Multivariable Urine Flow Cytometry–Based Screening for Prediction of Urine Culture Positivity

Background/Objectives: Urine samples are the most frequently analyzed specimens in clinical microbiology laboratories. Although urine culture remains the gold standard for diagnosing urinary tract infections, it is time-consuming and resource-intensive. Therefore, reliable screening methods capable of predicting urine culture positivity are needed to optimize laboratory workflow. Automated urine analysis based on flow cytometry enables efficient screening and identification of samples with a low probability of bacterial infection, thereby rationalizing microbiological testing. This study evaluated the usefulness of a multivariable approach to support interpretation of flow cytometry results following the implementation of the Sysmex UF-4000 urine flow cytometer. Methods: Routinely collected urine samples from outpatients and hospitalized patients were analyzed using the UF-4000 flow cytometer, with a positivity threshold of ≥100 leukocytes/µL. Urinary parameters were compared between samples with positive and negative cultures. Multivariable logistic regression was applied to identify independent predictors of a positive urine culture. Urinary sediment parameters, including leukocyte, bacterial, fungal, and squamous epithelial cell counts, were assessed as covariates. Results: Urine samples with positive cultures showed significantly higher leukocyte counts (median 355.0, IQR 146.5–1429.4) and bacterial counts (median 9805.2, IQR 1134.3–45,011.5). Fungal and squamous epithelial cell counts differed only slightly between groups, although the differences were statistically significant (p < 0.001). Leukocyte counts were higher in urine samples from which Gram-negative bacteria were isolated compared with samples containing Gram-positive bacterial isolates (p < 0.001). The multivariable model demonstrated the most favorable overall performance, combining high sensitivity with improved specificity and the highest negative predictive value (AUC = 0.927). Optimal cut-off values were 70 leukocytes/µL and 105 bacteria/µL. Conclusions: Leukocyte and bacterial counts were the strongest predictors of positive urine culture results. A multivariable model including only these two parameters demonstrated high diagnostic accuracy and may serve as a practical screening tool to identify urine samples with a low probability of bacterial infection. The implementation of this approach could support more efficient use of urine cultures and help optimize laboratory workflow.