Using the Sysmex UF-4000 urine flow cytometer for rapid diagnosis of urinary tract infection in the clinical microbiological laboratory.

BACKGROUND: Urinary tract infections are responsible for a significant worldwide disease burden. Performing urine culture is time consuming and labor intensive. Urine flow cytometry might provide a quick and reliable method to screen for urinary tract infection. METHODS: We analyzed routinely collected urine samples received between 2020 and 2022 from both inpatients and outpatients. The UF-4000 urine flow cytometer was implemented with an optimal threshold for positivity of ≥100 bacteria/µL. We thereafter validated the prognostic value to detect the presence of urinary tract infection (UTI) based on bacterial (BACT), leukocyte (WBC), and yeast-like cell (YLC) counts combined with the bacterial morphology (UF gram-flag). RESULTS: In the first phase, in 2019, the UF-4000 was implemented using 970 urine samples. In the second phase, between 2020 and 2022, the validation was performed in 42,958 midstream urine samples. The UF-4000 screen resulted in a 37% (n = 15,895) decrease in performed urine cultures. Uropathogens were identified in 18,673 (69%) positively flagged urine samples. BACT > 10.000/µL combined with a gram-negative flag had a >90% positive predictive value for the presence of gram-negative uropathogens. The absence of gram-positive flag or YLC had high negative predictive values (99% and >99%, respectively) and are, therefore, best used to rule out the presence of gram-positive bacteria or yeast. WBC counts did not add to the prediction of uropathogens. CONCLUSION: Implementation of the UF-4000 in routine practice decreased the number of cultured urine samples by 37%. Bacterial cell counts were highly predictive for the presence of UTI, especially when combined with the presence of a gram-negative flag.

Surface-enhanced Raman Spectroscopy in urinalysis of hypertension patients with kidney disease.

Arterial hypertension (AH) is a multifactorial and asymptomatic disease that affects vital organs such as the kidneys and heart. Considering its prevalence and the associated severe health repercussions, hypertension has become a disease of great relevance for public health across the globe. Conventionally, the classification of an individual as hypertensive or non-hypertensive is conducted through ambulatory blood pressure monitoring over a 24-h period. Although this method provides a reliable diagnosis, it has notable limitations, such as additional costs, intolerance experienced by some patients, and interferences derived from physical activities. Moreover, some patients with significant renal impairment may not present proteinuria. Accordingly, alternative methodologies are applied for the classification of individuals as hypertensive or non-hypertensive, such as the detection of metabolites in urine samples through liquid chromatography or mass spectrometry. However, the high cost of these techniques limits their applicability for clinical use. Consequently, an alternative methodology was developed for the detection of molecular patterns in urine collected from hypertension patients. This study generated a direct discrimination model for hypertensive and non-hypertensive individuals through the amplification of Raman signals in urine samples based on gold nanoparticles and supported by chemometric techniques such as partial least squares-discriminant analysis (PLS-DA). Specifically, 162 patient urine samples were used to create a PLS-DA model. These samples included 87 urine samples from patients diagnosed with hypertension and 75 samples from non-hypertensive volunteers. In the AH group, 35 patients were diagnosed with kidney damage and were further classified into a subgroup termed (RAH). The PLS-DA model with 4 latent variables (LV) was used to classify the hypertensive patients with external validation prediction (P) sensitivity of 86.4%, P specificity of 77.8%, and P accuracy of 82.5%. This study demonstrates the ability of surface-enhanced Raman spectroscopy to differentiate between hypertensive and non-hypertensive patients through urine samples, representing a significant advance in the detection and management of AH. Additionally, the same model was then used to discriminate only patients diagnosed with renal damage and controls with a P sensitivity of 100%, P specificity of 77.8%, and P accuracy of 82.5%.

Evaluation of a urine dipstick protein to urine specific gravity ratio for the detection of proteinuria in dogs and cats.

