Evaluation of the detection ability of uropathogen morphology and vaginal contamination by the Atellica UAS800 automated urine microscopy analyzer and its effectiveness

• Published in: Journal of clinical laboratory analysis Vol. 35; no. 3; pp. e23698 - n/a
• Main Authors: Nakamura, Akihiro, Shinke, Tetsuya, Noguchi, Nobuyoshi, Komatsu, Masaru, Yamanishi, Hachiro
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.03.2021; John Wiley and Sons Inc
• Subjects: automated urine microscopy analysis; Automation; Bacilli; Bacteria; Bacteriuria; Cocci; Contamination; E coli; Epithelial cells; Erythrocytes; Flow cytometry; Microscopy; Morphology; multivariable logistic regression analysis; Predictions; Pyuria; Regression analysis; Statistical analysis; Urinalysis; Urinary tract; urinary tract infection; Urinary tract infections; Urine; Urogenital system; uropathogens morphology; Vagina; vaginal contamination; Japan; automated urine microscopy analysis; multivariable logistic regression analysis; uropathogens morphology; urinary tract infection; vaginal contamination
• ISSN: 0887-8013; 1098-2825
• DOI: 10.1002/jcla.23698

Background To help combat the worldwide spread of multidrug‐resistant Enterobacterales, which are responsible for many causes of urinary tract infection (UTI), we evaluated the ability of the Atellica UAS800 automated microscopy system, the only one offering the capability of bacterial morphological differentiation, to determine its effectiveness. Methods We examined 118 outpatient spot urine samples in which pyuria and bacteriuria were observed using flow cytometry (training set: 81; cross‐validation set: 37). The ability of the Atellica UAS800 to differentiate between bacilli and cocci was verified. To improve its ability, multiple logistic regression analysis was used to construct a prediction formula. Results This instrument's detection sensitivity was 106 CFU/ml, and reproducibility in that range was good, but data reliability for the number of cocci was low. Multiple logistic regression analysis with each explanatory variable (14 items from the Atellica UAS800, age and sex) showed the best prediction formula for discrimination of uropathogen morphology was a model with 5 explanatory variables: number of bacilli (p < 0.001), squamous epithelial cells (p = 0.004), age (p = 0.039), number of cocci (p = 0.107), and erythrocytes (p = 0.111). For a predicted cutoff value of 0.449, sensitivity was 0.879 and specificity was 0.854. In the cross‐validation set, sensitivity was 0.813 and specificity was 0.857. Conclusions The Atellica UAS800 could detect squamous epithelial cells, an indicator of vaginal contamination, with high sensitivity, which further improved performance. Simultaneous use of this probability prediction formula with urinalysis results may facilitate real‐time prediction of uropathogens and vaginal contamination, thus providing helpful information for empiric therapy. The workflow and its performance for discrimination of bacterial morphology and vaginal contamination using Atellica UAS800 are shown. The ability to discriminate bacterial morphology (positive predictive value) was 91% in the bacilli group and 81% in the cocci or polymicrobial group in the training set. The ability to discriminate vaginal contamination (positive predictive value) was 96% in the non‐contamination group and 84% in the contamination group within the training set. In the cross‐validation set, the positive predictive value was 82% in the bacilli group, 80% in the cocci or polymicrobial group, 92% in the vaginal non‐contamination group, and 80% in the contamination group.