivity testing. Pre and postoperative urinalyses were compared using Wilcoxon and McNemar non-parametric tests. RESULTS: Fifty-one women participated in the study. Escherichia coligrew in six preoperative samples (11.8%) and Klebsiella pneumoniae in one (1.9%), but bacterial growth did not occur in any postoperative sample. Urinalysis showed lower number of pus cells in the postoperative urine samples (P < 0.05). There were no differences in red blood cell counts or in the nitrite and leukocyte esterase tests, between the samples. CONCLUSION: Bacteriuria was found in 13.7% of the preoperative samples. Gram-negative bacteria sensitive to most antibiotics were identified. In the postoperative samples, no bacterial growth was observed. Urinalysis only showed significant reduction of leukocyturia in the postoperative period.

Renal tubular epithelial cells add value in the diagnosis of upper urinary tract pathology

2020 ◽  
Vol 58 (4) ◽  
pp. 597-604 ◽  
Author(s):  
Matthijs Oyaert ◽  
Marijn Speeckaert ◽  
Jerina Boelens ◽  
Joris R. Delanghe
Keyword(s):  
Urinary Tract ◽  
Epithelial Cells ◽  
Diagnostic Performance ◽  
Storage Conditions ◽  
Upper Urinary Tract ◽  
Tubular Epithelial Cells ◽  
Renal Tubular Epithelial Cells ◽  
Urine Sediment ◽  
Renal Tubular ◽  
Tract Infections

AbstractBackgroundDiagnosis of upper urinary tract infections (UTI) is challenging. We evaluated the analytical and diagnostic performance characteristics of renal tubular epithelial cells (RTECs) and transitional epithelial cells (TECs) on the Sysmex UF-5000 urine sediment analyzer.MethodsUrinary samples from 506 patients presenting with symptoms of a UTI were collected. Only samples for which a urinary culture was available were included. Analytical (imprecision, accuracy, stability and correlation with manual microscopy) and diagnostic performance (sensitivity and specificity) were evaluated.ResultsThe Sysmex UF-5000 demonstrated a good analytical performance. Depending on the storage time, storage conditions (2–8 °C or 20–25 °C) and urinary pH, RTECs and TECs were stable in urine for at least 4 h. Using Passing-Bablok and Bland-Altman analysis, an acceptable agreement was observed between the manual and automated methods. Compared to TECs, RTECs demonstrated an acceptable diagnostic performance for the diagnosis of upper UTI.ConclusionsWhile TECs do not seem to serve as a helpful marker, increased urinary levels of RTECs add value in the diagnosis of upper UTI and may be helpful in the discrimination between upper and lower UTIs.

scholarly journals Urine Sediment Findings and the Immune Response to Pathologies in Fungal Urinary Tract Infections Caused by Candida spp.

2020 ◽  
Vol 6 (4) ◽  
pp. 245
Author(s):  
José Antonio Tesser Poloni ◽  
Liane Nanci Rotta
Keyword(s):  
Immune Response ◽  
Urinary Tract ◽  
Urine Samples ◽  
Candida Spp ◽  
Urine Sediment ◽  
Culture Techniques ◽  
Detection And Identification ◽  
Identification Of Fungi ◽  
Tract Infections ◽  
Urine Markers

Fungi are pathogenic agents that can also cause disseminated infections involving the kidneys. Besides Candida, other agents like Cryptococcus spp. can cause urinary tract infection (UTI), as well as other non-yeast fungi, especially among immunocompromised patients. The detection and identification of fungi in urine samples (by microscopy and culture) plays an essential role in the diagnosis of fungal UTI. However, variable cutoff definitions and unreliable culture techniques may skew analysis of the incidence and outcome of candiduria. The sediment analysis plays a key role in the identification of fungal UTI because both yeasts and pseudohyphae are easily identified and can be used as a clinical sign of fungal UTI but should not be overinterpreted. Indeed, urine markers of the immune response (leukocytes), urine barriers of tissue protection (epithelial cells), and urine markers of kidney disease (urinary casts) can be found in urine samples. This work explores the manifestations associated with the fungal UTI from the urinalysis perspective, namely the urinary findings and clinical picture of patients with fungal UTI caused by Candida spp., aspects associated with the immune response, and the future perspectives of urinalysis in the diagnosis of this clinical condition.