BACKGROUND: The utility of urine dipsticks for the quantification of proteinuria is limited because of the influence of urine specific gravity (USG). To circumvent the need for urine protein creatinine ratios (UPCR) some have proposed a calculated dipstick urine protein to USG ratio (DUR) for the detection of proteinuria. However, the performance of DUR has not been evaluated in veterinary patients. OBJECTIVES: Evaluate the correlation between DUR and UPCR, while also assessing the effect of urine characteristics on this relationship and evaluating the performance of DUR in detecting proteinuria. ANIMALS: Urine samples from 308 dogs and 70 cats. METHODS: Retrospective cohort study of urinalyses and UPCRs from dogs and cats collected between 2016 and 2021. RESULTS: Both canine and feline urine samples showed a positive moderate correlation between the UPCR and DUR. The correlation was not influenced by the presence of active urine sediment, glucosuria, or urine pH. In detecting canine urine samples with a UPCR >0.5, an optimal DUR of 1.4 had sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 89%, 83%, 96%, and 63%, respectively. In detecting feline urine samples with a UPCR >0.4, an optimal DUR of 2.1 had sensitivity, specificity, PPV, and NPV of 70%, 100%, 100%, and 75%, respectively. CONCLUSIONS AND CLINICAL IMPORTANCE: Use of the DUR can be a relatively reliable method for identification of proteinuria. However, given its poor NPV, the DUR cannot be recommended for exclusion of proteinuric patients.

Point-of-care image-based quantitative urinalysis with commercial reagent strips: Design and clinical evaluation.

Urinalysis is a useful test as an indicator of health or disease and as such, is a part of routine health screening. Urinalysis can be undertaken in many ways, one of which is reagent strips used in the general evaluation of health and to aid in the diagnosis and monitoring of kidney disease. To be effective, the test must be performed properly, and the results interpreted correctly. However, different light conditions and colour perception can vary between users leading to ambiguous readings. This has led to camera devices being used to capture and generate the estimated biomarker concentrations, but image colour can be affected by variations in illumination and inbuilt image processing. Therefore, a new portable device with embedded image processing techniques is presented in this study to provide quantitative measurements that are invariant to changes in illumination. The device includes a novel calibration process and uses the ratio of RGB values to compensate for variations in illumination across an image and improve the accuracy of quantitative measurements. Results show that the proposed calibration method gives consistent homogeneous illumination across the whole image. Comparisons against other existing methods and clinical results show good performance with a correlation to the clinical values. The proposed device can be used for point-of-care testing to provide reliable results consistent with clinical values.

Microscopic urinary particle detection by different YOLOv5 models with evolutionary genetic algorithm based hyperparameter optimization.

The diagnosis of kidney disease often involves analysing urine sediment particles. Traditionally, urinalysis was performed manually by collecting urine samples and using a centrifuge, which was prone to manual errors and relied on labour-intensive processes. Automated urine sediment microscopy, based on machine learning models, requires segmentation and feature extraction, which can hinder model performance due to intrinsic characteristics of microscopic images. Deep learning models based on convolutional neural networks (CNNs) often rely on a large number of manually annotated data, making the system computationally complex. This study propose an advanced deep learning model based on YOLOv5, which offers faster performance and requires comparatively less data. The proposed model used five variants of the YOLOv5 model (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) to detect six categories of urine particles (erythrocyte, leukocyte, crystals, cast, mycete, epithelial cells) from microscopic urine sediment images. The dataset involved 5376 images of urine sediments with 6 particles. There are 30 sets of hyperparamreteres are employed in the YOLOv5 model. To optimize the hyperparameters and fine-tune with the urine sediment dataset and for training each model, the system employed a genetic algorithm (GA) based on evolutionary principles named as Evolutionary Genetic Algorithm (EGA). Among the six categories of detected particles mycete achieved maximum performance with a mAP of 97.6 % and crystals achieved minimum performance with a mAP of 81.7 % with YOLOv5x model compared to other particles. To optimize the hyperparameters for training each model, the system employed a genetic algorithm (GA) based on evolutionary principles named as Evolutionary Genetic Algorithm (EGA). Among all the models, YOLOv5l and YOLOv5x performed the best. YOLOv5l achieved a mean average precision (mAP) of 85.8 % while YOLOv5x achieved a mAP of 85.4 % at an IoU threshold of 0.5. The detection speed per image was 23.4 ms for YOLOv5l and 28.4 ms for YOLOv5x. The proposed method developed a faster and better automated microscopic model using advanced deep learning techniques to detect urinary particles from microscopic urine sediment images for kidney disease identification. The method demonstrated strong performance in urinalysis.

Machine learning-assisted image label-free smartphone platform for rapid segmentation and robust multi-urinalysis.

