Urine Analyzer Research Articles

Quantification of urinary albumin in clinical samples using smartphone enabled LFA reader incorporating automated segmentation

Abstract Smartphone-assisted urine analyzers estimate the urinary albumin by quantifying color changes at sensor pad of test strips. These strips yield color variations due to the total protein present in the sample, making it difficult to relate to color changes due to specific analyte. We have addressed it using a Lateral Flow Assay (LFA) device for automatic detection and quantification of urinary albumin. LFAs are specific to individual analytes, allowing color changes to be linked to the specific analyte, minimizing the interference. The proposed reader performs automatic segmentation of the region of interest (ROI) using YOLOv5, a deep learning-based model. Concentrations of urinary albumin in clinical samples were classified using customized machine learning algorithms. An accuracy of 96% was achieved on the test data using the k-Nearest Neighbour (k-NN) algorithm. Performance of the model was also evaluated under different illumination conditions and with different smartphone cameras, and validated using standard nephelometer.

Smartphone-assisted colorimetry and array test strip integrated platform for urine multi-index simultaneous precise quantification and personalized healthcare monitoring

As a non-invasive monitoring method, urine analysis is one of the “three routines” of medical examination, which is of great significance for the early diagnosis, therapeutic effect judgment and long-term fine monitoring of chronic diseases such as metabolic system diseases and kidney diseases. It is imperative to develop a home urine routine detection system. In this paper, Smart Urine Analyzer is innovatively developed by combining smartphones with array urine dipsticks, and equipped with the unique design of adjustable bracket suitable for camera position of different smartphones. The optimal colorimetric value was selected for analysis based on chromogenic response characteristics combined with digital image colorimetry, and the breakthrough from qualitative semi-quantification to precise quantification of 9 kinds of urine biomarkers was completed. And a smartphone App for automated image recognition and processing was developed, which was applied for at-home routine healthcare. 100 clinical urine samples were used to evaluate the reliability of the system. Among them, the ROC curve showed a high consistency between Smart Urine Analyzer and the Dirui H-800 urine analyzer in hospital, which further verified the practicability. As a result, the user-friendly Smart Urine Analyzer can share health data and condition with doctors in real time, which provides a new scheme and means for the home detection of urine biomarkers, and has great practical application value as a potential home medical service assistant.

Performance of UF-5000 in rapidly screening out urinary tract infection, predicting Gram-negative bacteria infection.

Urine culture is the gold standard for identifying microorganisms in urine. However, the process is time-consuming and usually takes days to get the results, which impedes timely treatment. This study aimed to evaluate the performance of UF-5000 in excluding bacterial urinary tract infection (UTI) and detecting the presence of Gram-negative bacteria. A total of 1,522 urine specimens were subjected to routine urine analysis and culture. Bacteria, white blood cell counts, and UF-5000 bacteria information flags were compared with urine culture. Bacteria forward scatter (B_FSC), and bacteria fluorescent light intensity (B_FLH) parameters were assessed to determine the optimal angle for discriminating bacterial growth patterns, which was verified in the validation cohort. The optimal cut-off values were 42.2/µL and 100.2/µL to rule out UTI for bacteria at ≥104 CFU/mL and ≥105 CFU/mL, respectively. The agreement of UF-5000 bacterial classification and urine culture was fair (Kappa = 0.227). Regarding the discrimination of bacterial groups, "Gram Neg?" was associated with a specificity of 94.0% and a positive predictive value of 87.2% in the detection of Gram-negative bacteria. By estimating the B_FSC and B_FLH parameters, the best angle was 28° for distinguishing Gram-negative and Gram-positive bacteria. Among the 136 urine specimens with the low angle pattern (<28°) in the validation cohort, 127 specimens were confirmed to contain Gram-negative bacteria. The UF-5000 analyzer is a suitable and rapid tool to exclude negative urine specimens, and bacterial information flags for Gram-negative bacteria could be trusted by clinicians.IMPORTANCEThe strength of our study relied on being the first study assessing the bacteria forward scatter (B_FSC) and bacteria fluorescent light intensity (B_FLH) research parameters with a UF-5000 urine analyzer and establishing the best angle for distinguishing Gram-negative and Gram-positive bacteria. When the bacterial scatter plot angle is less than 28°, the possibility of Gram-negative bacterial infection is more than 80%. Meanwhile, we find that UF-5000 bacterial information flags have a significant advantage in detecting Gram-negative bacteria with a specificity of over 90% and a positive predictive value of over 80%.

