ArticleDiagnostics (Basel, Switzerland)2022
Urine Flow Cytometry Parameter Cannot Safely Predict Contamination of Urine-A Cohort Study of a Swiss Emergency Department Using Machine Learning Techniques.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Correlation of Age and Laboratory Parameters with Urine Flow Cytometry and Culture Results in Patients with Urinary Tract Infections.Infectious disease reports · 2026Article
- Diagnostic Accuracy of Urine Flow Cytometry (UF) in Urinary Tract Infection (UTI) Detection and Management: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Multivariable Urine Flow Cytometry-Based Screening for Prediction of Urine Culture Positivity.Diagnostics (Basel, Switzerland) · 2026Article
- Unusual case ofFrontiers in cellular and infection microbiology · 2026Review
- Quality indicators for urine sample contamination: can squamous epithelial cells and bacteria count be used to identify properly collected samples?Clinical chemistry and laboratory medicine · 2025Observational
- Advancing Laboratory Medicine Practice With Machine Learning: Swift yet Exact.Annals of laboratory medicine · 2025Review
- Using the Sysmex UF-4000 urine flow cytometer for rapid diagnosis of urinary tract infection in the clinical microbiological laboratory.Journal of clinical laboratory analysis · 2024Article
- Urine Flow Cytometry and Dipstick Analysis in Diagnosing Bacteriuria and Urinary Tract Infections among Adults in the Emergency Department-A Diagnostic Accuracy Trial.Diagnostics (Basel, Switzerland) · 2024Article
- Analysis of factors with low positive predictive value in the diagnosis of urinary tract infection by flow cytometry.World journal of urology · 2023Article
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7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUrine 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.
methodsA 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.
resultsIn 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
conclusionsSignificant 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.
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