This study presents a groundbreaking approach for the early detection of chronic kidney disease (CKD) and other urological disorders through an image-label-free, multi-dipstick identification method, eliminating the need for complex machinery, label libraries, or preset coordinates. Our research successfully identified reaction pads on 187 multi-dipsticks, each with 11 pads, leveraging machine learning algorithms trained on human urine data. This technique aims to surpass traditional colourimetric methods and concentration-colour curve fitting, offering more robust and precise community screening and home monitoring capabilities. The developed algorithms enhance the generalizability of machine learning models by extracting primary colours and correcting urine colours on each reaction pad. This method's cost-effectiveness and portability are significant, as it requires no additional equipment beyond a standard smartphone. The system's performance rivals professional medical equipment without auxiliary lighting or flash under regular indoor light conditions, effectively managing false positives and negatives across various categories with remarkable accuracy. In a controlled experimental setting, we found that random forest algorithms, based on a Bagging strategy and applied in the HSV colour space, showed optimal results in smartphone-assisted urinalysis. This study also introduces a novel urine colour correction method, significantly improving machine learning model performance. Additionally, ISO parameters were identified as crucial factors influencing the accuracy of smartphone-based urinalysis in the absence of additional lighting or optical configurations, highlighting the potential of this technology in low-resource settings.

Diagnostic validation study of rapid urinary tract infection diagnosis kit at peripheral health facilities of West Bengal, India.

Patients reporting to the outpatient departments of peripheral health care settings in India with symptoms of urinary tract infection (UTI) receive one or the other antibiotic before culture confirmation and out of the total culture confirmed UTI cases, in less than one third cases the prescribed antibiotics matches to the antibiotic sensitivity test result. Hence, in this study, an indigenous point-of-care (POCT) rapid diagnostic kit (Rapidogram) for UTI was validated against conventional urine culture and sensitivity to understand its possible applicability at peripheral health care settings. This cross-sectional study was conducted during November 2021 to June 2022 in OPDs of two peripheral hospitals. A sample size of 300 was calculated using prevalence of urinary tract infection (UTI) as 33% for sensitivity and specificity using Buderer's formula. Urine specimens were collected following standard aseptic procedures from the recruited suspected UTI cases and transferred to laboratory maintaining the cold chain. The validation work up was done in two sections: lab validation and field validation. Out of 300 urine samples, 29 were found positive for the growth of UTI pathogen by both methods and 267 were found negative by both methods. Thus, the kit shows very high specificity (99.6%; 97.9-99.9%) and considerably high sensitivity (90.6%; 74.9-98.0%). We also observed higher PPV, NPV, test accuracy (> 96%). Diagnostic Odds Ratio and Youden index were respectively 2581 and 0.89. Clinical data showed that 44% of the suspected UTI cases were prescribed at least one antibiotic before urine test. Mostly they received Norfloxacin whereas the mostly identified organism E.coli was sensitive to Nitrofurantoin. In the context of absence of microbiology facility at peripheral setting and rampant empirical use of antibiotics in UTI, this highly specific and sensitive POCT for UTI may be used as it not only identifies the organism, also shows the antibiotic sensitivity pattern.

Development and validation of artificial intelligence models to predict urinary tract infections and secondary bloodstream infections in adult patients.

BACKGROUND: Traditional culture methods are time-consuming, making it difficult to utilize the results in the early stage of urinary tract infection (UTI) management, and automated urinalyses alone show insufficient performance for diagnosing UTIs. Several models have been proposed to predict urine culture positivity based on urinalysis. However, most of them have not been externally validated or consisted solely of urinalysis data obtained using one specific commercial analyzer. METHODS: A total of 259,187 patients were enrolled to develop artificial intelligence (AI) models. AI models were developed and validated for the diagnosis of UTI and urinary tract related-bloodstream infection (UT-BSI). The predictive performance of conventional urinalysis and AI algorithms were assessed by the areas under the receiver operating characteristic curve (AUROC). We also visualized feature importance rankings as Shapley additive explanation bar plots. RESULTS: In the two cohorts, the positive rates of urine culture tests were 25.2% and 30.4%, and the proportions of cases classified as UT-BSI were 1.8% and 1.6%. As a result of predicting UTI from the automated urinalysis, the AUROC were 0.745 (0.743-0.746) and 0.740 (0.737-0.743), and most AI algorithms presented excellent discriminant performance (AUROC > 0.9). In the external validation dataset, the XGBoost model achieved the best values in predicting both UTI (AUROC 0.967 [0.966-0.968]) and UT-BSI (AUROC 0.955 [0.951-0.959]). A reduced model using ten parameters was also derived. CONCLUSIONS: We found that AI models can improve the early prediction of urine culture positivity and UT-BSI by combining automated urinalysis with other clinical information. Clinical utilization of the model can reduce the risk of delayed antimicrobial therapy in patients with nonspecific symptoms of UTI and classify patients with UT-BSI who require further treatment and close monitoring.

