Diagnosis of Urinary Tract Infections by Urine Flow Cytometry: Adjusted Cut-Off Values in Different Clinical Presentations

Comparison of Laboratory Diagnosis of Urinary Tract Infections Based on Leukocyte and Bacterial Parameters Using Standardized Microscopic and Flow Cytometry Methods

Background

Rapid and reliable tests are essential for the diagnostic laboratory confirmation of urinary tract infections (UTIs). Until now, UTI has been confirmed by the microbiology culture of urine, requiring at least 48-hour turnaround time (TAT), with a standardized microscopic method being widely favored. Automated urine flow cytometry, however, has recently been used to improve the rapid TAT by analyzing the urine sediment. This study therefore aimed to compare the diagnostic value of the Shih-Yung conventional microscopic and urine flow cytometry methods in the detection of leukocyte and bacterial parameters of patients with UTIs in an outpatient clinic.

Methods

A cross-sectional study was conducted on a total of 100 patients. Seventy urine samples were positive for leukocytes and nitrite chemistry, and 30 were negative for both. The measurements of urine leukocytes and bacteria were compared between Sysmex UF-5000 urine flow cytometry and the Shih-Yung method. The diagnostic value was obtained from ROC analysis of urine flow cytometry and the culture.

Results

A leukocyte cutoff value of 87.2/μL had a sensitivity and specificity of 98.33% and 95%, respectively, and 98.33% sensitivity and 75% specificity at a bacterial cutoff of 582.22/μL. Interestingly, our study identified strong and consistent agreement of leukocyte and bacterial parameters between urine flow cytometry and Shih-Yung (k = 0.959, p < 0.001 and k = 0.939, p < 0.001, respectively). Furthermore, through analyzing the dominance angle of the scattergram, a strong agreement was obtained with the culture result (k = 0.880, p < 0.001).

Conclusions

The Shih-Yung method showed consistent agreement with urine flow cytometry for the detection of leukocytes and bacteria. The use of certain cutoffs for bacterial and leukocyte parameters in urine flow cytometry demonstrated very good performance in detecting acquired symptomatic UTIs.

Urine Flow Cytometry Parameter Cannot Safely Predict Contamination of Urine—A Cohort Study of a Swiss Emergency Department Using Machine Learning Techniques

Background: Urine flow cytometry (UFC) analyses urine samples and determines parameter counts. We aimed to predict different types of urine culture growth, including mixed growth indicating urine culture contamination. Methods: A retrospective cohort study (07/2017–09/2020) was performed on pairs of urine samples and urine cultures obtained from adult emergency department patients. The dataset was split into a training (75%) and validation set (25%). Statistical analysis was performed using a machine learning approach with extreme gradient boosting to predict urine culture growth types (i.e., negative, positive, and mixed) using UFC parameters obtained by UF-4000, sex, and age. Results: In total, 3835 urine samples were included. Detection of squamous epithelial cells, bacteria, and leukocytes by UFC were associated with the different types of culture growth. We achieved a prediction accuracy of 80% in the three-class approach. Of the n = 126 mixed cultures in the validation set, 11.1% were correctly predicted; positive and negative cultures were correctly predicted in 74.0% and 96.3%. Conclusions: Significant bacterial growth can be safely ruled out using UFC parameters. However, positive urine culture growth (rule in) or even mixed culture growth (suggesting contamination) cannot be adequately predicted using UFC parameters alone. Squamous epithelial cells are associated with mixed culture growth.

The Direct Semi-Quantitative Detection of 18 Pathogens and Simultaneous Screening for Nine Resistance Genes in Clinical Urine Samples by a High-Throughput Multiplex Genetic Detection System

Background

Urinary tract infections (UTIs) are one the most common infections. The rapid and accurate identification of uropathogens, and the determination of antimicrobial susceptibility, are essential aspects of the management of UTIs. However, existing detection methods are associated with certain limitations. In this study, a new urinary tract infection high-throughput multiplex genetic detection system (UTI-HMGS) was developed for the semi-quantitative detection of 18 pathogens and the simultaneously screening of nine resistance genes directly from the clinical urine sample within 4 hours.

