Detection of significant bacteriuria by use of the iQ200 automated urine microscope

Evaluation of the DxU 850m Iris automated urine microscopy analyzer for identifying culture-negative urine samples: From a perspective of reducing urine culture number

We evaluated the DxU 850m Iris Urine Microscopy analyzer as a screening tool for excluding negative urine samples (n = 1337). At a cutoff of 103 colony counts·mL-1, sensitivity was 55.1 %, specificity 68.6 %. The DxU 850m Iris does not offer acceptable prediction of culture-negative urine samples at the tested cutoff.

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.

Applications of Artificial Intelligence in Urinalysis: Is the Future Already Here?

BACKGROUND

Artificial intelligence (AI) has emerged as a promising and transformative tool in the field of urinalysis, offering substantial potential for advancements in disease diagnosis and the development of predictive models for monitoring medical treatment responses.

CONTENT

Through an extensive examination of relevant literature, this narrative review illustrates the significance and applicability of AI models across the diverse application area of urinalysis. It encompasses automated urine test strip and sediment analysis, urinary tract infection screening, and the interpretation of complex biochemical signatures in urine, including the utilization of cutting-edge techniques such as mass spectrometry and molecular-based profiles.

SUMMARY

Retrospective studies consistently demonstrate good performance of AI models in urinalysis, showcasing their potential to revolutionize clinical practice. However, to comprehensively evaluate the real clinical value and efficacy of AI models, large-scale prospective studies are essential. Such studies hold the potential to enhance diagnostic accuracy, improve patient outcomes, and optimize medical treatment strategies. By bridging the gap between research and clinical implementation, AI can reshape the landscape of urinalysis, paving the way for more personalized and effective patient care.

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.

A novel approach to screening and managing the urinary tract infections suspected sample in the general human population

Background

Automated urine technology providing standard urinalysis data can be used to support clinicians in screening and managing a UTI-suspected sample. Fully automated urinalysis systems have expanded in laboratory practice. Commonly used were devices based on digital imaging with automatic particle recognition, which expresses urinary sediment results on an ordinal scale. There were introduced fluorescent flow cytometry analyzers reporting all parameters quantitatively. There is a need to harmonize the result and support comparing bacteria and WBC qualitative versus semiquantitative results.

Methods

A total of 1,131 urine samples were analyzed on both automated urinalysis systems. The chemical components of urinalysis (leukocyte esterase and nitrate reductase) and the sediment results (leukocytes and bacteria) were investigated as potential UTI indicators. Additionally, 106 specimens were analyzed on UF-5000 and compared with culture plating to establish cut-offs that can be suitable for standard urinalysis requirements and help to guide on how to interpret urinalysis results in the context of cultivation reflex.

Results

The medians of bacteria counts varies from 16.2 (absence), 43.0 (trace), 443.5 (few), 5,389.2 (moderate), 19,356.6 (many) to 32,545.2 (massive) for particular digital microscopic bacteriuria thresholds. For pyuria thresholds, the medians of WBC counts varies from 0.8 (absence), 2.0 (0-1), 7.7 (2-3), 21.3 (4-6), 38.9 (7-10), 61.3 (11-15) to 242.2 (>30). Comparing the culture and FFC data (bacterial and/or WBC counts) was performed. Satisfactory sensitivity (100%), specificity (83.7%), negative predictive value (100%), and positive predictive value (75%) were obtained using indicators with the following cut-off values: leukocytes ≥40/µl or bacteria ≥300/µl.

Conclusions

Accurate urinalysis gives information about the count of bacteria and leukocytes as useful indicators in UTIs, in general practice it can be a future tool to cross-link clinical and microbiology laboratories. However, the cut-off adjustments require individual optimization.

UriSed 3 PRO automated microscope in screening bacteriuria at region-wide laboratory organization

BACKGROUND AND AIMS

We assessed the possibility to rule out negative urine cultures by counting with UriSed 3 PRO (77 Elektronika, Hungary) at Helsinki and Uusimaa Hospital District.