Accurate point-of-care lotus biomimetic microfluidic urine testing platform for patients with chronic kidney disease

Improving outcomes of patients with chronic kidney disease (CKD) caused by glomerulonephritis needs regular home urine monitoring. Here we present an accurate, fast and easy-to-operate home urine analyzer using lotus leaf bionic structure for patients with CKD. Bottom of the gradient channel is consisting of bionic micro/nano structure similar with the surface of the lotus leaf. These microstructures can make the viscous resistance of the liquid in the channel greatly reduced, allowing the liquid to be transported spontaneously and directionally under the action of gravity, thus achieving the pump-free directional transport of sample. Urine albumin (UA) concentration, urine turbidity (UT) and urine color (UC), of the sample are obtained by capturing the image of the sample and conducting gradient RGB differential analysis. Historical data storage and cloud data interconnection can assist doctors in diagnosing patient conditions. The limit of detection (LOD) of albumin reaches 4.3 mg/L, which allows accurate diagnosis of microproteinuria. Personalized medical advice is provided by combining the acquired parameters with hospital. 40 samples were tested, the accuracy of the diagnosis results of albuminuria, hydration status and turbidity are 90 %, 92.5 % and 90 % respectively. It can be predicted that this low-cost, flexible and convenient urine detection device for CKD patients at home will inspire more home detection devices in the future.

Performance evaluation and clinical application of the UA-5600 automatic dry chemistry urine analyzer in renal diseases.

Urine testing as a routine screening programme, abnormal test results can be suggestive to clinicians but can sometimes be overlooked, and the establishment of a diagnostic model can better assist clinicians in identifying potential problems. BLD (blood), LEU (leukocyte), PRO (protein) and GLU (glucose) are the four most important parameters in urine testing, and the accuracy of their results is a key concern for clinicians, so it is essential to verify the accuracy of their results. In this study, we evaluated the analytical and clinical performance of Mindray's automatic urine dry chemistry analyzer, the UA-5600 (Hereinafter referred to as the (UA-5600), and the test strips configured with the instrument, and developed a machine-learning (ML) model for kidney disease screening from the results of 11 parameters output from the UA-5600 with the aim of detecting abnormal urine test results. Urine samples from outpatients and inpatients at The First Affiliated Hospital of Sun Yat-sen University were collected from August to September 2022 to evaluate the performance of the Mindray UA-5600 dry chemistry analyzer and test strips. The evaluation of the UA-5600 and its test strips focused on the agreement of the urine BLD and LEU readings with the RBC (red blood cell) and WBC (white blood cell) counts obtained by the Mindray EH-2090 urine formed element analyzer. We also compared the PRO and GLU readings with the results of the Mindray BS-2800M biochemistry analyzer. Urine samples from outpatients and inpatients were retrospectively analysed and grouped according to LIS diagnosis. Additionally, eight ML models for kidney disease screening were developed using 11 parameters measured by the UA-5600. And the model was validated by the validation set. The UA-5600 had an 89.55% concordance rate for BLD and a 91.04% concordance rate for LEU compared to the EH-2090 analyzer. When benchmarked against the BS-2800M, the concordance rates for PRO and GLU were 94.14% and 95.20%, respectively. A total of 1,691 samples were used for the construction of the ML models, of which 346 patients (135 males and 211 females, age range: 18 to 98 years) diagnosed with renal disease, and 1,345 patients (397 males and 948 females, age range: 18 to 92 years) with non-renal disease diagnosed with other conditions. Notably, the Naïve Bayes (NB) model, which was built from the UA-5600 parameters, demonstrated superior predictive capabilities for renal disease, with an area under the receiver operating characteristic curve of 0.9470, a sensitivity of 0.7767, and a specificity of 0.9457. The Mindray UA-5600 demonstrates robust detection abilities for both BLD and LEU, and its results for PRO and GLU align closely with those obtained from the chemistry analyzer. The NB model has a good screening ability and shows promise as an effective screening tool.