Methods

We designed and optimized a multiplex polymerase chain reaction (PCR) involving fluorescent dye-labeled specific primers to detect 18 pathogens and nine resistance genes. The specificity of the UTI-HMGS was tested using standard strains or plasmids for each gene target. The sensitivity of the UTI-HMGS assay was tested by the detection of serial tenfold dilutions of plasmids or simulated positive urine samples. We also collected clinical urine samples and used these to perform urine culture and antimicrobial susceptibility testing (AST). Finally, all urine samples were detected by UTI-HMGS and the results were compared with both urine culture and Sanger sequencing.

Results

UTI-HMGS showed high levels of sensitivity and specificity for the detection of uropathogens when compared with culture and sequencing. In addition, ten species of bacteria and three species of fungi were detected semi-quantitatively to allow accurate discrimination of significant bacteriuria and candiduria. The sensitivity of the UTI-HMGS for the all the target genes could reach 50 copies per reaction. In total, 531 urine samples were collected and analyzed by UTI-HMGS, which exhibited high levels of sensitivity and specificity for the detection of uropathogens and resistance genes when compared with Sanger sequencing. The results from UTI-HMGS showed that the detection rates of 15 pathogens were significantly higher (P<0.05) than that of the culture method. In addition, there were 41(7.72%, 41/531) urine samples were positive for difficult-to-culture pathogens, which were missed detected by routine culture method.

Conclusions

UTI-HMGS proved to be an efficient method for the direct semi-quantitative detection of 18 uropathogens and the simultaneously screening of nine antibiotic resistance genes in urine samples. The UTI-HMGS could represent an alternative method for the clinical detection and monitoring of antibiotic resistance.

Accuracy of urine flow cytometry and urine test strip in predicting relevant bacteriuria in different patient populations

Background

Urinary tract infection (UTI) is diagnosed combining urinary symptoms with demonstration of urine culture growth above a given threshold. Our aim was to compare the diagnostic accuracy of Urine Flow Cytometry (UFC) with urine test strip in predicting bacterial growth and in identifying contaminated urine samples, and to derive an algorithm to identify relevant bacterial growth for clinical use.

Methods

Species identification and colony-forming unit (CFU/ml) quantification from bacterial cultures were matched to corresponding cellular (leucocytes/epithelial cells) and bacteria counts per μl. Results comprise samples analysed between 2013 and 2015 for which urine culture (reference standard) and UFC and urine test strip data (index tests, Sysmex UX-2000) were available.

Results

47,572 urine samples of 26,256 patients were analysed. Bacteria counts used to predict bacterial growth of ≥10⁵ CFU/ml showed an accuracy with an area under the receiver operating characteristic curve of > 93% compared to 82% using leukocyte counts. The relevant bacteriuria rule-out cut-off of 50 bacteria/μl reached a negative predictive value of 98, 91 and 89% and the rule-in cut-off of 250 bacteria/μl identified relevant bacteriuria with an overall positive predictive value of 67, 72 and 73% for microbiologically defined bacteriuria thresholds of 10⁵, 10⁴ or 10³ CFU/ml, respectively. Measured epithelial cell counts by UFC could not identify contaminated urine.

Conclusions

Prediction of a relevant bacterial growth by bacteria counts was most accurate and was a better predictor than leucocyte counts independently of the source of the urine and the medical specialty ordering the test (medical, surgical or others).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-05893-3.

Emerging Antibiotic Resistance to Bacterial Isolates from Human Urinary Tract Infections in Grenada

A urinary tract infection (UTI) in humans is one of the most common ailments in developing countries. The treatment of UTI is becoming difficult because of the increasing drug resistance against the common bacteria associated with UTI. This research aimed to determine the bacteria, and their antimicrobial drug resistance, associated with UTI in the Grenada population. A retrospective study of data (2015 through 2017) from the microbiology laboratory of the Grenada General Hospital was analyzed. Bacteria were isolated from 1289 (33.3%) urine cultures of 3867 UTI suspected urine samples. Both Gram-positive (Staphylococcus aureus 5.0%; Enterococci group D 43.2%) and Gram-negative bacteria (Escherichia coli 51%; Klebsiella pneumoniae20.0%; Proteus mirabilis 10.0%; Acinetobacter spp. 20.0%) were isolated. Bacterial isolates were tested for their resistance to nine antibacterial drugs (ampicillin, gentamicin, norfloxacin, cefuroxime, ceftazidime, Bactrim, imipenem, augmentin, and ciprofloxacin). Gram-negative bacteria showed higher antimicrobial drug resistance.