MATERIALS AND METHODS

Bacteria counting of the UriSed 3 PRO automated microscope was verified with reference phase contrast microscopy against growth in culture. After acceptance into routine, results of bacteria and leukocyte counting from 56 426 specimens with eight UriSed 3 PRO instruments were compared against results from parallel samples cultured on chromogenic agar. Laboratory data including preanalytical details were accessed through the regional database of the Helsinki and Uusimaa Hospital District.

RESULTS

A combined sensitivity of 87-92% and a negative predictive value of 90-96% with a specificity of 54-50% was reached, depending on criteria. Preanalytical data (incubation time in bladder) combined with the way of urine collection would improve these figures if reliable.

CONCLUSIONS

Complex patient populations, regional logistics and data interfases, and economics related to increased costs of additional particle counts against costs of screening cultures of all samples, did not support adaptation of a screening process of urine cultures. This conclusion was made locally, and may not be valid elsewhere.

Evaluation of urinary inflammatory index in rapid screening of urinary tract infection

The objective of this study was to assess the diagnosis value of urinary inflammatory index (UII) and systemic immune-inflammation index (SII) for UTI. Nine inflammatory indexes including neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, SII and six UIIs were calculated for Receiver operating characteristic curve analysis to select which one is suitable for the screening of UTIs or distinguishing the types of bacteria. UII3, which calculated from leucocyte esterase (LE), nitrite, white blood cells and bacteria, was preferentially used as an indicator for the diagnosis of UTI when the threshold was set at 0.53. UII2 was more suitable for the distinction between groups when the cutoff is set to 0.94. Appropriate urinary inflammation index calculated by rapid urinalysis of urine dipstick and urine sediment can help us to predict urinary tract infection and bacterial type, and reduce the workload and costs of urine culture.

Automated urinalysis combining physicochemical analysis, on-board centrifugation, and digital imaging in one system: A multicenter performance evaluation of the cobas 6500 urine work area

BACKGROUND

We evaluated the analytical performance of the fully automated cobas® 6500 urine work area and its automated components-cobas u 601 and cobas u 701.

DESIGN AND METHODS

The study was conducted at three European centers using un-centrifuged surplus routine urine samples; all measurements were performed within 2 h of sample collection. Precision, sample carry-over, and method comparisons were evaluated per Clinical and Laboratory Standards Institute guidelines. Method comparisons: cobas u 601 versus Urisys 2400 and cobas u 411 urine test strips; and cobas u 701 versus KOVA® visual microscopy and iQ200 analyzer. Operability and functionality were assessed using questionnaires.

RESULTS

Precision of the entire cobas 6500 system was within predefined acceptance limits and no significant carry-over was observed. Erythrocytes, leukocytes, nitrites, and protein were in good agreement (≥93%) with cobas u 411 reflectometry. High correlation was shown between the cobas u 701 analyzer and KOVA visual microscopy for red blood cells (RBC; slope, 0.89; Pearson's r, 0.95) and white blood cells (WBC; slope, 0.96; Pearson's r, 0.96), demonstrating equivalence of test results. The 97.5% percentile reference values on the cobas u 701 analyzer were 5.3 cells/μL (RBC) and 6.2 cells/μL (WBC). The cobas 6500 system showed good sensitivity for small bacteria (>1 μm) and pathological casts, and the user interface, maintenance wizards, and system design were highly rated by operators.

CONCLUSIONS

The fully automated workflow, high precision, and high throughput of the cobas 6500 system have the potential to facilitate standardization of urine screening.

Performance of the cobas u 701 Analyzer in Urinary Tract Infection Screening

Background

Negative urine cultures to rule out urinary tract infections (UTI) generate a considerable laboratory workload; thus, a rapid screening test is desirable. We evaluated the performance of a new automated microscopy analyzer, cobas u 701 (Roche Diagnostics International, Rotkreuz, Switzerland) for the screening of UTI, and developed a rule-out strategy to reduce the number of samples requiring culture. We also assessed squamous epithelial cell (SEC) count as a predictor of culture contamination.