scholarly journals Using the Idexx SediVue Dx to predict the need for urine bacteriologic culture in cats

2021 ◽  
pp. 104063872110389
Author(s):  
Elisabeth Neubert ◽  
Karin Weber
Keyword(s):  
Bacterial Growth ◽  
High Power ◽  
Leukocyte Count ◽  
Urine Samples ◽  
High Power Field ◽  
Urine Sediment ◽  
Sediment Analysis ◽  
Accurate Detection ◽  
Culture Positive ◽  
Culture Negative

We analyzed urine samples from 191 cats for bacteriuria with an automated urine sediment analyzer (Idexx SediVue Dx), combined with image review by an observer, and compared to bacteriologic culture results. Sixty-nine samples were unambiguously assigned to be free of bacteria by the instrument and the observer, and no bacterial growth was detected. Twenty-seven samples were unambiguously assigned to have bacteriuria; 24 of these 27 samples were culture-positive. For these samples, bacteriuria was predicted with a sensitivity of 100% and a specificity of 96%. A clear assignment was not possible for 95 samples, 81 of which were culture-negative. Specificity dropped to 45% when all samples were considered. Using the automated leukocyte count to predict bacteriuria, sensitivity was 82% and specificity was 75%. Automated sediment analysis is faster and less observer-dependent than sediment analysis under a microscope, but accurate detection of bacteriuria remains difficult in a large proportion of samples. Bacteriuria was significantly associated with leukocyte count; the leukocyte count was >5/high power field in 82% of culture-positive samples.

scholarly journals Urinary tract infections in culled sows from Greek herds: prevalence and associations between findings of histopathology, bacteriology and urinalysis

2020 ◽  
Author(s):  
Mihaela Cernat ◽  
Vasilis Skampardonis ◽  
Georgios A. Papadopoulos ◽  
Fotios Kroustallas ◽  
Sofia Chalvatzi ◽  
...  
Keyword(s):  
Urinary Tract ◽  
Chronic Inflammation ◽  
Urinary Tract Infections ◽  
Urine Samples ◽  
Urine Ph ◽  
Urine Sediment ◽  
Positive Urine ◽  
Tract Infections ◽  
Staphylococcus Spp ◽  
On Farm

Abstract Background Urinary tract infections (UTI) of sows which include cystitis, which may progress to ureteritis and pyelonephritis affect their productivity, longevity and welfare. In this study we determined the prevalence of UTI by histopathology and bacteriology and investigated possible associations between histologically confirmed cystitis and the results of urinalysis and urine cultures in culled sows from three Greek farrow-to-finish herds. Materials and methods Routinely culled sows were included in the study. Their urinary bladders were collected from abattoirs and examined histopathologically. Furthermore, urinalysis and urine cultures were performed on urine samples aseptically collected from the bladders. Results Histologically confirmed cystitis was evident in 85/185 (45.94%) culled sows. Among those, 44 (51.76%) suffered from acute and 41 (48.24%) from chronic inflammation. The majority of the positive urine cultures were due to colonization of the urinary tract with E.coli, which was responsible for 55.81% of the total cases, followed by Staphylococcus spp. which caused 18.60% of the infections detected. Evidence of cystitis was associated with bacteriuria and sows with bacteriuria were 2.30 (p = 0.03, 95% CI: 1.10–4.83) times more likely to have histologically confirmed cystitis compared to sows with negative urine cultures. Bacteriuria was associated with proteinuria (p < 0.01), urine pH (p < 0.01) and presence of sediment (p < 0.01) in urine. Sows with proteinuria had 9.72 (2.63–35.88) times higher odds of bacteriuria than those without. Histologically defined cystitis was associated with proteinuria (p < 0.01) and increased urine pH (p < 0.01). Sows with proteinuria were 5.18 times (2.03–13.2) more likely to have histological lesions consistent with cystitis, than those without. Conclusions In the studied herds, UTI affected almost one out of two culled sows. Bacteriuria, which was more common among sows with UTI than those without, was mainly ascribed to members of the intestinal and environmental microbiota. Proteinuria and the existence of urine sediment which were associated with UTI may be proposed as likely on-farm predictors of UTI in live sows.

