1Department of Laboratory Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
2Department of Laboratory Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
3Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul, Korea
4Department of Laboratory Medicine, Gyeongsang National University Hospital, Gyeongsang National University College of Medicine, Jinju, Korea
5Department of Laboratory Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
*Corresponding author: E-mail: deyong@yuhs.ac
ABSTRACT
Background: Although urine culture is considered a reference standard for the diagnosis of urinary tract infection (UTI), it is time-consuming, labor-intensive, and expensive. Here, we evaluated the performance of five recent automated urine analyzers for UTI diagnosis.
Methods: For the 510 specimens analyzed, the criterion for ‘significant bacteriuria’ was defined as ≥ 104 CFU/mL in the inoculated plate for all specimens or ≥ 103 CFU/mL for specimens from patients using a Foley catheter or with urinary symptoms. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of UTI were analyzed using indicators individually, in different combinations, or with various cut-off values.
Results: Seventy-one specimens (13.9%) exhibited ‘significant bacteriuria’. In the receiver operating characteristics curve analysis, UF-5000 (Sysmex Corp., Japan) showed the highest area under the curve values for both males and females (0.876 and 0.846, respectively). The PPVs for specimens from males with all indicators positive increased up to 100% after adjusting the cut-off values. NPVs for specimens with all indicators negative were 94.3%–98.2% in males and 78.1%–93.8% in females after adjusting the cut-off values.
Conclusion: As a rapid and accurate diagnostic tool, urine sediment analyzers can be valuable for UTI diagnosis by reducing unnecessary culture and can help clinicians determine a treatment plan.