Methods

In total, 1,604 urine samples from outpatients were analyzed with cobas u 701 and culture. Bacterial (BAC) and white blood cell (WBC) counts were used for sample interpretation. To determine a useful cut-off point to predict negative cultures, we selected the highest sensitivity and specificity values obtained from ROC curves. Diagnostic accuracy by age and gender was evaluated.

Results

Urine culture showed growth of ≥10⁴ colony forming units (CFU)/mL in 256 samples (16.0%). The highest sensitivity (91.8%) and specificity (68.4%) were obtained for cut-off points of 119 BAC/µL and 22 WBC/µL. The combination of BAC and WBC improved the performance of the rule-out strategy with a low rate of false-negative results (1.5%) and a high negative predictive value (NPV, 97.3%). Fifty-seven percent of the samples would not have required culture. SEC count was a poor predictor of culture contamination.

Conclusions

cobas u 701 can substantially reduce the number of urine samples requiring culture, with a low false-negative rate and a high NPV.

Clinical usefulness of iQ200/iChem Velocity workstation for screening of urine culture

BACKGROUND

Clinical microbiology laboratories are asked to process large numbers of urine specimens for culture, but only 20-40% of them are positive. Therefore, a rapid, reliable screening method is necessary to speed up the reporting of a negative result. In this study, we evaluated the iQ200/iChem workstation, which is a combination of digital imaging software and a strip reader to predict negative urine culture.

METHOD

A total of 1942 urine specimens were processed through both culture and iQ200/ iChem workstation. We analyzed the performance using two definition of positive urine culture; one or two potential uropathogens at a concentration of ≥10⁵ CFU/ml and ≥ 10⁴ CFU/ml. We assessed combinations of parameters (ASP; all small particles, WBC; leukocyte, BACT; bcteria, LE; leukocyte esterase) applying various cut-offs which can achieve the negative predictive value (NPV) ≥97% and culture reduction rate ≥ 50%.

RESULTS

The culture positive rate was 12.8 and 18.4% applying the criteria of ≥10⁵ CFU/ml and ≥ 10⁴ CFU/ml, respectively. The area under the curve (AUC) of each parameter for ≥10⁵ CFU/ml / ≥10⁴ CFU/ml bacteriuria was 795 /0.719 for WBC, 0.722 / 0.701 for ASP and 0.740 /0.704 for bacteria. Therefore, we investigated the combination of the parameters. With the fixed parameter of BACT≥1/HPF and positive LE, the combinations of WBC ≥ 4/HPF and ASP ≥8500/μl or WBC ≥ 6/HPF and ASP≥5500/μl showed good performance for detecting ≥10⁵ CFU/ml uropathogen. The ranges of sensitivity, specificity, negative predictive value and culture reduction rate were 91.5-92.3%, 49.8-52.6%, 97.7-97.9% and 50.4-53.0%, respectively. However, none of the combined setting yielded acceptable range of NPV for detecting ≥10⁴ CFU/ml uropathogen (NPV 92.9-94.9%). Enterococcus spp. was the most common uropathogen causing the false negative results (55.7%), and also the main pathogen among the positive culture of 10^4-5 CFU/ml bacteriuria (45%).

CONCLUSIONS

iQ200/iChem workstation was excellent in detection of ≥10⁵ CFU/ml uropathogen, but unsatisfactory in detection of 10^4-5 CFU/ml uropathogen and Enterococcus spp. It can be useful for screening of urine specimens to reduce bacterial culture. However, notice from clinician will be necessary for specimens from the patients with high risk for UTI, such as pregnant woman, infant, elderly or immune compromised patients.

Risk factors analysis for hyperuricemic nephropathy among CKD stages 3-4 patients: an epidemiological study of hyperuricemia in CKD stages 3-4 patients in Ningbo, China

Objective: Uric acid (UA) is a risk marker of CKD and SUA level in CKD 3–4 patients closely correlates with hyperuricemic nephropathy (HN) morbidity. This study was designed to evaluate the risk factors for HN in CKD 3–4 patients.