Comparison of Two Automated Urine Analyzers (URiSCAN Super + YD Diagnostics and Sysmex UC-3500–UF-5000 Urine Chemistry Analyzer) with Routine Microscopy

Background and Aim: Urinalysis is one of the most commonly performed screening tests in the clinical laboratory to diagnose and monitor various urological as well as systemic conditions. Newly developed automated urine analyzers are expected to routinely screen urine in less time compared to conventional routine microscopy and provide clinicians with prompt clinical information with a lower false-negative rate. The study aimed to evaluate the diagnostic performance of the two automated urine analyzers, URiSCAN Super + YD Diagnostics and Sysmex UC-3500–UF-5000 urine analyzer and compare them with microscopy. Materials and Methods: This was a prospective study performed on 124 randomly selected samples of patients coming to the clinical pathology laboratory at a tertiary care hospital for 3 months between March 1, 2022 and May 30, 2022. Each sample was taken for both automated analyzer testing and microscopic evaluation. The results of urine examination consisting of cells, formed elements, and microorganisms were compared between both the automated urine analyzers and microscopy, and the degree of concordance was calculated utilizing Cohen’s kappa (κ) analysis to see the concordance between all three values. The statistical analyses were performed using SPSS version 25, developed by IBM Co, and P &lt; 0.05 was considered statistically significant. Results: The degree of concordance of red blood cell (RBC), white blood cell (WBC), and epithelial cells (ECs) was good between the two automated analyzers, URiSCAN Super + YD Diagnostics and Sysmex UC-3500–UF-5000 urine analyzer and microscopy with P &lt; 0.05. The concordance between the Sysmex UC-3500–UF-5000 urine analyzer and manual microscopy was better than between manual microscopy and URiSCAN Super + YD Diagnostics. There was no concordance between any of these methods for either crystals, cast, bacteria, and fungi with P &gt; 0.05. Conclusion: The results from the automated analyzers for RBCs, WBCs, and ECs were similar to the result of manual microscopy, and the analyzers can be relied on for testing and can help in reducing turnaround time of routine urine examination. However, bacteria, fungi, dysmorphic red cells, casts, and crystals need to be analyzed by microscopic examination before giving a final diagnosis.

Evaluation of labsan tricell-1000 automated urine analyzer for the erythrocyte and leukocyte count performances

Evaluation of labsan tricell-1000 automated urine analyzer for the erythrocyte and leukocyte count performances

Simultaneous Detection of Citric Acid and Oxalic Acid Based on Dual Spectrum and Biomimetic Peroxidase for Urolithiasis Screening with a Fully Automatic Urine Analyzer (Small 9/2024)

Simultaneous Detection of Citric Acid and Oxalic Acid Based on Dual Spectrum and Biomimetic Peroxidase for Urolithiasis Screening with a Fully Automatic Urine Analyzer (Small 9/2024)

Assessing Concordance of Results: A Comparative Study of the Manual and Automated Urinalysis Methods.