Screening of presumptive urinary tract infections by the automated urine sediment analyser sediMAX

2015 ◽  
Vol 53 (s2) ◽  
Author(s):  
Andrea Tessari ◽  
Nicoletta Osti ◽  
Marino Scarin
Keyword(s):  
Urinary Tract ◽  
Urinary Tract Infections ◽  
Predictive Value ◽  
Urine Culture ◽  
False Negative ◽  
False Negative Rate ◽  
Urine Samples ◽  
Indwelling Catheter ◽  
Urine Sediment ◽  
Tract Infections

AbstractUrinary tract infections (UTI) are among the most common bacterial infections and urine samples represent a large proportion of the specimens processed in clinical microbiology laboratories, up to 80% of which, however, yield negative results. Automated microscopy is widely used for urine sediment analysis and has recently been evaluated in a few studies for bacteriological screening of urine samples, achieving high levels of performance.We present a study in which urine samples from both inpatients and outpatients, with either clean-catch or indwelling catheter urine samples, were screened for UTI by urine culture, as the reference method, and the automated urine analyser sediMAX, for the detection of bacteria, leukocytes and yeasts.In total, 3443 urine samples were evaluated. When a single algorithm was adopted for sediMAX to screen the total patient population, 96.4% sensitivity, 75.4% specificity, 57.8% positive predictive value, and 98.4% negative predictive value were found. However, for male outpatients and all patients with indwelling catheter other algorithms were necessary to improve performances. Altogether, with sediMAX false negative rate was 2.4% and false positive rate was 27.6%. In addition, 54% of the investigated samples could have avoided urine culture.After the identification of specific algorithms for different patient subgroups, the automated urine analyser sediMAX can be reliably employed in the screening of UTI.

scholarly journals The ensemble learning model is not better than the Asian modified CKD-EPI equation for glomerular filtration rate estimation in Chinese CKD patients in the external validation study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Li Zhao ◽  
Jing-jing Zhang ◽  
Xin Tian ◽  
Jian-min Huang ◽  
Peng Xie ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Ensemble Learning ◽  
Validation Study ◽  
Diagnostic Performance ◽  
Filtration Rate ◽  
External Validation ◽  
Learning Model ◽  
Altman Analysis ◽  
Bland Altman Analysis ◽  
External Validation Study

Abstract Objective To assess the clinical practicability of the ensemble learning model established by Liu et al. in estimating glomerular filtration rate (GFR) and validate whether it is a better model than the Asian modified Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation in a cohort of Chinese chronic kidney disease (CKD) patients in an external validation study. Methods According to the ensemble learning model and the Asian modified CKD-EPI equation, we calculated estimated GFRensemble and GFRCKD-EPI, separately. Diagnostic performance of the two models was assessed and compared by correlation coefficient, regression equation, Bland–Altman analysis, bias, precision and P30 under the premise of 99mTc-diethylenetriaminepentaacetic acid (99mTc-DTPA) dual plasma sample clearance method as reference method for GFR measurement (mGFR). Results A total of 158 Chinese CKD patients were included in our external validation study. The GFRensemble was highly related with mGFR, with the correlation coefficient of 0.94. However, regression equation of GFRensemble = 0.66*mGFR + 23.05, the regression coefficient was far away from one, and the intercept was wide. Compared with the Asian modified CKD-EPI equation, the diagnostic performance of the ensemble learning model also demonstrated a wider 95% limit of agreement in Bland-Altman analysis (52.6 vs 42.4 ml/min/1.73 m2), a poorer bias (8.0 vs 1.0 ml/min/1.73 m2, P = 0.02), an inferior precision (18.4 vs 12.7 ml/min/1.73 m2, P < 0.001) and a lower P30 (58.9% vs 74.1%, P < 0.001). Conclusions Our study showed that the ensemble learning model cannot replace the Asian modified CKD-EPI equation for the first choice for GFR estimation in overall Chinese CKD patients.