Validity of combined hydration self-assessment measurements to estimate a low vs. high urine concentration in a small sample of (tactical) athletes.

PURPOSE: Relationships between body weight, urine color (Uc), and thirst level (WUT) have been proposed as a simple and inexpensive self-assessment method to predict dehydration. This study aimed to determine if this method also allowed us to accurately identify a low vs. high urine concentration in (tactical) athletes. METHODS: A total of n = 19 Army Reserve Officer Training Corps cadets and club sports athletes (22.7 ± 3.8 years old, of which 13 male) were included in the analysis, providing morning body weight, thirst sensation, and Uc for five consecutive days. Each item received a score 0 or 1, resulting in a WUT score ranging from 0 (likely hydrated) to 3 (very likely dehydrated). WUT model and individual item outcomes were then compared with a ≥ 1.020 urine specific gravity (USG) cut-off indicating a high urine concentration, using descriptive comparisons, generalized linear mixed models, and logistic regression (to calculate the area under the curve (AUC)). RESULTS: WUT score was not significantly predictive of urine concentration, z = 1.59, p = 0.11. The AUC ranged from 0.54 to 0.77 for test days, suggesting a fair AUC on most days. Only Uc was significantly related to urine concentration, z = 2.49, p = 0.01. The accuracy of the WUT model for correctly classifying urine samples with a high concentration was 68% vs. 51% of samples with a low concentration, resulting in an average accuracy of 61%. CONCLUSION: This study shows that WUT scores were not predictive of urine concentration, and the method did not substantially outperform the accuracy of Uc scoring alone.

[Evaluation of Sysmex UF-5000 flow cytometer flag BACT-info for Gram discrimination in urinary tract infection]. / Evaluación del parámetro BACT-info del citómetro de flujo UF-5000 (Sysmex) para la clasificación gram en infección del tracto urinario.

OBJECTIVE: Urine culture as a gold standard for the diagnosis of urinary tract infection (UTI) involves a considerable workload in Clinical Microbiology Departments, due to the high number of samples received that will ultimately be negative. Therefore, it is necessary to use screening systems that also reduce the turnaround time for UTI diagnosis. The new flow cytometer UF-5000 (Sysmex Corporation) is able to differentiate between Gram-negative and Gram-positive bacteria using the BACT-info parameter according to manufacturer. The aim of our study was to evaluate the gram discrimination ability of the UF-5000 cytometer. METHODS: A prospective study with 449 urine samples collected consecutively was conducted, in the period 7/3/2022-27/5/2022, in which the BACT-info flag was compared with urine culture as the reference method. RESULTS: The sensitivity obtained for both Gram-negative and Gram-positive bacteria was above 95%. However, for Gram-positive bacteria, the moderate Kappa index (0.49) and the low positive predictive value (37.1%) indicated that the correlation between BACT-info flag and urine culture was not acceptable and should not be reported to the requesting clinician. CONCLUSIONS: Implementation of the third generation UF-5000 cytometer represents a significant advance in the aetiological orientation of UTIs caused by Gram-negative bacteria. Reporting the Gram morphology in the urine samples reduces the response time in the microbiological diagnosis of UTI, which would have an impact on the reduction and optimisation of empirical treatment, and thus on the generation of antimicrobial resistance.

Evaluation of the fully automated urine particle analyzer UF-1500.