Methods: The 461 CKD 3–4 patients were recruited and all patients were divided into three groups (24 h UUA normal, underexeret, and overproduct type groups) according to the 24 h UUA level after receiving low purine food for five days. Clinical and biochemical characteristics of CKD patients were collected for the logistic regression analysis. Correlation analysis of the mRNA relative expression level of hUAT and hURAT1 with serum UA (SUA) level also was evaluated.

Results: There were significant increases in characteristics including average age, waist-to-height ratio (WHR), SUA levels, HN ratio, TG/HDL ratio, body mass index (BMI), blood pressure (BP), uNgal/Cr. ratio, and uKim-1/Cr. ratio in overproduct type group in comparison with the other two groups. Logistic regression analysis showed SUA, CHO, uKim-1/Cr. ratio and uNgal/Cr. ratio were independent and multiple risk factors for HN. Moreover, hUAT and hURAT1 mRNA relative expression levels were significantly correlated with SUA level in the underexeret type CKD 3–4 patients.

Conclusions: These results showed SUA and other characteristics contributed to HN morbidity in CKD 3–4 patients.

Progress in Automated Urinalysis

New technological advances have paved the way for significant progress in automated urinalysis. Quantitative reading of urinary test strips using reflectometry has become possible, while complementary metal oxide semiconductor (CMOS) technology has enhanced analytical sensitivity and shown promise in microalbuminuria testing. Microscopy-based urine particle analysis has greatly progressed over the past decades, enabling high throughput in clinical laboratories. Urinary flow cytometry is an alternative for automated microscopy, and more thorough analysis of flow cytometric data has enabled rapid differentiation of urinary microorganisms. Integration of dilution parameters (e.g., creatinine, specific gravity, and conductivity) in urine test strip readers and urine particle flow cytometers enables correction for urinary dilution, which improves result interpretation. Automated urinalysis can be used for urinary tract screening and for diagnosing and monitoring a broad variety of nephrological and urological conditions; newer applications show promising results for early detection of urothelial cancer. Concomitantly, the introduction of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) has enabled fast identification of urinary pathogens. Automation and workflow simplification have led to mechanical integration of test strip readers and particle analysis in urinalysis. As the information obtained by urinalysis is complex, the introduction of expert systems may further reduce analytical errors and improve the quality of sediment and test strip analysis. With the introduction of laboratory-on-a-chip approaches and the use of microfluidics, new affordable applications for quantitative urinalysis and readout on cell phones may become available. In this review, we present the main recent developments in automated urinalysis and future perspectives.

Use of Automated Urine Microscopy Analysis in Clinical Diagnosis of Urinary Tract Infection: Defining an Optimal Diagnostic Score in an Academic Medical Center Population

A retrospective case record study was conducted that established a scoring tool based on clinical and iQ200 parameters, able to predict or rule out the clinical diagnosis of UTI in the majority of adult patients in an academic hospital. Automated standardized quantitative urine analysis, such as iQ200 analysis, is on the rise because of its high accuracy and efficiency compared to those of traditional urine analysis. Previous research on automated urinalysis focused mainly on predicting culture results but not on the clinical diagnosis of urinary tract infection (UTI). A retrospective analysis was conducted of consecutive urine samples sent in for culture because of suspected UTI. UTI was defined by expert opinion, based on reported symptoms, conventional urine sediment analysis, and urine cultures. Parameters of iQ200 analysis and clinical symptoms and signs were compared between cases and controls. Optimal cutoff values were determined for iQ200 parameters, and multivariate logistic regression analysis was used to identify the set of variables that best predicts the clinical diagnosis of UTI for development of a scoring tool. A total of 382 patients were included. Optimal cutoff values of iQ200 analysis were 74 white blood cells (WBC)/μl, 6,250 "all small particles" (ASP)/μl, and a bacterial score of 2 on an ordinal scale of 0 to 5. The scoring tool attributed 1 point for frequent micturition or increased urge, 2 points for dysuria, 1 point for a bacterial score of ≥2, 2 points for WBC/μl of ≥50, and an additional point for WBC/μl of ≥150. This score had a sensitivity of 86% and a specificity of 92% when using a threshold of <4 points. The combination of iQ200 analysis and a simple survey could predict or rule out UTIs in a majority of patients in an academic medical center.

