ABSTRACT
BACKGROUND: Automated cellular analyzers are expected to improve the analytical performance in body fluid (BF) analysis. We evaluated the analytical performance of three automated cellular analyzers and established optimum reflex analysis guidelines. METHODS: A total of 542 BF samples (88 cerebrospinal fluid [CSF] samples and 454 non-CSF samples) were examined using manual counting and three automated cellular analyzers: UniCel DxH 800 (Beckman Coulter), XN-350 (Sysmex), and UF-5000 (Sysmex). Additionally, 2,779 BF analysis results were retrospectively reviewed. For malignant cell analysis, the receiver operating characteristic (ROC) curve was used, and the detection of high fluorescence-BF cells (HF-BFs) using the XN-350 analyzer was compared with cytology results. RESULTS: All three analyzers showed good agreement for total nucleated cell (TNC) and red blood cell (RBC) counts, except for the RBC count in CSF samples using the UniCel DxH 800. However, variable degrees of differences were observed during differential cell counting. For malignant cell analysis, the area under the curve was 0.63 for the XN-350 analyzer and 0.76 for manual counting. We established our own reflex analysis guidelines as follows: HF-BFs 83.4/100 WBCs or eosinophils >3.8% are the criteria for mandatory double check confirmation with 1,000× magnification examination. CONCLUSIONS: The three automated analyzers showed good analytical performances. Application of reflex analysis guidelines is recommended for eosinophils and HF-BFs, and manual confirmation is warranted.
Subject(s)
Body Fluids , Cell Count , Cerebrospinal Fluid , Eosinophils , Erythrocytes , Leukocytes , Reflex , Retrospective Studies , ROC CurveABSTRACT
Introduction@#The traditional microscopic method is to visually count the elements in the urine, but it is difficult to distinguish between the cells because they are not stained. Sternheimer Malbin staining, on the other hand, contains a variety of dyes that help to distinguish elements in urine sediment, improve the differentiation between cell nuclei and cytoplasm, provide more information about cell shape and image, and make it easier to differentiate kidney disease. @*Objective@#To study the results of the reading of a fully automatic urine sediment analyzer of compared with the Sternheimer Malbin stained bright field microscope method.@*Research materials and methods@#In this study included 150 people who served the MJTH of the MNUMS received permission to participate in the research. The urine sample collected in accordance with the standard operating instructions was counted by a fully automated analyzer and stained with Sternheimer Malbin dye and counted red cells (RBC), white blood cells (WBC), epithelial cells (EC), and renal epithelium (RTEC) under a microscope using a Fuchs-Rosenthal chamber.@*Results@#23.3% (n=35) of the respondents were male, 76.6% (n=115) were female, and the average age was 44.3±11.6. There 16.6% (25)/9.3% (14) of the RBCs were counted in excess of the reference volume when analyzed under an microscope stained with an automated urine sediment analyzer and Sternheimer-Malbin dye. For each WBC method, 45.4% (68)/41 (61)% and EC 24.7% (37)/23.3% (35) were counted above the reference volume. 90% (135)/32% (48) of the total samples were counted in excess of the RTEC reference volume. Comparing the performance of the automatic urine sediment analyzer with the light microscope method, the sensitivity and specificity were RBC-99.8%/99.1%, WBC-99.3%/99.6%, EC-99.7%/99.2, and RTEC-99.1%/99.2%. False-positive and false-negative results were rated for each RBC-99.9%/99.1%, WBC-99.3%/99.6%, EC 99.8%/99.2%, and RTEC-99.7%/99.9%, respectively. The positive likelihood ratio was RBC, WBC, RTEC 1.0, or the test was useless, while the negative likelihood ratio was RBC was very different, WBC was slightly different, EC was very different, and RTEC was very different. Positive and negative predictive value indicators RBC-99.3%/99.4%, WBC-99.4%/99.4%, EC-99.4%/99.5, RTEC-99.2%/99.1%, optimality for RBC, WBC, EC 99.4%, RTEC -99.1%.@*Conclusion@#</br> 1. The results of an automated urine sediment analyzer and a bright field microscope stained by Sternheimer Malbin were similar for red blood cells, white blood cells, and epithelial cells, but different for renal tubular epithelial cells. </br> 2. The resuls UF-5000 analyzer and bright field microscope analysis using Sternheimer Malbin dye were comparable.
ABSTRACT
BACKGROUND: Analysis of body fluids provides important information for assessing various medical conditions. We aimed to validate the analytical and diagnostic performance of the Sysmex UF-5000 (Sysmex, Japan) system for the analysis of different body fluids. METHODS: Eighty body fluid samples were analyzed using the UF-5000 system in the body fluid mode and light microscopy. Body fluids included ascitic, pleural, and cerebrospinal fluid (CSF), as well as other fluid samples. RESULTS: A comparison between the UF-5000 system and manual counting demonstrated good correlations with regard to red (r=0.6555) and white blood cell (r=0.9666) counts. The UF-5000 system also demonstrated good performance for differential cell counting (r=0.9028). CSF particularly showed a good correlation. CONCLUSIONS: The use of the UF-5000 system for cell counting and differential analysis of body fluid samples might be an effective and automated alternative to chamber counting in laboratory routine analysis, thereby enhancing laboratory workflow and clinical effectiveness.
Subject(s)
Automation , Body Fluids , Cell Count , Cerebrospinal Fluid , Erythrocytes , Leukocytes , Methods , Microscopy , Treatment OutcomeABSTRACT
BACKGROUND: Urine culture is one of the most frequently requested tests in microbiology. Automated urine analyzers yield much infection-related information. The Sysmex UF-5000 analyzer (Sysmex, Japan) is a new flow cytometry urine analyzer capable of quantifying urinary particles, including bacteria, WBCs, and yeast-like cells (YLCs) and can provide a Gram stainability flag. In this work, we evaluated how many unnecessary urine cultures could be screened out using the UF-5000. METHODS: We compared the culture results of 126 urine samples among 453 requested urine cultures (from sources other than the Urology and Nephrology departments) with urinalysis results. Urine cultures were considered positive if bacterial or YLC growth was ≥104 CFUs/mL. RESULTS: We used urinalysis cut-off values of 50/µL and 100/µL for bacteria and YLC, respectively. Forty eight of the 126 (38.1%, or 10.6% of 453 requested) cultures were below these cut-off values and did not contain any culture-positive samples. CONCLUSION: Bacteria and YLC counts generated using the UF-5000 analyzer could be used to screen out negative cultures and reduce urine culture volume by ~10% without sacrificing detection of positive cultures.