An accurate urine analysis is a good indicator of the status of the renal and genitourinary system. However, limited studies have been done on comparing the diagnostic performance of the fully automated analyser and manual urinalysis especially in Ghana. This study evaluated the concordance of results of the fully automated urine analyser (Sysmex UN series) and the manual method urinalysis at the Komfo Anokye Teaching Hospital in Kumasi, Ghana. Methodology. Sixty-seven (67) freshly voided urine samples were analysed by the automated urine analyser Sysmex UN series and by manual examination at Komfo Anokye Teaching Hospital, Ghana. Kappa and Bland-Altman plot analyses were used to evaluate the degree of concordance and correlation of both methods, respectively. Substantial (κ = 0.711, p < 0.01), slight (κ = 0.193, p = 0.004), and slight (κ = 0.109, p < 0.001) agreements were found for urine colour, appearance, and pH, respectively, between the manual and automated methods. A strong and significant correlation (r = 0.593, p < 0.001) was found between both methods for specific gravity with a strong positive linear correlation observed for red blood cell count (r = 0.951, R2 = 0.904, p < 0.001), white blood cell count (r = 0.907, R2 = 0.822, p < 0.001), and epithelial cell count (r = 0.729, R2 = 0.532, p < 0.001). A perfect agreement of urine chemistry results in both methods was observed for nitrite 67 (100%) (κ = 1.000, p < 0.001) with a fair agreement for protein 46 (68.7%) (κ = 0.395, p < 0.001). A strong agreement was found in both methods for the presence of cast 65 (97.0%) (κ = 0.734, p < 0.001) with no concordance observed for the presence of crystals (κ = 0.115, p = 0.326) and yeast-like cells (YLC) (κ = 0.171, p = 0.116). The automated and manual methods showed similar performances and good correlation, especially for physical and chemical examination. However, manual microscopy remains necessary to classify urine sediments, particularly for bacteria and yeast-like cells. Future research with larger samples could help validate automated urinalysis for wider clinical use and identify areas requiring improved automated detection capabilities.

Rapid Diagnosis of Urinary Tract Infection by Flow Cytometry-Based Urine Analyser

Rapid Diagnosis of Urinary Tract Infection by Flow Cytometry-Based Urine Analyser

Colorimetry-based and smartphone-assisted machine-learning model for quantification of urinary albumin

The presence of albumin in the urine is indicative of kidney damage and can occur due to several underlying conditions, such as diabetes. The concentration of albumin in urine is used for the diagnosis and staging of chronic kidney disease (CKD). In clinical samples, the detection of albumin at lower concentrations is crucial for the early diagnosis and monitoring of CKD. Current urine analyzers precisely quantify albumin but are expensive and difficult to use in point-of-care (PoC) settings. Here, we demonstrate the quantification of albumin concentration in a urine sample using colorimetry. This model presents an accessory-free urine analyzer that uses a smartphone and customized machine-learning algorithms. Here, a urine sample is introduced onto a chemically impregnated dipstick that exhibits a change in color with the amount of albumin. Images of the urine dipsticks are captured using a smartphone camera under different illumination/experimental conditions and are processed to extract changes in the color values arising due to changes in the concentration of urinary albumin. Albumin concentrations are estimated from changes in color values. We used customized machine-learning algorithms to classify albumin concentrations and mitigate the effect of ambient light conditions. The k-nearest neighbor algorithm yielded an average classification accuracy of 96% with a detection limit of 4 mg l−1. The proposed scheme can be extensively used to monitor albumin concentration in PoC settings.

Simultaneous Detection of Citric Acid and Oxalic Acid Based on Dual Spectrum and Biomimetic Peroxidase for Urolithiasis Screening with a Fully Automatic Urine Analyzer.

Urolithiasis stands as a prevalent ailment within the urinary system, with hyperoxaluria and hypocitraturia being the most frequent manifestations characterized by excessive oxalic acid (OA) and deficient citric acid (CA) levels in urine. Detecting these compounds in urine quantitatively holds paramount importance for early urolithiasis screening. Existing methodologies fall short in achieving simultaneous and on-site identification of OA and CA, posing challenges for accurate urolithiasis screening. Addressing this concern, the study successfully accomplishes the concurrent identification of OA and CA in urine through a combination of dual-spectral analysis and biomimetic peroxidase utilization. Bovine serum albumin and dithiothreitol-modified copper nanoclusters (BSA-DTT-CuNCs) are employed as biomimetic peroxidases, effectively mitigating interference and enabling the simultaneous determination of OA and CA. The quantification range spans from 0 to 12mm for OA and 0.5 to 2.5mm for CA, with detection limits of 0.18 and 0.11mm, respectively. To facilitate swift and on-location urine analysis, a fully automated urine analyzer (FAUA) is introduced that streamlines the process of biomarker pretreatment and identification within urine samples. Validation with real urine samples from urolithiasis patients demonstrates the method's diagnostic precision, highlighting the dual-spectral technique and analyzer's promising role in urolithiasis screening.