Evaluation of the new Sysmex UF-5000 fluorescence flow cytometry analyser for ruling out bacterial urinary tract infection and for prediction of Gram negative bacteria in urine cultures

BACKGROUND

We evaluated the new flow cytometer UF-5000 with a blue semiconductant laser as a screening tool for ruling out urine samples negative for UTI and its ability to predict Gram negatives in culture.

METHODS

Flow cytometry and microbiological analysis were performed on 2719 urine samples, sent to our microbiology laboratory with a request for urine culture.

RESULTS

UF-5000 showed a very good performance in the screening process. Carryover and cross-contamination was negligible. 797 samples were culture positive at a cut-off of ≥10⁵CFU/mL. ROC curve analysis for BACT count demonstrated AUC between 0.973, on 2714 samples, 0.959, on 1516 female samples, and 0.988 on 1198 male samples, respectively. At the cut-off of BACT ≥58/μL AND/OR YLC ≥150/μL, SE was 99.4%, SP 78.2%, PPV 65.4% and NPV 99.7%; false negatives were 0.6%, avoiding unnecessary cultures in 55.5% of specimens. "Gram Neg?" flag predicted Gram negatives in culture with a SE of 81.6% and SP of 93.3%.

CONCLUSION

The new Sysmex UF-5000 showed high diagnostic accuracy in UTI-screening with a very low rate of false negatives. The instrument is capable of predicting Gram negatives with a good SE and a high agreement with the culture, even if this performance needs further evaluation.

[URISCAM project: Multicenter evaluation of the UF-Series cytometer in the urinary tract infections screening]

OBJECTIVE

Urine culture, the gold standard to confirm the presence of urinary tract infection (UTI), is the most requested assay in the microbiology department. Our objective was to determine the diagnostic yield of the UF-Series cytometer as a screening method for UTI.

METHODS

All the urine samples sent to the six Microbiology Laboratories participating in a period of 5 working days were analyzed. We collected demographic variables, apart from those variables related to urine samples: source and sample type (midstream, catheterized or nephrostomy urines), collection with/without boric acid, cytometer parameters (leukocyturia, bacteriuria, bacteria morphology and epithelial cells) and urine culture results. ROC curves were plotted to determine predictive capacity of the cytometer.

RESULTS

A sample of 2,468 patients with average age of 53 years were processed (ratio women:men 2:1). Urine culture detected 23% of positive urine samples. The predictor variables of UTI were: morphology of bacilli, bacteriuria ≥21 bacteria/µL, age ≥65 years, samples collected in the emergency service and hospitalization and preserving conditions. With 21 bacteria/µL as a cut-off point, we obtained a sensitivity of 93.3% and 94.5% negative predictive value, then reducing the samples to be cultured by 28.9% with 1.6% false negatives.

CONCLUSIONS

We consider that the UF-Series is a valid and accurate tool for the detection of UTI. Therefore, it could be used as screening method in the clinical practice prior to the urine culture, reducing culture requirement by approximately 30%, with a low false negative rate.

Sysmex UF-1000i flow cytometer to screen urinary tract infections: the URISCAM multicentre study

The new Sysmex UF-1000i analyzer - which incorporates bacteria morphology distinction - allows to automatically screen samples to be cultured at microbiology laboratories. We have evaluated the feasibility and accuracy of Sysmex UF-1000i to screen urinary tract infections (UTIs). A total amount of 2468 urine samples from six Spanish hospitals were analysed. Demographic and clinical data such as age, gender, source and sample type, preserving conditions, cytometer parameters (bacteria, leucocytes and bacteria morphology) as well as urine culture results (gold standard) were recorded. After applying data mining techniques, the variables of age, bacteria count and rod morphology were defined as predictive variables of UTIs. By using the UF-1000i in combination with a predictive algorithm of three decision rules, we could identify 94·9 and 47·4% positive and negative urine samples, respectively, with a negative predictive value of 97 and only 1·17% diagnostic error. This error was reduced down to 0·4% when contaminated samples were excluded. Our results show that flow cytometry parameters together with age, by means of a predictive algorithm model, can be used to screen UTIs. Its implementation would avoid culturing 38% of urine samples, and therefore, would reduce time to diagnosis with a discrete false negative ratio.