Reply: Bland-Altman analysis for pupillometer comparison

2010 ◽  
Vol 36 (10) ◽  
pp. 1803-1804
Author(s):  
Magdalena Scheffel ◽  
Christoph Kuehne ◽  
Thomas Kohnen
Keyword(s):  
Altman Analysis ◽  
Bland Altman Analysis

scholarly journals Population-based input function for TSPO quantification and kinetic modeling with [11C]-DPA-713

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mercy I. Akerele ◽  
Sara A. Zein ◽  
Sneha Pandya ◽  
Anastasia Nikolopoulou ◽  
Susan A. Gauthier ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Kinetic Analysis ◽  
Kinetic Modeling ◽  
Input Function ◽  
Brain Regions ◽  
Population Based ◽  
Patient Specific ◽  
Systematic Bias ◽  
Altman Analysis ◽  
Bland Altman Analysis

Abstract Introduction Quantitative positron emission tomography (PET) studies of neurodegenerative diseases typically require the measurement of arterial input functions (AIF), an invasive and risky procedure. This study aims to assess the reproducibility of [11C]DPA-713 PET kinetic analysis using population-based input function (PBIF). The final goal is to possibly eliminate the need for AIF. Materials and methods Eighteen subjects including six healthy volunteers (HV) and twelve Parkinson disease (PD) subjects from two [11C]-DPA-713 PET studies were included. Each subject underwent 90 min of dynamic PET imaging. Five healthy volunteers underwent a test-retest scan within the same day to assess the repeatability of the kinetic parameters. Kinetic modeling was carried out using the Logan total volume of distribution (VT) model. For each data set, kinetic analysis was performed using a patient-specific AIF (PSAIF, ground-truth standard) and then repeated using the PBIF. PBIF was generated using the leave-one-out method for each subject from the remaining 17 subjects and after normalizing the PSAIFs by 3 techniques: (a) Weightsubject×DoseInjected, (b) area under AIF curve (AUC), and (c) Weightsubject×AUC. The variability in the VT measured with PSAIF, in the test-retest study, was determined for selected brain regions (white matter, cerebellum, thalamus, caudate, putamen, pallidum, brainstem, hippocampus, and amygdala) using the Bland-Altman analysis and for each of the 3 normalization techniques. Similarly, for all subjects, the variabilities due to the use of PBIF were assessed. Results Bland-Altman analysis showed systematic bias between test and retest studies. The corresponding mean bias and 95% limits of agreement (LOA) for the studied brain regions were 30% and ± 70%. Comparing PBIF- and PSAIF-based VT estimate for all subjects and all brain regions, a significant difference between the results generated by the three normalization techniques existed for all brain structures except for the brainstem (P-value = 0.095). The mean % difference and 95% LOA is −10% and ±45% for Weightsubject×DoseInjected; +8% and ±50% for AUC; and +2% and ± 38% for Weightsubject×AUC. In all cases, normalizing by Weightsubject×AUC yielded the smallest % bias and variability (% bias = ±2%; LOA = ±38% for all brain regions). Estimating the reproducibility of PBIF-kinetics to PSAIF based on disease groups (HV/PD) and genotype (MAB/HAB), the average VT values for all regions obtained from PBIF is insignificantly higher than PSAIF (%difference = 4.53%, P-value = 0.73 for HAB; and %difference = 0.73%, P-value = 0.96 for MAB). PBIF also tends to overestimate the difference between PD and HV for HAB (% difference = 32.33% versus 13.28%) and underestimate it in MAB (%difference = 6.84% versus 20.92%). Conclusions PSAIF kinetic results are reproducible with PBIF, with variability in VT within that obtained for the test-retest studies. Therefore, VT assessed using PBIF-based kinetic modeling is clinically feasible and can be an alternative to PSAIF.

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