BACKGROUND AND AIMS: This study primarily assessed the performance of the UF-1500, the novel and compact model of the fully automated urine particle analyzer and evaluated its performance against the existing UF-5000 instrument. MATERIALS AND METHODS: A total of 648 residual urine specimens were randomly collected and examined using both the UF-1500 and UF-5000 instruments as well as manual microscopy. For each parameter, the concordance rates and detection accuracy of the UF-1500 against manual microscopy were compared with the UF-5000. RESULTS: The concordance rates between the UF-1500 and manual microscopy were 75.3%-98.5%. The UF-1500 concordance rates within one group agreement were observed to be >90%, for all parameters except for YLCs. The differences within one group agreement between the UF-1500 and manual microscopy were insignificant, in comparison to the UF-5000, with exceptions noted for ECs and YLCs. The sensitivity and specificity of the UF-1500 for RBCs, WBCs, Squa.ECs, and BACT exceeded 80%, while the positive predictive values of ECs and CASTs were below 70%. CONCLUSION: The UF-1500 exhibited a performance that was comparable to the existing instrument, the UF-5000, and was suitable to be introduced in clinical practice. For the samples with suspected false-positive or false-negative results, a manual microscopic examination is required for accurate testing.

Contrast Media Ioversol Interferes with Urinalysis.

BACKGROUND: Ioversol is a commonly used non-ionic radiological contrast media in medical imaging to enhance the visualization of blood vessels, tissues, or organs. However, if it is not completely excreted, ioversol can interfere with urinalysis and lead to abnormal test results. METHODS: This study reported a case where the contrast media ioversol interfered with Sysmex UN automated urine analyzer. RESULTS: UC-3500 displayed no test results except the error code "0401". UF-4000 indicated "abnormally high RBCs" and no parameter results. CONCLUSIONS: Urine specimens containing contrast media are considered unqualified samples. Urinalysis should be performed only after the patient has completely excreted the contrast media.

[Urinalysis in dogs and cats, part 2: Urine sediment analysis]. / Die Urinuntersuchung bei Hund und Katze, Teil 2: Urinsedimentanalyse.

Examination of the urine sediment is part of a routine urinalysis and is undertaken in order to identify insoluble particles in the urine. This procedure is mainly used in the context of diagnostic evaluation of urinary tract diseases, but may also be useful for the diagnosis of systemic diseases and intoxications. Analysis of fresh urine is recommended as changes in cell morphology, cell lysis and in vitro crystal formation may occur in the course of its storage. Manual urine sediment analysis is still performed in many veterinary practices. Native wet-mount preparations are suitable for the identification and quantification of urine sediment particles. The examination of stained wet-mount preparations or air-dried smears may be necessary to further differentiate cells and to identify bacteria. For several years, automatic urine sediment analyzers have been available in veterinary medicine. These save considerable time and staff resources, however verification of the automatically generated results by an experienced observer remains necessary. Urine sediment particles that are frequently identified and clinically relevant include red blood cells, white blood cells, different types of epithelial cells, crystals, and casts as well as bacteria. Furthermore, parasite eggs, fungal hyphae, lipid droplets, spermatozoa, fibres, hair, mucus, plant parts or environmental contaminations may be found in the urine sediment and result in a complication of the result interpretation.

Diagnosis of urinary tract infection in infants under 3 months with fever without a source: reliability of urinalysis and urine culture.

BACKGROUND: Urinary tract infection (UTI) is infants' most common serious bacterial infection. This study aimed to investigate the reliability of urianalysis (UA) to predict UTI, to specify the colony forming units (CFU)/ml threshold for diagnosis, and to identify variables that help suspect bacteremia in infants under 3 months with UTI. METHODS: We reviewed clinical records of children under 3 months hospitalized for a fever without source and recorded age, sex, days of fever pre-consultation, temperature and severity at admission, discharge diagnoses, laboratory tests, and treatments. According to the discharge diagnosis, we divided them into UTIs (-) and (+) with or without bacteremia. RESULTS: A total of 467 infants were admitted: 334 with UTI and 133 without UTI. In UTIs (+), the pyuria had a sensitivity of 95.8% and bacteria (+) 88.3%; specificity was high, especially for nitrites (96.2%) and bacteria (+) (92.5%). Positive predictive value (PPV) for nitrites was 95.9%, for bacteria 96.7%, and oyuria 92.5%. Escherichia coli was present in 83.8% of urine and 87% of blood cultures. UTIs with bacteremia had inflammatory urinalysis, urine culture > 100,000 CFU/ml, and higher percentage of C reactive protein (CRP) > 50 mg (p= 0.002); 94.6% of the urine culture had > 50,000 CFU. CONCLUSIONS: The pyuria and bacteria (+) in urine obtained by catheterization predict UTI. The cut-off point for diagnosis was ≥ 50,000 CFU/ml. No variables to suspect bacteremia were identified in this study.