B-010 Comparison of Manual Urine Dipstick and Microscopy With Automated UC-3500 and UF-4000 Sysmex UN Series Urinalysis; A Comparative Experimental Research Approach at an Urban Referral Hospital in Ghana

Abstract Background Urine analysis is one of the most common tests for assessing urinary-tract and kidney diseases. In recent years several automated instruments examining urine have been introduced. Though the introduction of automated urine analyzers is expected to reduce the labour involved, turnaround time and potential assay variations, manual urine analysis remains the gold standard or reference method for the analysis of urine. The new Sysmex UN-Series is believed to offer superior performance than other automated systems and manual methods. Methodology A total of 67 routine freshly voided midstream urine samples were analyzed within 1 hour in our study. The results of the automated system were compared with the results of the manual processing which consisted of physical, dipstick chemistry and sediment analysis. Correlation and concordance/agreement between the automated and the manual system were assessed by Bland-Altman plot and Cohen’s Kappa analysis respectively. Results There was a significant fair agreement between the manual analysis and Sysmex UF-3500, in the determination of urine physicochemical parameters (k = 0.193, P = 0.004). Both showed a significant moderate correlation (r = 0.593, P &amp;lt; 0.001) and a significant fair agreement (κ = 0.109, P &amp;lt; 0.001) for specific gravity and pH determination respectively. The best concordance between the two methods was in the nitrates (κ = 1.000, P &amp;lt; 0.001). Both systems recorded strong positive correlation for RBC (r = 0.951 and R2 = 0.904) and WBC (r = 0.91 and R2 = 0.822, P &amp;lt; 0.001) counts. Together, they had a significant good agreement [(65/67(97.0%), κ = 0.734, P &amp;lt; 0.001)] and a significant fair agreement [(48/67 (71.6%) κ = 0.065, P = 0.491)] for cast and bacteria counts correspondingly. Conclusion We conclude that the new automated Sysmex-UN series urine analyzer can be safely used in our laboratory since it demonstrated good performance and compatibility with the manual method. We recommend that microscopy results of the automated platform should be confirmed by manual microscopy, particularly in pathological samples.

Comparative Evaluation between Visual and Automated Dipstick Urinalyses in Dogs

Urine test strips are commercially available and can be assessed with semi-automated analyzers or by visual assessment. This study aimed to compare the visual and automated evaluations of dipstick variables in canine urine samples. One hundred and nineteen urine samples were evaluated. Automated analysis was performed on a veterinary urine analyzer URIT-50Vet (URIT Medical Electronic) with UC VET13 Plus strips. Multistix 10 SG dipsticks (Siemens Healthcare GmbH, Erlangen, Germany) were used for visual evaluation, along with a refractometer (Clinical Refractometer Atago T2-Ne, Atago Co., Tokyo, Japan) for urine specific gravity measurements. A linear relationship was observed between the pH measurements (p = 0.2) of the two methods; the Passing-Bablok procedure was valid since neither proportional nor systematic significant errors were observed. Comparing the two methods, the correlation for urine specific gravity was poor (p = 0.01, CI 0.667-1.000). Moderate agreement was demonstrated for proteins (κ = 0.431), bilirubin (κ = 0.434) and glucose (κ = 0.450). Agreement was substantial for blood (κ = 0.620) and poor for leukocytes (κ = 0.100). Poor agreement was observed for ketones (κ = -0.006). Apart from the pH analysis, visual and automated dipstick urinalyses should not be used interchangeably. Multiple urine samples obtained from the same dog during the day should be evaluated using the same method to overcome erroneous results.