SIGNIFICANCE AND IMPACT OF THE STUDY

Fluorescent flow cytometry performance has recently spread for urine screening. However, controversy about cytometer results can be drawn from medical literature. This study shows the diagnosis accuracy of Sysmex UF-1000i analyzer by means of a group of decision rules encompassing both demographic variables (age) and cytometer parameters (bacteria, leucocytes and bacteria morphology). After applying the predictive algorithm, the UF-1000i could optimally identify 95% urinary tract infections with high negative predictive value and low diagnostic error. Implementation of UF-1000i would avoid culturing almost 38% of urine samples, thus reducing time to diagnosis, unnecessary antibiotic treatments and consequently improving cost-effectiveness.

Validation and verification of automated urine particle analysers

There is often uncertainty on how validation and verification of newly introduced tests should be conducted, and there is a real risk of verification becoming a meaningless ritual, rather than a useful exercise. This article reviews the literature and makes recommendations regarding the validation and verification of automated urine particles analysers. A generic practical approach to verification is also recommended. For many analysers, the accuracy of white blood cells, epithelial cells and bacterial counts is corroborated by a number of independent evaluations; thus, any verification laboratory work could be significantly scaled down. Conversely, in the scenario that automated urine microscopy is used as a screening test to reduce the number of urines cultured, the extremely variable performance reported in the literature requires a full-scale verification to define the optimal cut-off values that give a sensitivity of >98% with the local settings and circumstances. With some analysers, the risk of carry-over also needs to be assessed, as part of the verification process, and exclusion criteria (urines requiring culture regardless of the microscopy results) need to be well defined, as there are patients or specimen types for which the performance of microscopy as a screening test may not be adequate.

Diagnosis of Urinary Tract Infections in Children

Urinary tract infections (UTIs) are a common occurrence in children. The management and laboratory diagnosis of these infections pose unique challenges that are not encountered in adults. Important factors, such as specimen collection, urinalysis interpretation, culture thresholds, and antimicrobial susceptibility testing, require special consideration in children and will be discussed in detail in the following review.

Interlaboratory Collaboration for Optimized Screening for Urinary Tract Infection

As the majority of urine samples submitted for culture yields a negative result, rapid screening that accurately predicts culture outcome benefits clinicians by reducing the time to result and improves the efficiency of the microbiological laboratory. Automated urinalysis using the IRIS Diagnostics iQ200 Elite (iQ200) analyzer permits just such a fast and large-scale screening. We aimed to predict and thus to reduce negative cultures with a screening algorithm based on iQ200 urinalysis in a tertiary university hospital. In parallel, we evaluated the performance of the iQ200 screen compared to that of Gram stain for sample quality. We screened 1,442 samples submitted for bacterial culture using the iQ200 analyzer; of these samples, 357 (24.8%) had a positive culture result. We identified the absence of microorganisms in the iQ200 screen as the strongest solitary predictor for a negative culture, with a sensitivity of 90.5% (323/357). The algorithm was further improved by performing logistic regression on leukocyte counts, which gave a cutoff of 65 leukocytes/μl to obtain the desired sensitivity of >95% (95.2%; 95% confidence interval [CI], 92.5 to 97.0), a negative predictive value of 97.3% (95% CI, 95.7 to 98.3), and an anticipated culture workload reduction of 44% (95% CI, 41 to 46). Concordance between sample quality based on Gram stain and iQ200 screening was only 72%, which was probably a result of interobserver effect in evaluation of the Gram stain. In conclusion, in our setting, screening by iQ200 proved to be a safe and cost-effective means to provide faster culture results, and it has the added benefit of a more objective evaluation of sample quality.