INTRODUCCIÓN: La infección del tracto urinario (ITU) es una infección bacteriana grave frecuente en lactantes. El objetivo de este trabajo fue investigar la fiabilidad del análisis de orina (AO) para predecirla, precisar el umbral de unidades formadoras de colonias (UFC)/ml para el diagnóstico y buscar variables que ayuden a sospechar de bacteriemia en lactantes menores de 3 meses con ITU. MÉTODOS: Se revisaron fichas clínicas de lactantes menores de 3 meses hospitalizados por fiebre sin foco evidente, registrando edad, sexo, días de fiebre preconsulta, temperatura y gravedad al ingreso, diagnósticos de egreso, exámenes de laboratorio y tratamientos. Según diagnóstico de egreso, se separaron en ITU (-) y (+), con o sin bacteriemia. RESULTADOS: Ingresaron 467 lactantes: 334 con ITU y 133 sin ITU. En ITU (+), la sensibilidad de la piuria fue de 95.8% y bacterias (+) 88.3%; la especificidad fue alta para nitritos (96.2%) y bacterias (+) (92.5%). El valor predictivo positivo (VPP) fue de 95.9% para nitritos, 96.7% para bacterias y 92.5% para piuria. Escherichia coli se encontró en el 83.8% de los urocultivos (UC) (+) y en el 87% de los hemocultivos (+). Las ITU con bacteriemia presentaron elementos inflamatorios, UC con ≥ 100,000 UFC/ml y mayor porcentaje de proteína C reactiva (PCR) > 50 mg/l (p= 0.002); el 94.6% de los UC (+) tuvo ≥ 50,000 UFC/ml. CONCLUSIONES: La piuria y bacterias (+) en el AO son excelentes para pronosticar ITU en orina obtenida con sonda vesical y el punto de corte para el diagnóstico debe ser ≥ 50,000 UFC/ml. No encontramos señales que ayudaran a sospechar ITU con bacteriemia.

Entrando en el manejo 2.0 de la cistinuria con una herramienta médica digital para monitorizar el pH de la orina: un estudio prospectivo y aleatorizado / Entering into 2.0 cystinuria management with a medical digital tool to monitor urine pH: A prospective, randomized study

Introducción y objetivos Las personas con cistinuria pueden experimentar eventos recurrentes de litiasis debido a la relativa insolubilidad de la cistina en el pH fisiológico de la orina, lo que resulta en deterioro de la función renal. El pHmetro Lit-Control® es un dispositivo médico que permite la automedición precisa del pH de la orina. El objetivo principal de este estudio fue comparar la usabilidad del pHmetro Lit-Control® con las tiras reactivas para la automonitorización domiciliaria del pH urinario por parte de pacientes con cistinuria, y su satisfacción general con cada herramienta.Pacientes y métodosSe incluyeron 28 pacientes (9 mujeres y 19 varones, de 19 a 76 años), que fueron asignados aleatoriamente a monitorizar su pH urinario con tiras reactivas (n=17) o el pHmetro Lit-Control® (n=11).ResultadosDespués de 6 meses de uso, la satisfacción con los 2 métodos fue similarmente alta, pero los pacientes calificaron (en una escala de 0 a 10) mejor el pHmetro en términos de facilidad de aprendizaje (media± DE, 8,11±0,60 vs. 7,06±1,18; p=0,038), facilidad de preparación (8,22±0,67 vs. 7,25±1,18; p=0,034) y facilidad de uso (8,22±0,67 vs. 7,25±1,39; p=0,062). En general, los pacientes no alcanzaron los objetivos de alcalinización (pH entre 7,0 y 8,0).ConclusionesEl pHmetro Lit-Control® demostró ser un dispositivo fácil de usar que puede facilitar el control del pH urinario en los pacientes con cistinuria. Queda justificado un estudio prospectivo para evaluar la correlación entre la monitorización del pH de la orina, una estrategia de tratamiento por objetivo y la recurrencia de los cálculos de cistina. (AU)