Evaluation of the Sysmex UF-4000i urine analyzer as a screening test to rule out urinary tract infection and reduce urine cultures.

Urinary tract infection (UTI) diagnosis by urine culture is time- and labor- consuming. In the Ibn Rochd microbiology laboratory, up to 70% of urine culture samples yield no growth or insignificant growth. To evaluate the new generation of Sysmex UF-4000i fluorescence flow cytometry analyzer with a blue semiconducting laser as a method to rule out negative urine samples for UTI, in comparison of urine culture. Flow cytometry and microbiological analysis were performed on 502 urine samples included in the study. We used ROC analysis to determine cutoff points at which optimal sensitivity and specificity are achieved for clinical use. Our results showed that bacteria count at a cut-off of 100/μL, and/or the leucocytes count ≥45/μL are the optimal indicator for positive culture results. At these cut off, bacteria sensitivity (SE), specificity (SP), Positive predictive value (PPV) and negative predictive value (NPV) were 97,3%, 95%, 87,8% and 98,8% respectively. For leucocytes, SE, SP, PPV and NPV were 99,1%, 95,8%, 88,6% and 99,7% respectively. The bacterial and leucocytes counts generated by UF-4000i analysis may be useful in our context as a rapid screening to exclude UTI by reducing about 70% of urines cultures and then workload. Nevertheless, further validation is needed for different patient groups especially with urological disease or immunocompromised patients.

The Correlation between Leukocytes and Bacterial Number from Urine of Type 2 Diabetes Mellitus Patients Using Urine Analyzer

Diabetes Mellitus (DM) is a chronic metabolic disorder. DM can occur if the pancreas does not produce enough insulin or the insulin that is produced isn’t effectively used. Patients with diabetes mellitus have a high risk for chronic microvascular complications, including infection. This research aimed to determine the correlation between leukocytes and bacterial number from urine of patients with Type 2 Diabetes Mellitus at the X Hospital Blitar. This research used a cross sectional method, with 47 urine samples of Type 2 Diabetes Mellitus patients by purposive sampling, were tested with urine analyzer. The result showed that the number of high leukocytes was 13 (28%) respondents and normal leukocytes was 34 (72%) respondents. While the number of bacteria from respondents was normal 21 (45%) and high number was 26 (55%). Based on the Spearman-rho correlation test on SPSS, it can be concluded that there was a significant relationship between leukocytes with bacterial number of urine from Type 2 Diabetes Mellitus patients, with sig = 0,000 (0,05) and the correlation coefficient = 0,515.

Performance Evaluation of Urine Osmolality Measurement on Sysmex UF-5000 and the Effect of Molecules and Particles in Urine.

Urine osmolality determines the concentration ability of the kidney. Therefore, it is used to assess the body's hydration status, electrolyte levels, and acid-base disturbances. We aimed to evaluate the analytical performance of osmolality measurement of the Sysmex UF-5000 (UF-5000), to examine the effect of different molecules and particles in the urine on the osmolality measurement. Complete urinalysis and conductivity-based osmolality analysis with UF-5000 and osmolality analysis with Advanced® Model 3320 Micro-Osmometer (AI-3320) by freezing point reduction method were performed in the urine samples. Samples were grouped as negative, glucosuria, proteinuria, hematuria, pyuria, crystalluria, and urobilinogen. Total impressions were calculated as < 5% and accuracy values were < 1.66% in both analyzers. The regression equation was found to be y = -12.54 + 0.956x and the relative difference between the analyzers was 8.7% in 586 samples. When patients with Glucose > 2+ were excluded the regression equation of 507 samples was found as y = 5.10 + 0.948x and the relative difference was 4.6%. The percentages of samples with a difference greater than the allowable difference were 18.8%, 11.6%, 35.9%, 13.7%, 18.7%, and < 12.2% in all samples, all samples without glucosuria > 2+, glucosuria, glucosuria < 3+, proteinuria, and other subgroups, respectively. Considering the good accessibility of the automated routine complete urine analyzer, UF-5000 can be considered to determine whether urine osmolality is within reference or should be measured by methods based on colligative properties. Thus, referral of patients to a clinic that uses the colligative measurement method may be used more effectively.

Automated complete blood counter, urine analyzer and urine dipstick test results are correlated with thoma cell counting chamber counts in the diagnosis of dialysis related peritonitis in children

IntroductionPeritoneal dialysis is the treatment of choice for end-stage renal disease. Peritoneal dialysis related peritonitis is of great importance for patient and technical survival. The aim of our study was to evaluate the accuracy and the correlation between the three methods (complete blood count, urinalysis device, urine dipstick test) and with the reference manual method (Thoma Cell Counter Chamber). Materials and methodsWe retrospectively analyzed 167 peritoneal fluid samples taken from 25 patients receiving peritoneal dialysis treatment. Leukocyte counts were evaluated with Thoma Cell Counter Chamber, complete blood count, urinalysis device and urine dipstick test. ResultsThere was a significant positive correlation between Thoma Cell Counter Chamber and complete blood count results (Spearman's rho=0.70), between Thoma Cell Counter Chamber and urinalysis device (Spearman's rho=0.73), and between Thoma Cell Counter Chamber and urine dipstick test (Spearman's rho=0.71). Area under curve for complete blood count, urinalysis device and urine dipstick test were 0.93, 0.94 and 0.89 respectively, indicating good accuracy. Sensitivity and specificity were 89.7% and 86.7% in the complete blood count analysis (associated criterion: 130 cells/mm3). Sensitivity and specificity were 89.7% and 86.7% in the urinalysis device (associated criterion: 10 cells/HPF). Sensitivity and specificity were 79.6% and 91.4% when in the urine dipstick test analysis (associated criterion: +1 positivity). The Bland-Altman plot showed good agreement. ConclusionAutomatic complete blood count and urinalysis devices have good correlation and agreement with manual method in the diagnosis of peritonitis in the pediatric age group. Urine dipstick test in the home setting can be useful for screening patients with suspected peritonitis.

Performance evaluation of Hipee S2 point-of-care testing urine dipstick analyser: a cross-sectional study

ObjectiveWith advances in mobile technology, smartphone-based point-of-care testing (POCT) urinalysis hold great potential for disease screening and health management for clinicians and individual users. The purpose of this study is...

Reference Value Evaluation of Urine Sediment in Indonesian Adult Population Using Automated Urine Analyzer

Locally established clinical laboratory reference value are required to interpret laboratory test results for screening, diagnosis, prognosis, determining retest criteria or as microscopic confirmation. The objective of this study is to establish urine sediments reference value by using automated urine flow cytometry, investigating erythrocyte, leukocyte, epithelial cells, types of epithelial cells, bacteria, casts, pathologic casts, crystals, yeast, sperm, and mucus in Indonesian population using Sysmex UF-4000. A cross sectional study was conducted in October 2018 – April 2019 at R. Said Sukanto National Police Hospital in Jakarta. The study involved 240 participants comprised of clinically healthy 120 males and 120 females aged 18-65, with normal urine chemistry, hematology, blood glucose, liver function (AST, ALT), and renal function (urea, creatinine). The reference value was reported in microliter (μL) or in High Performance Field (HPF) or Low Performance Field (LPF). Mann-Whitney test through MedCalc software was used to test significant differences with p value &lt;0.05. This study observed significant differences between males and females in erythrocyte, epithelial cells, epithelial cell variety, bacteria, pathologic casts, and yeast”which are evidently higher in female patients. However, the mucus in LPF levels is found to be higher in male patients instead of the female, disparate than current reference value. Therefore, the reference value results were separated between the two groups in those aspects, whereas the results of other urine sediment characteristics studied were combined. As most reference value obtained are still within range of existing references, reference value established with Sysmex UF-4000 can be useful.