Background and objectives Individuals with cystinuria can experiment recurrent lithiasis events due to the relative insolubility of cystine at physiological urine pH, resulting in renal function decline. The Lit-Control® pH Meter is a medical device that accurately allows urine pH self-monitoring. The main objective of this study was to compare the usability of the Lit-Control® pH Meter with the reactive strips for self-monitoring of urinary pH at home by patients with cystinuria, and their overall satisfaction with each tool.Patients and methodsWe included 28 patients (9 females and 19 males, age 19-6 years), who were randomly assigned to monitor their urine pH with reactive strips (n=17) or the Lit-Control® pH Meter (n=11).ResultsAfter six months of use, the satisfaction with the two methods was similarly high, but the patients rated (0-10 scale) the pH meter better in terms of ease of learning (mean±SD, 8.11±0.60 vs. 7.06±1.18; P=.038), ease to prepare (8.22±0.67 vs. 7.25±1.18; P=0.034), and ease of use (8.22±0.67 vs. 7.25±1.39; P=.062). Overall, patients did not reach the alkalinization goals (pH between 7.0 and 8.0).ConclusionsThe Lit-Control® pH Meter demonstrated to be an easy-to-use device that can facilitate urinary pH control by cystinuric patients. A prospective study is warranted to assess the correlation between urine pH monitoring, a treat to target approach, and the recurrence of cystine stones. (AU)

Analysis of factors with low positive predictive value in the diagnosis of urinary tract infection by flow cytometry.

PURPOSE: Culture-negative urine specimens can be rapidly screened by urine flow cytometry (UFC), while low positive predictive value (PPV) limits the clinical application. We explored the factors associated with a low PPV. METHODS: A total of 5095 urine specimens were analyzed with UFC and culture. Diagnostic performance of leukocytes, bacteria, and BACT-info flags was evaluated by sensitivity, specificity, PPV, and negative predictive value (NPV). The association of contaminated culture and squamous epithelial cell count and BACT-info flag was performed by logistic regression analysis. RESULTS: The NPVs of parallel combination of bacteria and leucocytes were 98.9% in males and 97.9% in females, and PPVs of serial combination were 86.6% and 77.8% in men and women, respectively. The PPV of Gram-negative flag was higher than that of Gram-positive flag. The proportions of contamination in the urine culture results of false positive specimens were 86.9% in males and 98.5% in females at the cutoff points of the serial combination, and these parameters were 53.2% in males and 85.6% in females for the Gram-positive flag. There was a statistically significant association between contaminated cultures and squamous epithelial cells count in females, but not in males. Associations between contaminated cultures and Gram-positive flags or Gram-pos/-neg flags were statistically significant, but there was no association between contaminated cultures and Gram-negative flags. CONCLUSIONS: A serial combination of leukocytes and bacteria may maximize PPV in the diagnosis of bacterial urinary tract infection by urine flow cytometry, and contamination is the main reason for a low PPV.

Customized cutoff limits for the sediMAX-2 automated analyzer reduce the number of urine culture tests.

BACKGROUND: Urinary tract infections are highly prevalent in nosocomial and community settings. Their diagnosis, although costly and time-consuming, is crucial to avoid inappropriate treatments and/or clinical complications. In this context, automated analyzers have been developed and commercialized to screen and rule out negative urine samples. Adjustments of the manufacturers' suggested cutoff values might lead to substantial diagnostic and economic advantages. METHODS: We retrospectively analyzed 776 urine samples from different individuals. 546 samples (training group) were used to optimize develop new cutoffs values. The remaining 230 samples (validation group) were used to validate the optimized cutoffs. All samples were subjected to urine culture, 17% resulted positive. Escherichia coli and Enterococcus faecalis were the two most frequently identified bacteria, 95 and 9 samples, respectively. RESULTS: Two different cutoffs levels were obtained. Cutoff-A (bacteria>110 and/or white blood cells> 15 cell/µL), showed the same sensitivity of the manufacturers' suggested cutoff, yet leads to a large reduction of the samples to be cultured. Cutoff-B (bacteria>50 and/or white blood cells>20 cell/µL), showed an almost 100% sensitivity by subjecting only ~70% of the samples to urine culture. CONCLUSION: Cutoff-A is a good compromise between sensitivity and specificity yet allowing economic advantages by reducing the number of urinary cultures. Cutoff-B relegates urinary tract infection misdiagnosis to a rare event without the need of culturing the entire batch of samples. We believe that clinical implementation of the proposed cutoffs will help other laboratories, using similar instrumentation, to reach their most convenient balance between sensitivity and economical needs.

Prediction of clinically significant prostate cancer through urine metabolomic signatures: A large-scale validated study.

PURPOSE: Currently, there are no accurate markers for predicting potentially lethal prostate cancer (PC) before biopsy. This study aimed to develop urine tests to predict clinically significant PC (sPC) in men at risk. METHODS: Urine samples from 928 men, namely, 660 PC patients and 268 benign subjects, were analyzed by gas chromatography/quadrupole time-of-flight mass spectrophotometry (GC/Q-TOF MS) metabolomic profiling to construct four predictive models. Model I discriminated between PC and benign cases. Models II, III, and GS, respectively, predicted sPC in those classified as having favorable intermediate risk or higher, unfavorable intermediate risk or higher (according to the National Comprehensive Cancer Network risk groupings), and a Gleason sum (GS) of ≥ 7. Multivariable logistic regression was used to evaluate the area under the receiver operating characteristic curves (AUC). RESULTS: In Models I, II, III, and GS, the best AUCs (0.94, 0.85, 0.82, and 0.80, respectively; training cohort, N = 603) involved 26, 24, 26, and 22 metabolites, respectively. The addition of five clinical risk factors (serum prostate-specific antigen, patient age, previous negative biopsy, digital rectal examination, and family history) significantly improved the AUCs of the models (0.95, 0.92, 0.92, and 0.87, respectively). At 90% sensitivity, 48%, 47%, 50%, and 36% of unnecessary biopsies could be avoided. These models were successfully validated against an independent validation cohort (N = 325). Decision curve analysis showed a significant clinical net benefit with each combined model at low threshold probabilities. Models II and III were more robust and clinically relevant than Model GS. CONCLUSION: This urine test, which combines urine metabolic markers and clinical factors, may be used to predict sPC and thereby inform the necessity of biopsy in men with an elevated PC risk.

Application of analytical performance specifications for urine test strip methods: Importance of reflectance signals.

INTRODUCTION: Urinalysis is essential for diagnosing kidney-related medical conditions. Urine test strip analysis serves as an initial and efficient screening method for reflex testing with accurate quantitative methods. MATERIALS AND METHODS: Freshly voided urines (n = 206) were analysed using two urine test strip brands on UC-MAX (Menarini) and cobas u 601 (Roche Diagnostics) instruments. Ordinal scale categories and reflectance signals (if available) were both used for the comparison with reference quantitative methods for glucose, proteins and albumin (cobas 503). Samples were considered positive when glucose > 15 or ≥ 54 mg/dL, proteins ≥ 200 mg/L and albumin ≥ 10 mg/L. Optimized reflectance thresholds were calculated by ROC curve analysis. Analytical performance specifications (APS) for trueness of test strip were gathered from the EFLM guideline (FPD, FNG, FNC). RESULTS: Reflectance signals were significantly lower in urine samples considered positive by the reference method (p < 0.0001). Reflectance signals were also correlated with quantitative measurements, showing strong correlation (0.754 to 0.969). Only the use of optimized reflectance thresholds on cobas u 601 achieved at least the minimum EFLM APS (FPD < 20%, FNG < 50% and FNC < 10%). CONCLUSION: The use of reflectance signals from urine test strips enhanced accuracy for glucose, proteins, and albumin measurement and may contribute to improve diagnosis of diverse kidney-related conditions.

A fully interpretable machine learning model for increasing the effectiveness of urine screening.

OBJECTIVES: This article addresses the need for effective screening methods to identify negative urine samples before urine culture, reducing the workload, cost, and release time of results in the microbiology laboratory. We try to overcome the limitations of current solutions, which are either too simple, limiting effectiveness (1 or 2 parameters), or too complex, limiting interpretation, trust, and real-world implementation ("black box" machine learning models). METHODS: The study analyzed 15,312 samples from 10,534 patients with clinical features and the Sysmex Uf-1000i automated analyzer data. Decision tree (DT) models with or without lookahead strategy were used, as they offer a transparent set of logical rules that can be easily understood by medical professionals and implemented into automated analyzers. RESULTS: The best model achieved a sensitivity of 94.5% and classified negative samples based on age, bacteria, mucus, and 2 scattering parameters. The model reduced the workload by an additional 16% compared to the current procedure in the laboratory, with an estimated financial impact of €40,000/y considering 15,000 samples/y. Identified logical rules have a scientific rationale matched to existing knowledge in the literature. CONCLUSIONS: Overall, this study provides an effective and interpretable screening method for urine culture in microbiology laboratories, using data from the Sysmex UF-1000i automated analyzer. Unlike other machine learning models, our model is interpretable, generating trust and enabling real-world implementation.